Ecological Security Assessment, Prediction, and Zoning Management: An Integrated Analytical Framework

Bo Nan; Yujia Zhai; Mengmeng Wang; Hongjie Wang; Baoshan Cui

doi:10.1016/j.eng.2024.11.032

Engineering ›› 2025, Vol. 49 ›› Issue (6) :253 -265. DOI: 10.1016/j.eng.2024.11.032

Research

research-article

Ecological Security Assessment, Prediction, and Zoning Management: An Integrated Analytical Framework

Author information +

History +

PDF (4122KB)

Abstract

Enhancing ecological security for sustainable social, economic, and environmental development is a key focus of current research and a practical necessity for ecological management. However, the integration of retrospective ecological security assessments with future trend predictions and fine-scale targeted regulations remains inadequate, limiting effective ecological governance and sustainable regional development. Guided by Social–Economic–Natural Complex Ecosystems (SENCE) theory, this study proposes an analytical framework that integrates ecological security assessment, prediction, and zoning management. The Daqing River Basin, a typical river basin in the North China Plain, was selected as a case study. The results indicate that overall ecological security in the Daqing River Basin improved from a “Moderate” level to a “Relatively Safe” level between 2000 and 2020; however, spatial heterogeneity persisted, with higher ecological security in northwestern and eastern regions and lower ecological security in the central region. Approximately 62% of the Basin experienced an improvement in ecological security level, except in the major urban areas of Beijing, Tianjin, and Hebei, where ecological security deteriorated. From 2025 to 2040, the overall ecological security of the Daqing River Basin is expected to improve and remain at the “Relatively Safe” level. However, spatial heterogeneity will be further aggravated as the ecological security of major urban areas continues to deteriorate. Ecological security management zones and regulation strategies are proposed at the regional and county scales to emphasize integrated regulation for the entire basin and major urban areas. The proposed analytical framework provides valuable insights for advancing theoretical research on ecological security. The case study offers a practical reference for ecological security enhancement in river basins and other regions facing significant human–land conflicts.

Graphical abstract

Keywords

Ecological security / Analytical framework / Assessment / Prediction / Zoning management

Cite this article

Download citation ▾

Bo Nan, Yujia Zhai, Mengmeng Wang, Hongjie Wang, Baoshan Cui. Ecological Security Assessment, Prediction, and Zoning Management: An Integrated Analytical Framework. Engineering, 2025, 49(6): 253-265 DOI:10.1016/j.eng.2024.11.032

登录浏览全文

4963

注册一个新账户忘记密码

1. Introduction

Rapid global changes driven by human activities have resulted in numerous ecological challenges, forcing society to confront the escalating conflict between ecological conservation and development, threatening both human well-being and the ecological balance [1], [2]. To mitigate these conflicts and achieve sustainable human development, a comprehensive examination of ecological problems is required [3], [4], [5], [6].

Ecological security refers to the ability of ecosystems to adapt to environmental changes while maintaining sustainability and stability [7], [8]. In this state, ecosystems can provide essential services that effectively support socioeconomic systems and human well-being. Ecological security bridges the gap between ecosystem health and its capacity to meet human needs, guiding the tradeoffs between these aspects to achieve an optimal balance [9]. In the context of the intense interaction between human beings and nature, the intricate interdependence among social, economic, and natural elements poses challenges to ensuring ecological security and sustainable development in contemporary societies [10]. Social–Economic–Natural Complex Ecosystem (SENCE) theory offers valuable insights into regional sustainable development and ecological security [11], emphasizing the interdependence of humans and nature through the integration of social, economic, and natural elements [10]. It has gained widespread recognition among scholars and government administrators [12], [13], [14]. Hence, integrating SENCE theory into ecological security research to promote the coordinated development of ecological construction and the social economy is crucial.

Ecological security assessment, a key prerequisite for discussing ecological security, is considered the core of ecological security research and has been given special research priority [15], [16], [17]. Over the past few decades, numerous ecological security assessments have been performed from various perspectives in diverse regions, across multiple dimensions, and using data from different sources with varying levels of precision [16], [17]. Assessment models such as the fuzzy matter-element method [18], ecological footprint method [19], emergy ecological footprint method [20], technique for order preference by similarity to ideal solution (TOPSIS) method [21], and conceptual frameworks [22] have been commonly adopted for ecological security assessment. Currently, the field of ecological security is gradually moving forward from the focus of retrospective assessments to forecasting and early warning of future ecological threats. Ecological security predictions can help understand possible changes in the ecological environment and provide advance notice of potential environmental threats [23], [24]. Gray models, neural networks, system dynamics models, cellular automata, and logistic regression have been used in ecological prediction studies [25], [26].

Despite the significant progress in ecological security assessments and predictions, several issues require further exploration. First, current social development requires not only a comprehensive understanding of ecological security trends but also the identification of issues in different ecological regions and the formulation of targeted regulations to mitigate negative impacts. However, the recommended measures resulting from ecological security assessments are often imprecise, hampering effective policy implementation. Second, ecological security research based on administrative-scale statistics fails to effectively capture detailed ecological conditions across different spatial locations, nor does it adequately explore the spatiotemporal heterogeneity of ecological security at finer scales. Therefore, it is essential to integrate retrospective ecological security analyses with future ecological security trend predictions in fine-scale ecological security regulations. Furthermore, ecological security zoning and differentiated management involve implementing tailored strategies based on the diversity of regional ecological environments and differing ecological pressures aimed at maximizing the protection and maintenance of ecosystem functions and stability [27]. This approach facilitates targeted management and regulatory measures aligned with local ecology, thereby avoiding uniform “one-size-fits-all” management practices [28]. Building on the above analysis, this study incorporated SENCE theory to develop an analytical framework that integrates ecological security assessment, ecological security prediction, and ecological security zoning management. This framework aims to provide effective tools to enable precise ecological governance and foster improved ecological security and sustainable development.

