Spatiotemporal Evolution and Obstacle Factor Analysis of Land Ecological Security in the Surrounding Areas of Beijing, China

Abstract

Land ecological security is essential for sustainable land resource use and socioeconomic development. This study presents an evaluation index system combining single-indicator quantification, multi-indicator integration, and multi-criteria comprehensive assessment. It evaluates the land ecological security of 13 regions in the surrounding areas of Beijing from 2012 to 2021. Using Natural Breaks classification and hotspot and coldspot analysis, the study explores the temporal trends and spatial patterns of land ecological security. The obstacle degree model identifies constraining factors, and geographically weighted regression analyzes spatial heterogeneity. The results show the following: (1) The land ecological security index increased from 2012 to 2021, with rapid growth in southeastern Beijing and the three northern counties of Langfang, indicating high security levels. (2) The land ecological security of the region exhibits a symmetrical north–south distribution, with high-security areas concentrated in the Langfang Urban Area, Sanhe City, and Dachang County, while low-security areas are primarily found in Chicheng and Zhuolu counties. (3) The key factors hindering land ecological security are land economic density, fixed-asset investment per unit area, and GDP growth rate, with varying impacts across counties, showing significant spatial heterogeneity.

1. Introduction

Land is a fundamental resource for human social development and ecosystem balance, serving as the primary carrier for population, economic, and social activities [1,2]. However, with the rapid acceleration of global urbanization and industrialization, land resources are increasingly confronted with challenges such as degradation, pollution, over-exploitation, and ecosystem disruption [3]. Irregular land use has become a significant factor affecting food security, ecological stability, and sustainable economic development [4]. Achieving a balance between economic and social development and the protection of land’s ecological environment while ensuring land ecological security has become one of the major global issues of contemporary concern.

As one of the countries with the most intensive land resource use in the world, China has recently taken a series of measures in land ecological security management. The concept of “lucid waters and lush mountains are invaluable assets” has been proposed [4], and significant efforts have been made to advance ecological civilization construction. This involves integrating ecological civilization into economic development, optimizing the land space development pattern, and promoting the efficient intensification of production space, the livability of residential space, and ecologically harmonious landscapes with clear mountains and rivers [3]. The surrounding areas of Beijing serve as an ecological barrier for the capital, bearing the crucial responsibility of safeguarding its ecological security while also facing the challenge of economic development. The deep contradiction between the rigid constraint of ecological protection and the endogenous drive for economic growth makes this region a typical example for studying the coordination mechanisms of the human-environment relationship. Scientifically assessing the landscape ecological security of the surrounding areas of Beijing, analyzing its evolving trends, and identifying the main influencing factors are key issues for achieving sustainable regional development.

The concept of “land ecological security” can be traced back to Leopold’s “land health” theory proposed in the 1940s [5]. This theory posits that healthy land should possess self-restoration capacity and maintain ecological system stability and balance. With the intensification of global industrialization in the latter half of the 20th century, issues such as land pollution, desertification, and resource degradation became increasingly prominent, prompting scholars to conduct in-depth research on landscape ecological security [2,6,7]. In recent years, with the advancement of the Sustainable Development Goals (SDGs), landscape ecological security has evolved from being solely an ecological issue to a concept closely intertwined with social and economic sustainability. Research on landscape ecological security has thus become an integral component of global land resource management and ecological conservation efforts [8,9,10].

Existing research on land ecological security mainly focuses on three core areas: evaluation system construction, evaluation methods, and empirical analysis. (1) In terms of evaluation system development, early studies were primarily based on the Pressure–State–Response (PSR) model [11,12,13,14], which emphasizes the dynamic relationships between environmental pressure, ecological status, and human response. Subsequent research expanded into the Driver–Pressure–State–Impact–Response (DPSIR) model [15,16,17,18,19], which incorporates two additional factors—drivers and impacts—making it more suitable for multi-level ecological assessments. Additionally, some scholars have adopted the Economic–Social–Environmental (ESE) system approach to construct a landscape ecological security evaluation framework from the economic, social, and ecological dimensions [20,21], providing a more comprehensive reflection of the impact of landscape use on both the environment and socio-economic factors. However, few studies have approached land ecological security from the perspectives of ecological civilization and ecological livability. (2) Regarding evaluation methods, researchers have employed various approaches, including the ecological footprint method [22,23,24,25,26], the obstacle factors model [27,28,29], neural network methods [30,31,32,33], and geographically weighted regression (GWR) [34,35,36,37], for land ecological security assessment. Among these, the GWR model has been increasingly applied in recent years to explore spatial heterogeneity and identify key driving factors of land use change [38,39]. However, few studies have used the GWR model to analyze the spatial effects of obstacle factors. (3) In terms of empirical analysis, most studies have primarily focused on provincial [40], watershed [41,42], and urban agglomeration scales [43,44,45], while research at the county (district, city) scale remains limited, making it difficult to provide more targeted policy support.

Although a substantial body of research has explored land ecological security, most studies focus on the provincial, watershed, or urban agglomeration scales, with limited research at the county (municipality, district) level in the unique geographical unit of the surrounding areas of Beijing. Furthermore, existing evaluation systems have inadequately incorporated factors such as ecological civilization and ecological livability, making it difficult to comprehensively reflect the actual status of land ecological security. In terms of evaluation methods, current research predominantly focuses on traditional approaches, lacking an in-depth analysis of the spatial effects of obstacle factors. In response to the shortcomings of existing studies, and given the dual challenges faced by the surrounding areas of Beijing in terms of ecological barrier protection for the capital and regional economic development, it is essential to develop a more comprehensive evaluation index system. This system should integrate four dimensions: ecological environment, eco-economy, ecological livability, and ecological civilization. It systematically assesses the spatiotemporal evolution characteristics of land ecological security from 2012 to 2021, identifies and diagnoses key obstacle factors influencing land ecological security, and explores their spatial heterogeneity. This study not only enhances the understanding of the dynamic relationship between regional ecology and economy but also provides scientific evidence for achieving coordinated regional development and advancing ecological civilization construction.