River basins are complex ecosystems comprising natural resources, economic activities, and social factors, providing essential services such as water supply, biodiversity conservation, and climate regulation [28], [29]. They are highly susceptible to human–land conflicts and external disturbances, which can result in ecological imbalances [30]. Ensuring the ecological security of river basins is essential to protect ecosystem functions and promote socio-economic development [28]. Therefore, further efforts are needed to conduct comprehensive assessments of ecological security in river basins and formulate guidelines for decision-makers [31]. Located in the transitional zone between the Taihang Mountains and the North China Plain, the Daqing River Basin is crucial to the ecological security pattern of the North China Plain. It serves as the primary grain-producing area in China, supporting a substantial population in the Beijing–Tianjin–Hebei metropolitan region. The ecological security status of the Daqing River Basin is a significant constraint on local socioeconomic development. With the rapid coordinated development of the Beijing–Tianjin–Hebei region and ongoing construction of the Xiong'an New Area, human–land conflicts in the Daqing River Basin are expected to intensify. The examination of ecological security in the Daqing River Basin has significant implications for similar regions. However, studies on the ecological security of this region are limited.

Given the practical need to enhance ecological security in contemporary societies, guided by SENCE theory, this study proposes an analytical framework for ecological security, applying the Daqing River Basin as a case study. The objectives of this study were: ① to establish an analytical framework that effectively integrates ecological security assessment, ecological security prediction, and ecological security zoning management; and ② utilize this framework to analyze the spatiotemporal evolution of ecological security, predict future trends in ecological security, and propose ecological security management zones along with associated countermeasures. The contributions of this study are as follows: ① an analytical framework constituting an advancement in the theoretical development of ecological security research; and ② findings related to ecological security in the Daqing River Basin that are expected to provide valuable insights for enhancing ecological security in other basins and similar regions.

2. Materials and methods

2.1. Analytical framework for ecological security

This study established an analytical framework for ecological security (Fig. 1). This analytical framework effectively integrates four main components. The first component functions as a theoretical guide for ecological security research, grounded in SENCE theory. SENCE theory emphasizes the necessity of integrating social, economic, and natural systems to achieve harmonious ecological development and sustainable regional progress. This theoretical foundation enables a holistic understanding of ecological security by recognizing the interdependencies between these subsystems and serves as a basis for the coordinated development of ecological security strategies [11]. The second part of the study focused on an ecological security assessment using the pressure–state–response (PSR) model [32], which evaluates ecological security across the dimensions of pressure, state, and response. This assessment uncovered spatiotemporal evolution characteristics, identified transformation patterns at multiple levels, and emphasized key areas of ecological security evolution. The third component utilizes predictive modeling with historical spatiotemporal data to forecast future distribution characteristics, transformation patterns, and key areas of ecological security. Finally, based on the results of the ecological security assessment and predictions, multi-scale ecological security management zones were established at both regional and county scales. Corresponding zonal regulation strategies have been proposed to enhance ecological security.

2.1.1. Ecological security assessment

Assessment index system: Based on SENCE theory, an ecological security assessment index system was established using the PSR model. The PSR model meets the requirements for considering environmental protection policies and provides scientific guidance for regional ecological management when applied to ecological security evaluations [32]. The model comprises three indicator layers. The pressure layer denotes the impact of human activities on the ecological environment, the state layer reflects the capacity of an ecosystem to sustain itself, and the response layer details the human interventions aimed at ensuring ecosystem sustainability. The indicators chosen for the assessment should comprehensively reflect the natural, social, and economic conditions of the study area, as well as their distinct geographical characteristics. Additionally, the accessibility and practical applicability of the data must also be considered. Based on these principles, ten evaluation indices were selected for the ecological security assessment. Population density, night light index, and human interference index (HI) were selected to represent the pressure level. Net primary productivity (NPP), patch density (PD), and Shannon diversity indices (SHDI) were used to represent the state level. The proportions of tertiary industries, nature reserve areas, and ecosystem service values (ESV) were chosen to represent the response level (Table 1 [24], [28], [33], [34], [35], [36], [37], [38], [39]). Among them, ESV was obtained according to the equivalent factor method [33]. The HI was calculated following Yang et al. [34]. All indicators were categorized into positive and negative categories. Positive indicators indicate that higher index values correspond to greater ecological security, whereas negative indicators indicate the opposite. Specific descriptions of the 10 indices are provided in Table 1.

Index standardization and comprehensive weight assignment: The range normalization method was applied to standardize the original data, resulting in normalized values between 0 and 1. The calculation formulas were as follows:

(1)

P o s i t i v e i n d i c a t o r s : Y_{ij} = \frac{X_{ij} - X_{j m i n}}{X_{j m a x} - X_{j m i n}}

(2)

N e g a t i v e i n d i c a t o r s : Y_{ij} = \frac{X_{j m a x} - X_{ij}}{X_{j m a x} - X_{j m i n}}

where

Y_{ij}

represents the standardized value of indicator

j

of grid cell

i

;

X_{ij}

represents the original value of indicator

j

of grid cell

i

; and

X_{j m a x}

and

X_{j m i n}

represent the maximum and minimum values of indicator

j

, respectively.

The weights of the indicators are determined using a combination of subjective and objective methods, balancing expert judgment with data variability [40]. The subjective weights were derived using the analytic hierarchy process (AHP), whereas the objective weights were calculated using the entropy method. AHP was combined with the entropy weight method using the principle of minimizing relative information entropy to derive comprehensive weights [41]. This approach balances subjective insights with objective data patterns, thereby enhancing the robustness and credibility of the final weight allocation.

(3)$\begin{array}{l}x^{(1)}=\left\{x^{(1)}(1), x^{(1)}(2), \cdots, x^{(1)}(n)\right\}\\x^{(1)}(t)=\sum_{k=1}^{t} x^{(0)}(k), k=1,2, \cdots, n\end{array}$

where

W_{j}

is the comprehensive weight of indicator

j

W_{1 j}

is the subjective weight of indicator

j

, and

W_{2 j}

is the objective weight of indicator

j

n \in N^{+}

Section S2 in Appendix A presents the comprehensive weights for each indicator.

Calculation of ecological security index: The comprehensive evaluation method is widely used and well-established for assessing ecological security. Based on the index system and weights established in the previous section, Eq. (4) was used to calculate the ecological security index (ESI) for each grid cell.

(4)

E S I = \sum_{j = 1}^{n} W_{j} \times Z_{j}

where

E S I

is the final result of the ecological security in each grid cell,

Z_{j}

is the standard value of indicator

j

W_{j}

is the weight of indicator

j

, and

n

is the number of indicators. A higher

E S I

value indicates more favorable ecological security conditions.

Subsequently, the natural break classification method (Jenks) was applied to classify the ESI. Spatial data processing and analysis were performed using ArcGIS V10.2, and statistical analyses of the socioeconomic data were conducted using Microsoft Excel 2016.

2.1.2. Ecological security prediction

Ecological security prediction involves forecasting ecological trends over time and identifying potential issues using known spatiotemporal data [23]. Prediction is often based on past and present values of variables [42]. The GM(1,1) model, a key component of gray system theory, addresses uncertainty in small samples and incomplete data [43]. The model filters irregular changes in sample data, reflects trend characteristics, and predicts the primary direction of change over time [44]. We created the original sequence of time data

x^{(0)}

as follows:

(5)

x^{(0)} = \{x^{(0)} (1), x^{(0)} (2), {\cdot \cdot \cdot, x}^{(0)} (n)\}

The cumulative sequence

x^{(1)}

was derived by accumulating the original data

x^{(0)}

(6)

x^{(1)} = \{x^{(1)} (1), x^{(1)} (2), {\cdot \cdot \cdot, x}^{(1)} (n)\}

x^{(1)} (t) = \sum_{k = 1}^{t} x^{(0)} (k), k = 1, 2, \cdot \cdot \cdot, n

After accumulation,

x^{(1)}

satisfies the first-order form of the differential equation,

t

represents the discrete time index or time step used to denote specific points in the data sequence;

(7)

\frac{{d x}^{(1)} (t)}{d t} + {ax}^{(1)} = u

Let the matrix A be

(8)

A = [\begin{matrix} a \\ u \end{matrix}]

where a is the development coefficient of the model and u is its coordination coefficient.

We then constructed data matrix B and the vector of constant terms Y by solving according to the least-squares method:

(9)

a = {(B^{T} B)}^{- 1} B^{T} Y

(10)

B = [\begin{matrix} - \frac{1}{2} (x^{(1)} (1) + x^{(1)} (2)) & 1 \\ - \frac{1}{2} (x^{(1)} (2) + x^{(1)} (3)) & 1 \\ \begin{matrix} ⁝ \\ - \frac{1}{2} (x^{(1)} (n - 1) + x^{(1)} (n)) \end{matrix} & \begin{matrix} ⁝ \\ 1 \end{matrix} \end{matrix}]

(11)

Y = [\begin{matrix} x^{0} (2) \\ x^{0} (3) \\ \begin{matrix} ⁝ \\ x^{0} (n) \end{matrix} \end{matrix}]

Incorporating a and u into the differential equation to derive the time–response function for the accumulated sequence of predicted values reflects how changes in the ESI value over time indicate the ecological security condition:

(12)

{\hat{x}}^{(1)} (t + 1) = (x^{0} (1) - \frac{u}{a}) e^{- a t} + \frac{u}{a}

where

{\hat{x}}^{(0)} (t + 1)

represents the accumulated value of the predicted value at of

t + 1

Conduct cumulative reduction calculations to generate the corresponding prediction sequence:

(13)

{\hat{x}}^{(0)} (t + 1) = {\hat{x}}^{(1)} (t + 1) - {\hat{x}}^{(1)} (t)

The GM(1,1) prediction model was used to forecast the ecological security of the Daqing River Basin for 2025, 2030, 2035, and 2040, utilizing the evaluation results of each grid from 2000 to 2020 as foundational data. Relative errors were used to test the model accuracy, and the relative accuracy was used to assess the precision of the model [29].

2.1.3. Ecological security zoning management

Zoning and regulatory strategies that enhance ecological security are essential for resolving the conflict between human development and nature, and promoting human–nature integration [27]. Drawing on a comprehensive understanding of the spatiotemporal evolution of ecological security, future development trends, regional ecological policies, socioeconomic conditions, and natural geographic features, this study employed spatial clustering of ESI to delineate distinct ecological security zones [45]. The results of ecological security zoning are presented at two levels: regional and county. Ecological regulation strategies have been developed based on these zoning units.

2.2. Study area

The Daqing River Basin (37°37′N–40°34′N, 112°57′E–118°40′E) is located in the Taihang Mountains and North China Plain (Fig. 2). It covers 81 counties and has a total area of approximately 81 000 km² (excluding land areas newly created through reclamation after 2000). The Basin has a warm temperate semi-arid continental monsoon climate and consists of mountainous and hilly areas in the northwest and plains in the southeast. The water area is primarily concentrated in the eastern part of the Basin and Baiyangdian is situated in its hinterland. This area comprises six districts in Beijing, ten districts in Tianjin, 60 counties in Hebei Province, and five counties in Shanxi Province, with a total population of 56.28 million people in 2020. The region exhibits uneven socioeconomic development, with Beijing and Tianjin boasting high gross domestic products (GDPs). The main land-use types are cultivated land, grasslands, and forests. From 2000 to 2020, there was a reduction in cultivated land and wetlands, coupled with the expansion of construction land and water bodies. The region is facing increasing pollution, declining resources, and soil erosion, making it ecologically sensitive and fragile, and threatening sustainable development and human well-being.