To achieve the research objectives outlined above, this study focuses on 13 regions surrounding Beijing. It constructs a land ecological security evaluation index system based on four dimensions: ecological environment, eco-economy, ecological livability, and ecological civilization. The system integrates single-indicator quantification, multi-indicator integration, and multi-criteria comprehensive assessment to scientifically assess the spatiotemporal evolution characteristics of land ecological security. Additionally, the study utilizes an obstacle degree model and geographically weighted regression (GWR) to analyze obstacle factors and their spatial heterogeneity. The key issues explored in this study are as follows: (1) What are the spatiotemporal evolution characteristics of land ecological security in the surrounding areas of Beijing from 2012 to 2021? (2) What are the main obstacle factors affecting land ecological security in the surrounding areas of Beijing? (3) Do these obstacle factors exhibit significant spatial heterogeneity in their distribution? The research results will provide scientific support for regional ecological civilization construction, sustainable land resource management, and policy optimization.

2. Study Area and Data Sources

2.1. Study Area

Beijing is the capital of China, and this study focuses on the surrounding areas of Beijing, including Laishui County, Zhuozhou City, Huailai County, Zhuolu County, Chicheng County, Xinglong County, Luanping County, Fengning Manchu Autonomous County, Gu’an County, Xianghe County, Dachang Hui Autonomous County, Sanhe City, and the Langfang Urban Area (Figure 1), covering a total area of 30,016.20 square kilometers. The region’s topography features a distinct northwest-to-southeast gradient, with higher elevations in the northwest descending to lower elevations in the southeast. The northern Beijing area, comprising Chicheng County, Xinglong County, Fengning Manchu Autonomous County, and Luanping County, is characterized predominantly by plateau and mountainous terrain. The western area, including Huailai County, Zhuolu County, and Laishui County, features primarily mountainous and hilly landscapes. The southern and southeastern regions, encompassing Zhuozhou City, Gu’an County, the Langfang Urban Area, Sanhe City, Dachang Hui Autonomous County, and Xianghe County, are characterized by flat terrain. The climate of the surrounding areas of Beijing is classified as a temperate humid semi-arid continental monsoon climate, characterized by four distinct seasons, significant temperature variations between summer and winter, concentrated rainfall, and pronounced wet–dry contrasts. As a crucial area for China’s ecological civilization construction, the region has experienced rapid economic growth, increasing energy consumption, and accelerated urbanization. These factors have led to imbalances in the land ecological system and a decline in ecological functions, which have posed a severe threat to the ecological environment.

2.2. Data Sources

The per capita GDP, GDP growth rate, and urbanization rate for the surrounding areas of Beijing are primarily sourced from the Hebei Statistical Yearbook (2012–2021) [46]. Data on the proportion of the secondary and tertiary industry output to GDP, fixed asset investment per unit of land, land economic density, population density, natural population growth rate, and fertilizer usage are derived from the China County Statistical Yearbook (2012–2021) [47], as well as from local city statistical yearbooks and the National Economic and Social Development Statistical Bulletin. Data on areas of planted forests are sourced from the China Forestry and Grassland Statistical Yearbook (2012–2021) [48], while information on the per capita green space area, green coverage rate in built-up areas, sewage treatment rate, and harmless treatment rate of domestic waste are obtained from the China County Urban Construction Statistical Yearbook (2012–2021) [49].

Administrative boundary data are acquired from the National Fundamental Geographic Information System [50]. Nighttime light data are obtained from the National Earth System Science Data Center [51]. The Net Primary Productivity (NPP) data come from the MODIS product [52]. Nighttime light data and NPP data are pre-processed using ArcGIS 10.8 software, with masking applied based on the administrative boundary vector data of the study area. These datasets are then re-projected to a common coordinate system and standardized to a spatial resolution of 1 km.

3. Methods

This study employs multiple methods to systematically analyze the land ecological security status of the surrounding areas of Beijing. First, an evaluation index system is established, and a comprehensive index model is used to calculate the land ecological security level of each region. The results are presented in the form of line graphs, allowing for an in-depth analysis of the temporal changes in land ecological security. Additionally, the Natural Breaks method and hotspot and coldspot analysis are applied to assess the spatial variation characteristics. Building upon this, the obstacle degree model is employed to diagnose the primary obstacle factors, identifying the key constraints to land ecological security. Finally, the geographically weighted regression (GWR) model is utilized to analyze the spatial heterogeneity of the obstacle factors, revealing the regional differences in the influencing factors. Moreover, to further explore the degree of synergy between socio-economic development and land ecological security, a correlation analysis is conducted to calculate the correlation coefficient between nighttime light intensity and NPP data, thereby illustrating the impact of socio-economic development on land ecological security. This series of methods is interlinked, ensuring the study’s systematic approach and scientific rigor (Figure 2).

3.1. Construction of Evaluation Index System

This study considered the actual conditions of the surrounding areas of Beijing and selected 17 evaluation indicators based on previous scholarly research [53,54,55,56,57,58], covering four dimensions: ecological environment, eco-economy, ecological livability, and ecological civilization (

Table 1. Evaluation index system for land ecological security in the surrounding areas of Beijing.

Goal Layer	Criterion Layer	Indicator Layer	Weight	Indicator Explanation	Security Trend
Land Ecological Security Evaluation A	Ecological environment conditions (B1) 0.1494	Per Capita Green Area (C1)	0.0336	Green space area per permanent resident (hectares/10,000 people)	+
		Area of Planted Forests (C2)	0.0582	Hectare	+
		Sewage Discharge Volume (C3)	0.0256	100 million cubic meters	-
		Fertilizer Usage (C4)	0.0320	Pure amount, ton	-
	Eco-economy (B2) 0.4762	GDP Growth Rate (C5)	0.0506	(Current GDP—Previous GDP)/Previous GDP (%)	+
		Per Capita GDP (C6)	0.0722	Total GDP per permanent resident (10,000 yuan/person)	+
		Secondary Industry as % of GDP (C7)	0.0470	Secondary industry output value/Total GDP (%)	-
		Third Industry as % of GDP (C8)	0.0693	Tertiary industry output value/Total GDP (%)	+
		Fixed Asset Investment per Land Unit (C9)	0.1192	Total fixed asset investment/Administrative area (100 million yuan/square kilometer)	+
		Land Economic Density (C10)	0.1179	Total GDP/Administrative area (100 million yuan/square kilometer)	+
	Ecological livability (B3) 0.1508	Natural Population Growth Rate (C11)	0.0226	Birth rate—Death rate (%)	-
		Population Density (C12)	0.0435	Permanent residents/Administrative area (10,000 people/square kilometer)	-
		Urbanization Rate (C13)	0.0450	Urban permanent residents/(Urban permanent residents + Rural permanent residents) (%)	-
		Green Coverage Rate in Built-up Areas (C14)	0.0397	%	+
	Ecological civilization development (B4) 0.2236	Annual Precipitation (C15)	0.0608	Millimeter	+
		Sewage Treatment Rate (C16)	0.0322	%	+
		Harmless Treatment Rate of Domestic Waste (C17)	0.1306	%	+