2.3. Data sources and preprocessing

The data used in this study (2000, 2005, 2010, 2015, and 2020) are listed in Table 2. Given the significant role of wetlands in ecosystem services in the Daqing River Basin, land-use types were reclassified as cultivated land, forest, grassland, water area, construction land, wetland, and unused land. All spatial data were converted to the WGS84_Albers projection coordinate system and gridded to an accuracy of 3 km × 3 km following data preprocessing and calculations. Socioeconomic data subjected to price changes were adjusted to constant prices, with 2000 as the base period.

3. Results

3.1. Spatiotemporal evolution of ecological security

The ESI of the Daqing River Basin was classified into five levels: Safe (SA; Level I), Relatively Safe (RS; Level II), Moderate (MD; Level III), Relatively Unsafe (RU; Level IV), and Unsafe (US; Level V) (Table 3). The mean values of ESI for the whole Daqing River Basin in 2000, 2005, 2010, 2015, and 2020 were 0.5228, 0.5283, 0.5290, 0.5430, and 0.5583, respectively, the ecological security was at a MD level from 2000 to 2015, and an RS level in 2020, indicating an improvement in the ecological security of the Daqing River Basin.

In terms of the area’s proportion of different ecological security levels from 2000 to 2020, the SA level experienced the largest increase, rising by 24%, while the RU level underwent the largest decrease, dropping by 23%. Before 2010, the RU and RS levels were the predominant categories, accounting for a combined proportion exceeding 62%. Since 2015, the MD level has predominated, with a notable increase in SA level areas and significant declines in RU and RS level areas. By 2020, the MD level encompassed the largest area (32.41%), followed by the SA level (27.90%). Over the 20 years, the area of US level remained the smallest (Table 3).

Regarding the direction of transformation of ecological security levels over the 20 years, approximately 62% of the total area transitioned from a lower ecological security level to a higher ecological security level (Fig. 3). A total of 19 017 km² of the RU level were transformed into the MD level, 18 351 km² of the RS level were transformed into the SA level, and 11 007 km² of the MD level were transformed into the RS level. However, a small area saw a transition from higher to lower levels, primarily from the MD to RU level (990 km²) and from RS to US (846 km²).

Based on the spatial evolution characteristics of different ecological security levels, the ecological security distribution in the entire Daqing River Basin exhibited considerable spatial heterogeneity over the past 20 years. Ecological security was consistently stronger in northwestern and eastern regions, whereas the central region consistently demonstrated weaker ecological security (Fig. 4). Within northwestern, eastern, and central regions, the distributions of ecological security levels changed, with more substantial and pronounced changes occurring between 2010 and 2020 than between 2000 and 2010. Specifically, the northwestern region transitioned from being predominantly at RS and MD levels to mostly SA and RS levels. The south–central region transitioned from a predominantly RU level to a widespread MD level, and, the area of RU level shrank dramatically, with only a small area occupying the southern corner of the Basin and the rest scattered across the southcentral region by 2020. The eastern region and Baiyangdian area remained predominantly at the SA level. The US level was primarily concentrated in the major urban areas of Beijing, Tianjin, and Hebei, with most areas expanding over the past 20 years, particularly in Tianjin. From a county-level perspective, between 2000 and 2020, counties with the SA level exhibited the greatest increase, rising from 1 in 2000 to 7 in 2020, followed by counties with an MD level, which experienced a growth rate of 83.33%; counties with an RU level experienced the greatest decrease, with a rate of 53.49% (Section S3 in Appendix A).

3.2. Ecological security prediction

Based on relative accuracy metrics, the relative errors predominantly fell within the range of 0–5%, with an average error of 1.00% (Section S4 in Appendix A), demonstrating that the GM(1,1) model is a reliable tool for predicting the ESI in the Daqing River Basin. The predicted ESI values for the Daqing River Basin in 2025, 2030, 2035, and 2040 were 0.5668, 0.5786, 0.5909, and 0.6038, respectively (Table 4), suggesting that ecological security for the whole Daqing River Basin will improve and remain at the RS level in 2025 to 2040. However, the minimum ESI value will decrease from 0.2276 in 2025 to 0.1688 in 2040, indicating the potential for ongoing deterioration of the ecological environment in certain parts of the Basin. The areas of SA and RS levels are expected to expand from 2025 to 2040 (Table 4), with the SA level growing at a faster rate; its proportion is projected to increase from 32.61% in 2025 to 46.08% in 2040. The proportion of MD and RU levels will decrease continuously over the period 2025–2040. Meanwhile, the proportion of area of the Unsafe level will increase by 1.00%.

From the spatial distribution characteristics of different ecological security levels, by 2040, nearly all of the northwestern region is projected to achieve the SA level (Fig. 5). The southeastern region is projected to see a significant decline in MD and RU levels, which will be largely replaced by RS and SA levels. However, ecological security in the southern corner of the Basin is anticipated to remain relatively weak. The US level will become increasingly concentrated and expand around the major urban areas of Beijing, Tianjin, and Hebei, indicating a continued decline in ecological security in these areas. At the county level, most counties will experience an improvement in their ecological security by 2040. However, six counties, including Hebei District, Nankai District, Hongqiao District, Hexi District, Xinhua District, and Jingxiu District, will continue to be classified as at a US level. Daxing District, Haidian District, and 13 other counties showed declining ecological security trends (Section S5 in Appendix A).

3.3. Ecological security zoning management

Ecological security zone management serves as an essential basis for regional ecological regulation [27]. In this study, by analyzing the spatiotemporal evolution of ecological security from 2000 to 2020, along with projected trends from 2025 to 2040 and the local regional development plan, the ecological security management zones of the Daqing River Basin were categorized at both regional and county scales. For regional-scale management zones, the spatial heterogeneity of the ecological security, natural geographical features, and socioeconomic differences throughout the Basin are the main factors to be considered. The regional-scale management zones were divided into three zones: east, central, and west (Fig. 6). Specific information for each regional-scale management zone is presented in Table 5. Next, to better align with administrative management, county-scale ecological security management zones were proposed, dividing the 81 counties into five distinct ecological management zones: mountain and wetland zone, plain zone I, plain zone II, urban zone I, and urban zone II (Fig. 6). Specific information for each county management zone is provided in Table 6 (counties included in each management zone are detailed in Section S6 in Appendix A).

4. Discussion

4.1. Analytical framework of ecological security

Ecological security requires not only an understanding of the underlying principles of its evolution but also proactive measures to ensure future sustainable development [46], [47]. The essence of maintaining ecological security lies in the rational coordination of the relationships between humans and the environment [6]. Therefore, the primary objective of ecological security research should be to provide effective and practical guidance to enhance future ecological security. In light of this, our study followed the research trajectory of “assessment–prediction–zoning differentiated management” to develop an analytical framework and conduct a comprehensive ecological security analysis.

First, in terms of ecological security assessment, previous ecological security assessments primarily focused on either a single perspective (e.g., ecosystem services assessment, ecological risk assessment, or ecological carrying capacity assessment) or on conceptual frameworks (e.g., PSR model and driving–pressure–state–impact–response (DPSIR) model). These approaches are advantageous for elucidating the evolutionary trajectory and patterns of specific ecosystem aspects across spatial and temporal scales, and identifying priority areas where ecosystem quality is improving or deteriorating. However, each has a specific focus [48], [49], [50]. To address the needs of ecosystem management, this study developed an ecological security assessment index system that systematically integrates natural, economic, and societal elements within the conceptual logic of the PSR framework, providing a more holistic reflection of ecosystem change outcomes. We selected the PSR model for the ecological security assessment indicator system because of its systematic structure, which offers better classification accuracy and avoids duplicate calculations compared with the DPSIR and driving–pressure–state–impact–response–management (DPSIRM) frameworks. Introducing SENCE theory to guide the selection of evaluation indicators reduces the inherent uncertainties and enhances the flexibility of the model. Additionally, the selection of assessment indicators ensures both a comprehensive representation of the study area’s natural, social, and economic conditions and the accessibility and practical applicability of the data for achieving accurate results.

Second, current efforts toward ecological security enhancement measures often remain vague, lacking specific spatially integrated practical guidance for optimal ecological security management [27], [51], which diminishes the directive impact of ecological security initiatives. Therefore, our study introduces ecological security zoning and differentiated management to ecological security research. We delineated ecological security management zones at both regional and county scales. This division first considers the characteristics of ecological security influenced by natural geographical features and socioeconomic differentiation at the macro level to holistically identify the direction of optimization and regulation for each zone. It focuses on micro-level ecological security characteristics aligned with county administrative units to provide tailored recommendations for local ecological construction and sustainable development.

The ecological security research framework proposed in this study offers a standard framework for ecological security research and management. Moreover, an ecological security assessment index system could serve as a reference for ecological security assessment efforts.

4.2. Pressures, states, and responses of ecological security in the Daqing River Basin

Ecological security is influenced by multiple factors, and clarifying their relationships is crucial for effective regulation [52]. This study examines the factors influencing ecological security through a causal framework of pressure, state, and response. From the perspective of pressure, high population density and continuous urban expansion have caused a prominent increase in ecological pressure levels in the Daqing River Basin. However, at the same time, factors such as the increased NPP and SHDI, implementation of local ecological projects, growth of tertiary industries, and expansion of nature reserves have led to significant improvements in state and response levels. This study concluded that the ecological security of the Daqing River Basin has continued to improve over the past 20 years, illustrating that enhancing state and response levels can effectively mitigate pressure-related threats and promote overall ecological security.

In addition, the spatial heterogeneity of pressure, state, and response levels directly affects the spatial distribution of ecological security. Specifically, the northwestern region, with its strong ecological baseline of abundant vegetation and biodiversity, has experienced limited human activity and urban expansion because of its high altitude. Recent ecological construction initiatives, such as nature reserve expansion, have reduced stress levels, improved the ecological state and response levels, and significantly enhanced ecological security. The southeastern region includes the primary agricultural areas of the North China Plain and major urban areas of the Daqing River Basin. Although increasing population density and urban expansion in the southeastern region have significantly increased stress levels, the implementation of response measures, such as high-standard agricultural land construction, has significantly enhanced the response capacity, which in turn improves the ecological state and overall security in the region. Overall, the findings suggest that better responses and state levels combined with lower pressure levels are more likely to result in higher ecological security levels.

The major urban areas of Beijing, Tianjin, and Hebei have shown concentrated ecological deterioration, while the vast majority of the Basin has exhibited consistent improvements in ecological security levels over the past two decades, and the conflict between urban expansion, human activities, and ecological security in the Daqing River Basin has been largely reconciled at the basin-wide scale. The ecological security evolution trends of the Daqing River Basin reflect similar patterns observed in the Beijing–Tianjin–Hebei region [40], [54], and the fact that the urban areas of Beijing, Tianjin, and Hebei face severe ecological stress levels is becoming increasingly evident [53], [54], [55]. This highlights the need for targeted strategies to address ecological challenges in urban areas while promoting overall improvements. Furthermore, this study clarified that inequality in the spatial distribution of ecological security in the Daqing River Basin will be more prominent, and its polarization will be further aggravated. Most areas are expected to experience continued improvements in ecological security; however, the main urban areas and their surroundings are likely to experience further deterioration. This is a warning for regional ecological protection in the Daqing River Basin, and it is imperative to simultaneously emphasize integrated regulation for ecological security, both at the basin-scale and in major urban areas.