Note: The indicator values in Table 1 are calculated based on the raw data of 13 regions (i.e., 13 counties, cities, and districts). Subsequently, each indicator was standardized to allow for comparison under a unified standard. A positive sign (+) indicates a positive relationship between the evaluation index and the evaluation result, while a negative sign (-) indicates a negative relationship between the evaluation index and the evaluation result.

). Specifically, the ecological environment refers to the quality of the natural environment in the region [59], with indicators such as per capita green space area, area of planted forests, sewage discharge volume, and fertilizer usage reflecting the ecological environmental quality. Ecological economy focuses on the relationship between economic activities and natural resources, with indicators such as GDP growth rate, per capita GDP, fixed asset investment per unit of land, and land economic density used to measure the development of the ecological economy. Ecological livability concerns the quality of human living conditions, represented by indicators such as the urbanization rate, population density, and green coverage rate in built-up areas. Ecological civilization emphasizes the construction of ecological values [60], the promotion of green development concepts, and the implementation of ecological protection policies, with indicators such as the harmless treatment rate of domestic waste, annual precipitation, and sewage treatment rate reflecting the progress of ecological civilization. The selection of these indicators is designed to provide a multidimensional evaluation system for land ecological security, offering a comprehensive assessment of land ecological security levels. The goal layer (A) represents the comprehensive value of land ecological security, the criterion layer (B) outlines the standards that constitute the goal layer, and the indicator layer (C) further subdivides the criterion layer.

Annual precipitation significantly influences agricultural production and reflects the coordination between natural precipitation and land resources in the region. This coordination has a significant impact on the ecological environment and aligns with the resource coordination development emphasized by ecological civilization. The harmless treatment rate of domestic waste refers to the application of advanced techniques and technologies to reduce environmental pollution from waste, thereby improving resource recovery efficiency [61]. Sewage treatment encompasses the purification processes necessary to meet water quality standards for either discharge or reuse [62]. Both waste and sewage treatment indicators reflect the human management of resources and responses to pollution control, embodying the ecological awareness and behaviors central to ecological civilization.

To eliminate the dimensional differences between the indicators, the extreme value normalization method [63] is applied to standardize the data, enabling the comparison of indicators on a uniform scale. The weights are determined using the Delphi method [2], which involves two rounds of consultation. The first round primarily verifies whether the factor system is aligned with practical realities and determines the range of weight variation for each factor. The second round of consultation revises the weight assessment from the first round based on the overall opinions’ tendency and dispersion. The second-round weight results from the experts are then subjected to statistical analysis to determine the final weight values for each factor, as detailed in Table 1.

This study selects indicators from four dimensions (ecological environment, eco-economy, ecological livability, and ecological civilization), considering the multidimensional characteristics of land ecological security. It synthesizes these indicators with the spatial characteristics of the study area to construct a comprehensive evaluation system based on a multidimensional assessment, demonstrating the uniqueness of the research approach. Compared to existing studies, this hierarchical approach not only aligns with the actual conditions of the region but also comprehensively reflects the complexity of land ecological security from multiple dimensions. This enables an in-depth analysis of the distinct impacts each level has on land ecological security, providing a scientific foundation for future ecological protection policies. Moreover, a two-round expert evaluation was conducted using the Delphi method to ensure the scientific validity of the weights and the applicability to the region. In the calculation of the composite index model, the interrelationships between indicator weights were thoroughly considered, reflecting the ecological security status of the region at various hierarchical levels. This approach enables a more precise representation of the spatial disparities and evolutionary process of land ecological security in the surrounding areas of Beijing.

3.2. Comprehensive Index Model

The comprehensive index model [64,65] was employed to calculate the land ecological security values for each region in the surrounding areas of Beijing from 2012 to 2021. In this model, higher comprehensive values indicate greater land ecosystem stability and higher levels of ecological security, while lower values suggest more fragmented ecological structures and reduced ecological security levels. The calculation is expressed by the following formula:

$$T={\sum }_{j=1}^n{W}_{ij}{X}_{ij}$$

(1)

where $T$ is the composite value of land ecological security; $W_{ij}$ is the weight of the $j$-th indicator in the $i$-th criterion layer; $X_{ij}$ is the standardized value of the indicator; and $n$ is the total number of indicators. The calculated values correspond to the composite value of land ecological security for each region in the surrounding areas of Beijing from 2012 to 2021.

3.3. Natural Breaks Method

The Natural Breaks method is a data classification technique that utilizes clustering principles to divide the values in a dataset into several natural groups, minimizing the variation within groups while maximizing the variation between groups [66]. The core principle of this method is to identify the “natural breaks” in the data based on its distribution characteristics, thereby optimizing both within-group and between-group differences. Unlike methods that rely on predefined intervals or thresholds, this approach segments the data according to its inherent structure. It is widely used in fields such as Geographic Information Systems (GIS), data visualization, and cluster analysis. In this study, the Natural Breaks method is employed to categorize the land ecological security levels of 13 regions in the Surrounding areas of Beijing, with adjustments made to account for practical considerations, ultimately determining the classification standards and levels.