4.3. Regulation strategies in ecological security zones

Differentiated regulation strategies are a concrete approach to coordinate society, economy, and ecology to achieve the dual goals of ecological security and social development. It is essential to balance urban construction with the ecological security of the Daqing River Basin, implement preservation mechanisms, and emphasize collaborative governance efforts in Beijing, Tianjin, Hebei, and Shanxi. Furthermore, a combination of regional and county scales from top-down and bottom-up perspectives is necessary to enhance the practicality and efficiency of differentiated management strategies [56].

Regarding regional-scale zones, the west zone in the upper reaches of the Basin exhibited low human activity and maintained high ecological security. This zone should prioritize soil and water conservation, while promoting forest and grassland cultivation. The central zone, characterized by cultivated land and significant urban development, exhibited lower ecological security. This area must strictly adhere to cultivated land protection policies, enhance the efficiency of water and soil resource use, and coordinate urbanization with arable land protection. The east zone, which is dominated by coastal wetlands, requires protection and restoration.

For county-scale zones, mountain and wetland zone should prioritize soil and water conservation, protection of nature reserves, and restoration of wetlands. Plain zone I should focus on protecting cultivated land; maintaining its quantity, quality, and ecological balance; and improving water resource efficiency. Plain zone II should manage urbanization and cultivated land protection to prevent fragmentation of cultivated land exacerbated by the urbanization process. Urban zone I should explore sustainable paths for urban development amid population growth, ensuring scientifically planned urban land use, and enhance the urban ecological environment. Urban Zone II needs to accelerate green infrastructure development [57].

4.4. Limitations and future research directions

This study assessed ecological security based on the causal relationship between pressure, state, and response; however, the complex synergies and tradeoffs between these elements have not been sufficiently explored. Clarifying the mechanisms of this interaction should be the key focus of future research. Indices with resolutions used in future ecological security assessments can be flexibly adapted to the natural geography, socioeconomic characteristics, and data availability of the study area. Additionally, utilizing multiple ecological security prediction models to monitor trends in ecological security development is essential for improving prediction accuracy in future studies.

5. Conclusions

This paper proposes an analytical framework for ecological security that integrates ecological security assessment, prediction, and ecological security zoning management. We used Daqing River Basin, a typical river basin characterized by significant human-land conflict, as a case study. The primary conclusions are as follows.

The overall ecological security of the Daqing River Basin improved from the MD level to the RS level from 2000 to 2020. The spatial heterogeneity of ecological security consistently persisted during this period, being stronger in northwestern and eastern regions, but weaker in the central region. Except for the deterioration of ecological security in major urban areas, the vast majority of the Basin has improved throughout this timeframe. Between 2025 and 2040, ecological security in the vast majority of the Daqing River Basin will continue to improve and remain at a RS level, whereas in major urban areas, it is projected to continue deteriorating, further exacerbating the spatial heterogeneity of ecological security. The enhancement of the state and response levels effectively mitigates pressure levels and promotes overall ecological security in the Daqing River Basin; however, the deterioration of ecological security in the major urban areas of Beijing, Tianjin, and Hebei requires special attention. Accordingly, multi-scale ecological security management zones and regulation strategies have been proposed to simultaneously emphasize the integrated regulation of ecological security, both in the entire Basin and major urban areas. The regional-scale management zones were divided into west, central, and east zones, and the county-scale management zones were divided into mountain and wetland zone, plain zone I, plain zone II, urban zone I, and urban zone II.

The analytical ecological security framework described herein contributes to ecological security research and management by incorporating the interdependencies of social, economic, and natural elements, thereby facilitating the effective integration of ecological security assessment, prediction, and zoning management. The case study results and multi-scale ecological security management strategies provide valuable references for ecological governance in water basins and other similar regions experiencing significant human–land conflicts.

CRediT authorship contribution statement

Bo Nan: Writing – review & editing, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Yujia Zhai: Writing – review & editing, Investigation, Formal analysis. Mengmeng Wang: Writing – original draft, Data curation. Hongjie Wang: Supervision, Project administration, Conceptualization. Baoshan Cui: Supervision, Project administration, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This work was supported by the project of the National Natural Science Foundation of China (42330705).

Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.eng.2024.11.032.

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Guo J, Yue D, Li K, Hui C.Biocapacity optimization in regional planning.Sci Rep 2017; 7(1):41150.

[2]	Zhang B, Zou H, Duan D, Zhou X, Chen J, Sun Z, et al.Stability in change: building a stable ecological security pattern in northeast China under climate and land use changes.Sci Rep 2024; 14(1):12642.

[3]	Reyers B, Selig ER.Global targets that reveal the social–ecological interdependencies of sustainable development.Nat Ecol Evol 2020; 4(8):1011-1019.

[4]	Zhang L, Wang G, Peng W, Zhang J, Qin S.Integrating ecological security assessment and problem area identification: simulated annealing and multi-model fusion.Ecol Indic 2024; 158:111440.

[5]	Yoon J, Klassert C, Selby P, Lachaut T, Knox S, Avisse N, et al.A coupled human-natural system analysis of freshwater security under climate and population change.Proc Natl Acad Sci 2021; 118(14):e2020431118.

[6]	Dunlop T, Khojasteh D, Cohen-Shacham E, Glamore W, Haghani M, Van Den Bosch M, et al.The evolution and future of research on nature-based solutions to address societal challenges.Commun Earth Environ. 2024; 5(1):132.

[7]	Bai Y, Wong CP, Jiang B, Hughes AC, Wang M, Wang Q.Developing China’s ecological redline policy using ecosystem services assessments for land use planning.Nat Commun 2018; 9(1):3034.

[8]	Liu C, Li W, Xu J, Zhou H, Li C, Wang W.Global trends and characteristics of ecological security research in the early 21st century: a literature review and bibliometric analysis.Ecol Indic 2022; 137:108734.

[9]	Wen J, Hou K.Research on the progress of regional ecological security evaluation and optimization of its common limitations.Ecol Indic 2021; 127:107797.

[10]	Wang R, Li F, Hu D, Larry LB.Understanding eco-complexity: Social–Economic–Natural Complex Ecosystem approach.Ecol Complex 2011; 8(1):15-29.

[11]	Li C, Zhang J, Philbin SP, Yang X, Dong Z, Hong J, et al.Evaluating the impact of highway construction projects on landscape ecological risks in high altitude plateaus.Sci Rep 2022; 12(1):5170.

[12]	Yao L, Liu J, Wang R, Yin K, Han B.A qualitative network model for understanding regional metabolism in the context of Social–Economic–Natural Complex Ecosystem theory.Ecol Inform 2015; 26:29-34.

[13]	Zhu S, Li D, Feng H, Zhang N.The influencing factors and mechanisms for urban flood resilience in China: from the perspective of Social–Economic–Natural Complex Ecosystem.Ecol Indic 2023; 147:109959.

[14]	Wang C, Wang Y, Geng Y, Wang R, Zhang J.Measuring regional sustainability with an integrated Social–Economic–Natural Approach: a case study of the Yellow River Delta region of China.J Clean Prod 2016; 114:189-198.

[15]	Ghosh S, Das Chatterjee N, Dinda S.Urban ecological security assessment and forecasting using integrated DEMATEL-ANP and CA-Markov models: a case study on Kolkata Metropolitan area, India.Sustain Cities Soc 2021; 68:102773.

[16]	Pan N, Du Q, Guan Q, Tan Z, Sun Y, Wang Q.Ecological security assessment and pattern construction in arid and semi-arid areas: a case study of the Hexi Region, NW China.Ecol Indic 2022; 138:108797.

[17]	Zou Y, Wang S, Luo X, Jin H, Xiao X, Li Y.A new multivariate index for ecological security assessment in the China–Myanmar border region.J Environ Manage 2024; 364:121447.

[18]	Yu K, Gao H.Sustainable development, eco-tourism carrying capacity and fuzzy algorithm—a study on Kanas in Belt and Road.Sci Rep 2023; 13(1):16789.

[19]	Zhang J, Hao X, Li X, Fan X, Zhang S.Evaluation and regulation strategy for ecological security in the Tarim River Basin based on the ecological footprint.J Clean Prod 2024; 435:140488.

[20]	Tang C, Wu X, Zheng Q, Lyu N.Ecological security evaluations of the tourism industry in ecological conservation development areas: a case study of Beijing’s ECDA.J Clean Prod 2018; 197:999-1010.

[21]	Li Y, Lian Z, Zhai T, Xie X, Wang Y, Li M.Assessment of land ecological security based on the Boston model: a case study from China.Land 2023; 12(7):1348.

[22]	Grilo F, Mcphearson T, Santos-Reis M, Branquinho C.A trait-based conceptual framework to examine urban biodiversity, socio-ecological filters, and ecosystem services linkages.npj Urban Sustain 2022; 2(1):32.

[23]	Sun K, He W, Shen Y, Yan T, Liu C, Yang Z, et al.Ecological security evaluation and early warning in the water source area of the Middle Route of South-to-North Water Diversion Project.Sci Total Environ 2023; 868:161561.

[24]	Liu Y, Wang C, Wang H, Chang Y, Yang X, Zang F, et al.An integrated ecological security early-warning framework in the national nature reserve based on the gray model.J Nat Conserv 2023; 73:126394.

[25]	Xie B, Jones P, Dwivedi R, Bao L, Liang R.Evaluation, comparison, and unique features of ecological security in southwest China: a case study of Yunnan Province.Ecol Indic 2023; 153:110453.

[26]	Zhang H, Yan Q, Xie F, Ma S.Evaluation and prediction of landscape ecological security based on a CA-Markov model in overlapped area of crop and coal production.Land 2023; 12(1):207.

[27]	Shen W, Li Y, Qin Y.Research on the influencing factors and multi-scale regulatory pathway of ecosystem health: a case study in the Middle Reaches of the Yellow River, China.J Clean Prod 2023; 406:137038.

[28]	Hu Y, Gong J, Li X, Song L, Zhang Z, Zhang S, et al.Ecological security assessment and ecological management zoning based on ecosystem services in the West Liao River Basin.Ecol Eng 2023; 192:106973.

[29]	Liu H, Wang Z, Zhang L, Tang F, Wang G, Li M.Construction of an ecological security network in the Fenhe River Basin and its temporal and spatial evolution characteristics.J Clean Prod 2023; 417:137961.

[30]	Hou Q, Li Q, Yang Y, Zhou J, Du Y, Zhang Y.Evaluation and optimization of ecological spatial resilience of Yanhe River Basin based on complex network theory.Sci Rep 2024; 14(1):1361.

[31]	Han Z, Wei Y, Meng J, Zou Y, Wu Q.Integrated water security and coupling of social–ecological system to improve river basin sustainability.Sci Total Environ 2023; 905:167182.

[32]	Zhang R, Wang C, Xiong Y.Ecological security assessment of China based on the pressure–state–response framework.Ecol Indic 2023; 154:110647.

[33]	Pan Y, Dong F, Du C.Is China approaching the inflection point of the ecological Kuznets curve? Analysis based on ecosystem service value at the county level.J Environ Manage 2023; 326:116629.

[34]	Yang Y, Song G, Lu S.Assessment of land ecosystem health with Monte Carlo simulation: a case study in Qiqihaer, China.J Clean Prod 2020; 250:119522.

[35]	Fan Y, Fang C.Evolution process and obstacle factors of ecological security in western China, a case study of Qinghai Province.Ecol Indic 2020; 117:106659.