3.4. Hotspot and Coldspot Analysis

Hotspot and coldspot analysis detects clusters of high and low values, uncovering localized correlations that may be masked by global patterns [67,68]. This method enables the identification of spatial distribution characteristics of land ecological security across different regions. The calculation formulas are as follows [69]:

$$G_i^{*}={\displaystyle \frac{{\sum }_{i=1}^n{W}_{ij}\left(d\right)x_i}{{\sum }_{i=1}^n{x}_i}}$$

(2)

$$Z\left(G_i^{*}\right)={\displaystyle \frac{G_i^{*}-E\left(G_i^{*}\right)}{\sqrt{var\left(G_i^{*}\right)}}}$$

(3)

where $G_i^{*}$ is the local statistic used to measure the concentration of county $i$ and its surrounding areas; $\left(d\right)$ is the spatial distance; $W_{ij}\left(d\right)$ is the spatial weight matrix of adjacent counties $i$ and $j$ based on distance; $x_i$ is the land ecological security level of county $i$; $n$ is the total number of counties in this study; and $Z\left(G_i^{*}\right)$ is the Z-score used to measure the statistical significance of spatial patterns. A $Z\left(G_i^{*}\right)$ value greater than 2.58 indicates a hotspot; a $Z\left(G_i^{*}\right)$ between 1.96 and 2.58 indicates a secondary hotspot; a $Z\left(G_i^{*}\right)$ between −1.96 and 1.96 indicates no significant change; a $Z\left(G_i^{*}\right)$ between −1.96 and −2.58 indicates a secondary coldspot; and a $Z\left(G_i^{*}\right)$ value less than −2.58 indicates a coldspot. $E\left(G_i^{*}\right)$ and $var\left(G_i^{*}\right)$ are the expected value and variance of $G_i^{*}$, respectively.

3.5. Obstacle Degree Model

Following the evaluation of land ecological security in the surrounding areas of Beijing, the obstacle degree model was applied to diagnose the main obstacle factors at both the criterion and indicator levels. This model identifies the factors constraining the development of land ecological security [70]. The calculation formulas are as follows:

$$F_{ij}=R_i\times W_{ij}$$

(4)

$$Q_{ij}=1-X_{ij}$$

(5)

where $F_{ij}$ is the factor contribution; $R_i$ is the weight of the $i$-th criterion layer; $W_{ij}$ is the weight of the $j$-th indicator in the i-th criterion layer; $Q_{ij}$ is the indicator deviation, representing the deviation of the indicator factor from the land ecological security goal layer; and $X_{ij}$ is the standardized value of the indicator [71].

The obstacle degree of land ecological security for the $j$-th indicator in the $i$-th criterion layer is calculated as

$$M_{ij}={\displaystyle \frac{F_{ij}\times Q_{ij}}{{\sum }_1^{17}{F}_{ij}\times Q_{ij}}}\times 100\mathrm{\%}$$

(6)

where $M_{ij}$ is the obstacle degree of the indicator factor, representing the degree of restriction of the indicator on land ecological security [72].

The obstacle degree of the criterion layer factor on land ecological security is

$$B_i=\sum M_{ij}$$

(7)

where $B_i$ is the obstacle degree of the $i$-th criterion layer, which reflects the extent to which the factors of the corresponding criterion layer constrain the level of land ecological security [73].

3.6. Geographically Weighted Regression Model

The geographically weighted regression (GWR) model is an analytical method for estimating local parameters, which allows for modeling based on spatially varying coefficients at each spatial location. This approach generates locally fitted regression coefficients and, compared to global models, provides a more accurate representation of the spatial heterogeneity of influencing factors [74,75,76]. The model is expressed as

$$y_h={\beta }_0\left(u_h,v_h\right)+\sum _{k=1}^n{\beta }_k\left(u_h,v_h\right)x_{hk}+{\epsilon }_h$$

(8)

where $y_h$ is the value of the dependent variable for county $h$; $\left(u_h,v_h\right)$ is the geographical center coordinates of county $h$; ${\beta }_0\left(u_h,v_h\right)$ is the intercept constant for county $h$; ${\beta }_k\left(u_h,v_h\right)$ is the regression coefficient for the $k$-th independent variable in county $h$; $x_{hk}$ is the value of the $k$-th independent variable for county $h$; and ${\epsilon }_n$ is the random error term for county $h$. In this study, the dependent variable is the land ecological security level, and the independent variables are the corresponding influencing factors.

3.7. Correlation Analysis

The Pearson correlation coefficient is employed to quantify the strength of the linear relationship between two variables. The formula is as follows:

$$r={\displaystyle \frac{\sum (x_i-\overline{x})(y_i-\overline{y})}{\sqrt{\sum {(x_i-\overline{x})}^2}\sqrt{\sum {(y_i-\overline{y})}^2}}}$$

(9)

where $r$ is the Pearson correlation coefficient; $x_i$ and $y_i$ are the $i$-th observation values of the two variables; and $\overline{x}$ and $\overline{y}$ are the mean values of the two variables. The value of $r$ ranges between −1 and 1, with a positive value indicating a positive correlation between the two variables, a negative value indicating a negative correlation, and the absolute value reflecting the strength of the linear relationship, with larger absolute values indicating stronger relationships [77]. In this study, the correlation coefficient between nighttime light data and NPP data is calculated using the Band Collection Statistics tool in ArcGIS.

4. Results

4.1. Temporal Characteristics of Land Ecological Security

Based on the evaluation indicator system established in Section 3.1, the comprehensive index model was employed to calculate the comprehensive land ecological security index for each region from 2012 to 2021. To visualize the land ecological security status of each region, the comprehensive land ecological security values for the period 2012–2021 were plotted in line graphs, as shown in Figure 3.

The comprehensive land ecological security values in the surrounding areas of Beijing exhibited a consistent upward trend from 2012 to 2021. Among the four sub-regions, the southeastern Beijing area and the three northern counties of Langfang experienced the most significant improvements. The Langfang Urban Area demonstrated the fastest growth, with the land ecological security value rising from 0.4656 in 2012 to 0.7306 in 2021, representing a 56.94% increase. Dachang Hui Autonomous County followed with an increase from 0.5308 to 0.6769, representing a 27.53% growth. Xianghe and Gu’an counties recorded growth rates of 17.74% and 15.80%, respectively. In contrast, the western and northern areas of Beijing showed more moderate growth, with rates remaining below 7%.

The southeastern Beijing area and the three northern counties of Langfang generally exhibited higher land ecological security levels. The Langfang Urban Area achieved the highest security value of 0.7306 in 2021, followed by Dachang County (0.6769), Sanhe City (0.5678), and Xianghe County (0.5026). This relatively high level of ecological security can be attributed to the region’s advanced economic development, high urbanization, effective ecological protection measures, and robust regulatory controls, which collectively contributed to a more robust ecological economy and improved ecological livability.

The land ecological security in the western and northern regions of Beijing was generally low, with slower growth rates. Chicheng and Xinglong counties experienced significant fluctuations in their security values, particularly showing a sharp decline in 2015. Despite rapid improvements thereafter, their values remained lower than those of Luanping and Fengning counties. These fluctuations were primarily influenced by variations in multiple factors, including area of planted forests, GDP growth rate, industrial sector contributions to GDP, fixed asset investment per unit of land, and urbanization rates. While the western Beijing region performed well in terms of per capita green space and area of planted forests, the relatively high sewage discharge constrained further improvements in ecological security, resulting in an overall security level that remained moderate.

4.2. Spatial Distribution Characteristics of Land Ecological Security

Based on the Natural Breaks method (Section 3.3) and hotspot and coldspot analysis (Section 3.4), an in-depth analysis of the spatial distribution characteristics of land ecological security in the surrounding areas of Beijing was conducted.

To better explore the spatial distribution of land ecological security in the surrounding areas of Beijing, this study employed the Natural Breaks method in Section 3.3 to classify land ecological security levels from 2012 to 2021. Three breakpoints, 0.37, 0.42, and 0.53, were identified, dividing the land ecological security into four levels [78]: low (0, 0.37], moderate (0.37, 0.42], good (0.42, 0.53], and high-quality (0.53, 1] (Figure 4).

The ecological security levels range from 0 to 1, with values closer to 1 indicating higher security levels and better conditions and values closer to 0 indicating lower security levels and poorer conditions.

From a spatial perspective, the land ecological security in the surrounding areas of Beijing exhibits distinct distribution patterns. The overall ecological security level is favorable, with a symmetrical north–south distribution pattern. High-quality areas are primarily concentrated in Langfang Urban Area, Sanhe City, and Dachang County, while low-value areas are mainly found in Chicheng and Zhuolu counties.

In the northern part, Fengning Manchu Autonomous County and Luanping County, located in mountainous regions, experience stable population growth and favorable ecological conditions. They also exhibit relatively high values in indicators such as per capita green space, area of planted forests, and green coverage rate in built-up areas, resulting in high land ecological security levels. Huailai County and Laishui County have made notable progress in urban development and population concentration, achieving good ecological security levels. Similarly, Zhuozhou City, Gu’an County, and Xianghe County, situated in the plains with favorable living conditions and higher levels of economic development, also show good land ecological security levels.

Chicheng County and Xinglong County experience fluctuations in their land ecological security levels, ranging between low and moderate, primarily due to slow economic growth and low precipitation levels. Zhuolu County, which requires improvements in population growth management and environmental protection, also remains in the low-security category.

In Langfang Urban Area, Sanhe City, and Dachang County, the rapid pace of economic development and high economic indicators, coupled with positive ecological civilization and ecological livability indicators such as sewage treatment rate, domestic waste treatment rate, and urbanization rate, indicate a successful balance between economic growth and environmental protection. These areas consistently maintain high levels of land ecological security.

To accurately analyze the spatial distribution of land ecological security in the surrounding areas of Beijing, hotspot and coldspot analysis in Section 3.4 was performed using the ArcGIS Hotspot Analysis tool. By applying z-scores and p-value tests, clusters of high- and low-value areas were identified [79]. As shown in Figure 5, high-value clusters are mainly concentrated in the southeastern part of the study area, with clustering intensity increasing from 2012 to 2021. The Langfang Urban Area is the primary hotspot, characterized by robust economic development, a favorable ecological environment, and high urbanization levels, resulting in a high land ecological security level. Dachang Hui Autonomous County is a secondary hotspot with generally good ecological security. In contrast, Chicheng County exhibits a low-value cluster, with its clustering intensity decreasing over the study period. Due to slow economic growth and low precipitation, Chicheng County’s ecological security level remains between low and moderate, significantly lagging behind neighboring regions and forming a secondary cold spot in the study area. The remaining regions do not display significant clustering.

Figure 5. Spatial distribution of hot and cold spots in land ecological security across the surrounding areas of Beijing from 2012 to 2021.

Figure 5. Spatial distribution of hot and cold spots in land ecological security across the surrounding areas of Beijing from 2012 to 2021.

4.3. Spatial Distribution Analysis of Land Ecological Security at Criterion Level

The surrounding areas of Beijing, as the core region for economic, environmental, and human settlement development around Beijing, face pressures regarding land ecological security from multiple dimensions, including environmental, economic, living conditions, and civilization development. The ecological environment, eco-economy, ecological livability, and ecological civilization, as the criterion layers of land ecological security, reflect the overall status of various ecological security factors within the region through their spatial distribution characteristics. Using the Natural Breaks method in Section 3.3, the criterion layers of land ecological security from 2012 to 2021 were classified, as shown in Figure 6, Figure 7, Figure 8 and Figure 9. The spatial distribution characteristics of these dimensions were analyzed to examine the development trends of land ecological security in different regions and explore the underlying causes.

Figure 6. Spatiotemporal distribution of ecological environment conditions in the surrounding areas of Beijing from 2012 to 2021.

Figure 6. Spatiotemporal distribution of ecological environment conditions in the surrounding areas of Beijing from 2012 to 2021.

Figure 7. Spatiotemporal distribution of eco-economic indicators in the surrounding areas of Beijing from 2012 to 2021.

Figure 7. Spatiotemporal distribution of eco-economic indicators in the surrounding areas of Beijing from 2012 to 2021.

Figure 8. Spatiotemporal distribution of ecological livability in the surrounding areas of Beijing from 2012 to 2021.

Figure 8. Spatiotemporal distribution of ecological livability in the surrounding areas of Beijing from 2012 to 2021.

Figure 9. Spatiotemporal distribution of ecological civilization in the surrounding areas of Beijing from 2012 to 2021.

Figure 9. Spatiotemporal distribution of ecological civilization in the surrounding areas of Beijing from 2012 to 2021.