[36]	Liu X, Su Y, Li Z, Zhang S.Constructing ecological security patterns based on ecosystem services trade-offs and ecological sensitivity: a case study of Shenzhen metropolitan area, China.Ecol Indic 2023; 154:110626.

[37]	Qian W, Zhao Y, Li X.Construction of ecological security pattern in coastal urban areas: a case study in Qingdao, China.Ecol Indic 2023; 154:110754.

[38]	Huang Q, Peng B, Wei G, Wan A.Dynamic assessment and early warning of ecological security: a case study of the Yangtze river urban agglomeration.Nat Hazards 2021; 107(3):2441-2461.

[39]	Li ZT, Li M, Xia BC.Spatio–temporal dynamics of ecological security pattern of the Pearl River Delta urban agglomeration based on LUCC simulation.Ecol Indic 2020; 114:106319.

[40]	Peng C, Li B, Nan B.An analysis framework for the ecological security of urban agglomeration: a case study of the Beijing–Tianjin–Hebei urban agglomeration.J Clean Prod 2021; 315:128111.

[41]	Hou M, Li L, Yu H, Jin R, Zhu W.Ecological security evaluation of wetlands in Changbai Mountain area based on DPSIRM model.Ecol Indic 2024; 160:111773.

[42]	Wang Y, Si Q.Forecasting Chinese carbon emission intensity based on the interactive effect GM(1,N) power model.Grey Syst. 2024; 14(1):21-48.

[43]	Du W, Liao X, Tong Z, Rina S, Rong G, Zhang J, et al.Early warning and scenario simulation of ecological security based on DPSIRM model and Bayesian network: a case study of east Liaohe river in Jilin Province, China.J Clean Prod 2023; 398:136649.

[44]	Chen Y, Wang J.Ecological security early-warning in central Yunnan Province, China, based on the gray model.Ecol Indic 2020; 111:106000.

[45]	Sun C, Li P, Deng Y, Zhang C.Ecological regionalization of “Beautiful China” construction based on spatial heterogeneity of ecosystems.Acta Geogr Sin 2022; 77(11):2902-2919.

[46]	Li Y, Liu W, Feng Q, Zhu M, Zhang J, Yang L, et al.Spatiotemporal dynamics and driving factors of ecosystem services value in the Hexi regions, Northwest China.Sustainability 2022; 14(21):14164.

[47]	Li Y, Cai Y, Fu Q, Zhang X, Wan H, Yang Z.Dynamics of land use/land cover considering ecosystem services for a dense-population watershed based on a hybrid dual-subject agent and cellular automaton modeling approach.Engineering 2024; 37:182-195.

[48]	Xu M, Matsushima H.Multi-dimensional landscape ecological risk assessment and its drivers in coastal areas.Sci Total Environ 2024; 908:168183.

[49]	Daily GC, Matson PA.Ecosystem services: from theory to implementation.Proc Natl Acad Sci USA 2008; 105(28):9455-9456.

[50]	Wei Y, Huang C, Lam PTI, Yuan Z.Sustainable urban development: a review on urban carrying capacity assessment.Habitat Int 2015; 46:64-71.

[51]	Wang H, Lei X, Yan D, Wang X, Wu S, Yin Z, et al.An ecologically oriented operation strategy for a multi-reservoir system: a case study of the middle and lower Han River Basin, China.Engineering 2018; 4(5):627-634.

[52]	Li W, Kang J, Wang Y.Spatiotemporal changes and driving forces of ecological security in the Chengdu–Chongqing urban agglomeration, China: quantification using health–services–risk framework.J Clean Prod 2023; 389:136135.

[53]	Han B, Liu H, Wang R.Urban ecological security assessment for cities in the Beijing–Tianjin–Hebei metropolitan region based on fuzzy and entropy methods.Ecol Modell 2015; 318:217-225.

[54]	Han H, Guo L, Zhang J, Zhang K, Cui N.Spatiotemporal analysis of the coordination of economic development, resource utilization, and environmental quality in the Beijing–Tianjin–Hebei urban agglomeration.Ecol Indic 2021; 127:107724.

[55]	Wang Z, Liang L, Sun Z, Wang X.Spatiotemporal differentiation and the factors influencing urbanization and ecological environment synergistic effects within the Beijing–Tianjin–Hebei urban agglomeration.J Environ Manage 2019; 243:227-239.

[56]	Ding Z, Zheng H, Wang J, O P’connor, Li C, Chen X, et al.Integrating top-down and bottom-up approaches improves practicality and efficiency of large-scale ecological restoration planning: insights from a social–ecological system.Syst Eng 2023; 31:50-58.

[57]	Kim G.An integrated system of urban green infrastructure on different types of vacant land to provide multiple benefits for local communities.Sustain Cities Soc 2018; 36:116-130.

RIGHTS & PERMISSIONS

THE AUTHOR

PDF (4122KB)

4626

Accesses

Citation

Detail

Sections

Recommended

Browse

Authors & reviewers

About the journal

Abstract

Graphical abstract

Keywords

Cite this article

1. Introduction

2. Materials and methods

2.1. Analytical framework for ecological security

2.1.1. Ecological security assessment

2.1.2. Ecological security prediction

2.1.3. Ecological security zoning management

2.2. Study area

2.3. Data sources and preprocessing

3. Results

3.1. Spatiotemporal evolution of ecological security

3.2. Ecological security prediction

3.3. Ecological security zoning management

4. Discussion

4.1. Analytical framework of ecological security

4.2. Pressures, states, and responses of ecological security in the Daqing River Basin

4.3. Regulation strategies in ecological security zones

4.4. Limitations and future research directions

5. Conclusions

CRediT authorship contribution statement

Declaration of competing interest

Acknowledgment

Appendix A. Supplementary material

References

RIGHTS & PERMISSIONS