The surrounding areas of Beijing exhibits distinct characteristics and development trends across four dimensions: ecological environment conditions (Figure 6), eco-economy (Figure 7), ecological livability (Figure 8), and ecological civilization (Figure 9). The northern and western Beijing areas demonstrate relatively strong performance in terms of ecological environment and livability, yet they face challenges in the areas of eco-economy and ecological civilization. In contrast, the southeastern region and the three northern counties of Langfang excel in eco-economy and ecological civilization, but require further improvements in ecological environment and livability. These regional variations are primarily attributed to geographical and developmental factors. The northern and western regions, including Fengning Manchu Autonomous County, Luanping County, Xinglong County, Zhuolu County, Huailai County, and Chicheng County, are characterized by higher elevations and predominantly mountainous and hilly terrain. These areas benefit from extensive green coverage, low population density, low urbanization rates, and minimal fertilizer use and wastewater discharge, which contribute to favorable ecological conditions and livability. However, these regions face economic challenges, reflected in the lower land economic density, lower per capita GDP, limited tertiary industry output, and inadequate waste and wastewater treatment, resulting in comparatively lower levels of eco-economy and ecological civilization. In contrast, the southeastern region, encompassing Zhuozhou City, Gu’an County, the Langfang Urban Area, and the three northern counties of Langfang (Dachang Hui Autonomous County, Xianghe County, and Sanhe City), features flat terrain and well-developed infrastructure. These areas have experienced rapid economic growth, with high shares of the tertiary sector, elevated per capita GDP, and effective waste and wastewater treatment systems. However, they face environmental challenges due to the lower per capita green space, smaller areas of planted forests, and higher urbanization rates, fertilizer usage, and wastewater discharge. As a result, while the eco-economy and ecological civilization levels in these regions are high, their ecological environment and livability conditions remain relatively poor.

4.4. Obstacle Degree Analysis of Land Ecological Security

Based on the previous spatiotemporal analysis of land ecological security in the surrounding areas of Beijing, the obstacle degree model in Section 3.5 is used to diagnose the main obstacle factors at both the criterion and indicator levels [79].

4.4.1. Analysis of Criterion-Level Obstacle Factors

Based on the obstacle degree model in Section 3.5, the obstacle degree at the criterion level for the surrounding areas of Beijing is calculated, and the results are shown in

Table 2. Obstacle factor analysis of land ecological security criterion layers in the surrounding areas of Beijing from 2012 to 2021.

Year	Obstacle Degree (%)
	Ecological Environment Conditions	Eco-Economy	Ecological Livability	Ecological Civilization Development
2012	7.68	78.76	5.31	8.25
2015	7.64	76.33	7.37	8.66
2018	7.37	77.65	6.97	8.01
2021	9.13	80.26	6.73	3.88
Average Data	7.96	78.25	6.60	7.19

.

Overall, from 2012 to 2018, the obstacle degree of the eco-economy remained the dominant obstacle factor, consistently above 75%. This was followed by ecological civilization, with ecological environment conditions ranking third, and ecological livability showed the lowest obstacle degree. Ecological civilization development had the lowest obstacle degree by 2021. Examining the changes in the obstacle degree across criterion layers from 2012 to 2021, ecological livability exhibited the largest variation, increasing from 5.31% in 2012 to 6.73% in 2021, representing a 26.74% change. The ecological environment conditions showed the second-largest change, rising from 7.68% to 9.13%, corresponding to an 18.88% increase. The eco-economy experienced minimal change, increasing from 78.76% to 80.26%, with a modest 1.90% increase. Ecological civilization showed the largest decrease, falling from 8.25% in 2012 to 3.88% in 2021, representing a 52.97% reduction. This analysis reveals that the eco-economy represents the primary obstacle to land ecological security in the surrounding areas of Beijing, followed by ecological environment, ecological civilization development, and ecological livability. To enhance regional land ecological security, it is essential to address the prominent issues within the eco-economy [80]. This requires the careful coordination of economic indicators through strategies such as industrial transformation, upgrading, and structural optimization to improve the quality of economic development while maintaining land ecological security, ultimately promoting coordinated development between ecological and economic systems.

4.4.2. Analysis of Indicator-Level Obstacle Factors

The obstacle degree of land ecological security indicators for the 13 regions in the surrounding areas of Beijing from 2012 to 2021 was calculated using the obstacle degree model. Given the large number of factors at the indicator level, this study identifies the top five factors with the greatest impact at both the beginning and end of the study period, based on their obstacle degrees. This approach is used to diagnose the obstacle degree of indicator factors in the study area [81] (

Table 3. Analysis of obstacle factors at the indicator level for land ecological security in the surrounding areas of Beijing.

County	Main Obstacles in 2012 (%)					Main Obstacles in 2021 (%)
	1	2	3	4	5	1	2	3	4	5
Laishui	C9	C10	C6	C15	C2	C9	C10	C6	C8	C2
	29.89	29.34	18.46	5.79	3.65	28.75	27.22	17.53	6.10	3.84
Zhuozhou	C9	C10	C6	C15	C7	C9	C10	C6	C5	C7
	28.47	19.25	13.98	7.90	5.14	29.22	16.54	13.05	9.14	5.65
Huailai	C9	C10	C6	C15	C17	C9	C10	C6	C5	C15
	32.25	29.94	13.54	7.24	4.10	27.39	26.97	15.45	7.73	7.01
Zhuolu	C9	C10	C6	C8	C15	C10	C9	C6	C8	C15
	26.36	25.71	13.60	7.66	5.95	24.89	24.66	15.39	6.80	6.16
Chicheng	C9	C10	C8	C6	C5	C9	C10	C6	C8	C5
	22.88	22.52	13.30	10.40	9.71	24.56	24.41	14.58	11.18	10.48
Xinglong	C9	C10	C8	C6	C7	C9	C10	C8	C6	C7
	24.58	24.01	12.52	11.55	7.98	25.65	24.86	12.65	12.36	8.12
Luanping	C9	C10	C8	C7	C6	C9	C10	C8	C7	C6
	26.50	25.40	14.34	10.11	8.30	27.01	25.74	15.91	10.79	7.90
Fengning	C9	C10	C6	C8	C7	C9	C10	C8	C6	C7
	26.76	26.50	13.44	11.67	5.97	25.44	25.07	12.98	12.79	6.93
Gu’an	C9	C10	C6	C8	C7	C9	C10	C6	C5	C8
	25.01	21.92	14.12	13.34	5.10	29.19	15.99	11.90	11.66	5.90
Xianghe	C9	C10	C8	C6	C7	C9	C10	C6	C5	C8
	25.01	16.55	12.18	10.09	9.15	29.39	15.00	13.83	6.64	6.57
Dachang	C9	C8	C10	C7	C5	C9	C5	C2	C7	C12
	24.88	17.12	13.92	12.26	6.89	45.86	23.29	8.57	5.05	4.87
Sanhe	C9	C8	C7	C5	C2	C9	C6	C7	C5	C2
	27.83	16.57	15.74	8.93	5.98	30.12	14.97	9.50	8.54	6.68
Langfang Urban Area	C10	C17	C6	C7	C5	C5	C7	C2	C10	C6
	21.64	18.66	14.66	8.73	8.68	23.29	14.80	11.70	10.78	9.21

Note: C9: fixed asset investment per unit of land; C10: land economic density; C6: per capita GDP; C7: proportion of secondary industry output in GDP; C8: proportion of tertiary industry output in GDP; C5: GDP growth rate; C2: area of planted forests; C15: annual precipitation; C17: harmless treatment rate of domestic waste; C12: Population density.

).

Table 3 reveals distinct variations in obstacle factors across different regions. In 2012, the Langfang Urban Area exhibited the highest obstacle degree in land economic density (C10), while fixed asset investment per unit of land (C9) ranked highest in other regions, with Huailai County having a particularly high obstacle degree of 32.25%. The main obstacles to land ecological security were land economic density and fixed asset investment per unit of land, followed by per capita GDP (C6) and the proportion of tertiary industry output to GDP (C8). Notably, ecological livability indicators were not identified as major obstacles, suggesting that factors such as natural population growth rate, population density, urbanization rate, and green coverage in built-up areas have positively contributed to regional land ecological security, reflecting effective management practices.

Compared to 2012, by 2021, fixed asset investment per unit of land (C9) remained the primary obstacle in 12 out of the 13 regions, with the exception of the Langfang Urban Area. This suggests that the growth effect of fixed asset investment per unit area was limited and continued to be a major constraint on land ecological security in most regions. In the Langfang Urban Area, however, the primary obstacle shifted from land economic density to GDP growth rate (C5), indicating an improvement in land economic density and signaling that this factor no longer plays a leading role in influencing land ecological security there. Notably, population density (C12) became one of the top five obstacles in Dachang Hui Autonomous County, highlighting the emerging conflict between increasing population density and ecological protection as economic development progresses. This characteristic, unique to Dachang Hui Autonomous County in 2021, serves as a warning for other counties and cities to optimize industrial structures and regulate population density. Overall, in 2021, the primary constraints on land ecological security in the study area remained under the eco-economy criterion. Therefore, it is crucial to continue advancing high-quality economic development, optimize industrial structures, and enhance economic growth drivers, while ensuring land ecological security, thus achieving coordinated development between economic growth and land ecological security

4.5. Geographically Weighted Regression Analysis

Based on the geographically weighted regression model in Section 3.6, a spatial heterogeneity analysis of the obstacle factors is conducted.

In this study, land ecological security level was set as the dependent variable. Based on the obstacle degree analysis, eight indicators were selected as independent variables: area of planted forests (C2), per capita GDP (C6), the proportion of the secondary industry output to GDP (C7), the proportion of the tertiary industry output to GDP (C8), fixed asset investment per unit area (C9), land economic density (C10), population density (C12), and annual precipitation (C15). Initially, Ordinary Least Squares (OLS) analysis was conducted to examine multicollinearity among these factors. The results revealed multicollinearity among area of planted forests (C2), per capita GDP (C6), land economic density (C10), and population density (C12). After removing the indicators with multicollinearity issues, the remaining indicators—the proportion of the secondary industry output to GDP (C7), the proportion of the tertiary industry output to GDP (C8), fixed asset investment per unit of land (C9), and annual precipitation (C15)—were subjected to regression analysis using the geographically weighted regression (GWR) model. The resulting regression coefficients were classified into four levels using the Natural Breaks method, generating spatial distribution maps that illustrate the degree of influence of each indicator on land ecological security levels, as shown in Figure 10.

Initially, Ordinary Least Squares (OLS) regression was employed to test for multicollinearity among the influencing factors. The analysis revealed multicollinearity issues among four variables: area of planted forests (C2), per capita GDP (C6), land economic density (C10), and population density (C12). After excluding these collinear variables, we conducted a geographically weighted regression (GWR) analysis. The resulting regression coefficients were classified into four levels using the Natural Breaks method to generate spatial distribution maps showing the impact of each indicator on land ecological security levels, as illustrated in Figure 10.

As illustrated in Figure 10, the influence coefficient of the proportion of secondary industry output to GDP (C7) is consistently negative, ranging from −0.7362 to −0.6355 (with larger absolute values indicating stronger influence). This suggests a negative correlation with land ecological security, indicating that higher proportions of secondary industry output in GDP correspond to lower levels of land ecological security. The influence of this factor generally increases from northeast to southwest across the study area, with a more significant effect in Huailai, Zhuolu, and Laishui counties, while the impact is relatively smaller in Luanping and Xinglong counties.

Figure 10. Spatial distribution of geographically weighted regression results.

Figure 10. Spatial distribution of geographically weighted regression results.

The influence coefficient of the proportion of tertiary industry output to GDP (C8) is consistently positive (5.8383–6.4467), indicating a positive correlation with land ecological security. This means that a higher proportion of tertiary industry output in GDP is associated with higher levels of land ecological security. The influence of this factor gradually intensifies from northwest to southeast, with the strongest effects observed in the Langfang Urban Area, Xianghe County, and Xinglong County, and relatively weaker effects in the northwestern counties of Zhuolu, Huailai, Chicheng, and Fengning.

Fixed asset investment per unit area (C9) shows positive influence coefficients (0.0681–0.0895), indicating that higher investment levels correspond to improved land ecological security. Its influence increases from northeast to southwest, with the strongest effects in Zhuolu County, Laishui County, Zhuozhou City, and Gu’an County, and the weakest in Fengning, Xinglong, and Luanping counties.

Annual precipitation (C15) demonstrates positive influence coefficients (0.1993–0.2199), showing that higher precipitation correlates with better land ecological security. Its influence increases from southeast to northwest, with the strongest effects in Fengning, Chicheng, Huailai, Zhuolu, and Laishui counties, and the weakest in Xinglong County and Xianghe County.

4.6. Nighttime Light Data and NPP Data Correlation Analysis

To further explore the coordination between economic development and land ecological security in the surrounding areas of Beijing, nighttime light index and Net Primary Productivity (NPP) were introduced in Section 3.7 to reflect their relationship. Nighttime light remote sensing detects faint nocturnal illumination, effectively capturing radiation signals emitted by artificial lighting from human activities. Net Primary Productivity (NPP) represents the net accumulation of organic matter produced by plants through photosynthesis per unit area and time, after accounting for autotrophic respiration. NPP reflects the energy available for plant growth, development, and reproduction, serving as the fundamental material basis for the survival and propagation of other organisms in the ecosystem [82]. As a key parameter in terrestrial ecological processes, NPP is a critical indicator for estimating Earth’s carrying capacity and assessing the sustainability of terrestrial ecosystems [83]. It has been widely applied in ecological security research. To more intuitively reflect the relationship between socio-economic development and ecological security, remote sensing data, particularly nighttime light data, are commonly used to represent the socio-economic development level of a region, while NPP is employed as an indicator to characterize the regional ecological security status. However, we recognize that NPP only reflects the production capacity of ecosystems and does not fully encompass all dimensions of ecological security. Ecological security is a multidimensional concept, and NPP alone cannot fully capture the complexity of this concept. Other factors, such as the various indicators listed in Table 1, also need to be considered. However, for the sake of clarity and convenience, this study employs NPP solely as a representative indicator of ecological security, providing an additional perspective to supplement and validate the previous research findings.

Analyzing the spatial distribution of nighttime light intensity and NPP in the surrounding areas of Beijing (Figure 11) reveals the following key findings: (1) From 2012 to 2021, high nighttime light intensity values were primarily concentrated in the southeastern part of the study area, exhibiting an overall increasing trend, which indicates robust economic development in this region. (2) Low NPP values were primarily concentrated in the southeastern part of the study area. However, from 2012 to 2021, NPP values exhibited an overall increasing trend, indicating continuous improvement in ecological conditions. This finding aligns with the earlier conclusion that the comprehensive land ecological security index in the surrounding areas of Beijing has shown an overall upward trend. (3) High nighttime light intensity values were often accompanied by low NPP values, implying that regions with higher socio-economic development levels tend to have lower ecological security quality.

To further investigate the coordination between socioeconomic development and ecological security, the Band Collection Statistics tool in the ArcGIS platform was employed to compute the correlation matrix of the two raster datasets. This analysis quantified the correlation between nighttime light intensity and NPP values in the surrounding areas of Beijing. The resulting correlation matrix provides Pearson correlation coefficients for nighttime light data and Net Primary Productivity (NPP) from 2012 to 2021, as presented in

Table 4. Correlation coefficients between nighttime light data and NPP in the study area.

Year	Correlation Coefficients
2012	−0.4821
2015	−0.5358
2018	−0.5484
2021	−0.5725

.

As shown in Table 4, the correlation coefficients between nighttime light data and NPP in the study area are consistently negative, indicating a statistically significant negative correlation between nighttime light values and NPP values. This demonstrates that as nighttime light intensity increases, NPP values decrease, suggesting that ecological security is adversely affected by socioeconomic development. From a temporal perspective, the absolute values of the correlation coefficients have exhibited a steady upward trend over the years, indicating that the negative impact of socioeconomic development on ecological security has progressively intensified. This trend suggests that while economic and social progress has accelerated, efforts toward environmental protection and regulatory measures have been insufficient. These findings align with the previous analysis of obstacle factors.

In summary, although the ecological security in the surrounding areas of Beijing has improved annually, the increasing impact of socioeconomic development has intensified over time, preventing the achievement of the anticipated coordinated development.

5. Discussion

5.1. Interpretation of Main Research Results

As previously mentioned, regions with high land ecological security are primarily concentrated in the Langfang Urban Area, Sanhe City, and Dachang County. This is mainly attributed to the region’s flat terrain and rapid economic development, and high performance in ecological–economic indicators such as land economic density and GDP growth rate, which rank among the highest in the capital surrounding region. In 2012, the General Office of the State Council issued the “National Plan for the Construction of Municipal Solid Waste Disposal Facilities”. The Langfang Urban Area accelerated its efforts to improve the harmless treatment of domestic waste, increasing the treatment rate from 58.98% in 2012 to 100% in 2015 [49]. This improvement led to a substantial rise in Langfang’s ecological civilization index, from 0.07 to 0.21, representing a 215.39% increase and positioning it at the forefront of the region in terms of ecological security. It is noteworthy that between 2015 and 2018, the Langfang Urban Area experienced a rapid expansion of urban construction land area due to accelerated economic development and urbanization. However, the area of planted forests declined sharply by 56.32% [48] leading to a significant decline in land ecological security levels. In response, the Langfang government implemented measures to enhance ecological civilization development, including increasing the sewage treatment rate (which reached 98.42%) [49]. These efforts contributed to a steady improvement in land ecological security over time.

The low-value land ecological security areas are mainly distributed in Chicheng County, Xinglong County, and Zhuolu County. This is primarily due to the low land economic density and its slow growth. In 2012, Chicheng County’s land economic density was only 1.23 million yuan/km², and by 2021, its growth rate was merely 1.98%. Xinglong and Zhuolu counties followed closely behind, resulting in a significantly low level of land ecological security [47]. In 2011, Hebei Province initiated the construction of a green economic circle surrounding Beijing. As part of this initiative, Chicheng County reduced its reliance on high-pollution, high energy-consuming mining industries and shifted towards more environmentally sustainable industries. This transition led to a significant short-term economic slowdown, with the GDP growth rate dropping from 7.50% in 2012 to 0.70% in 2015. During this period (2012–2015), land ecological security experienced a significant decline. However, in the following years, the county prioritized the development of emerging industries and green services, resulting in a sustained increase in the proportion of tertiary industry output to GDP, which grew by 100% [47]. Consequently, land ecological security levels steadily improved.

5.2. Comparison with Existing Studies

This study analyzes the land ecological security status of 13 regions in the surrounding areas of Beijing and finds that from 2012 to 2021, the comprehensive land ecological security index exhibited an upward trend, with particularly rapid growth in the southeastern Beijing region and the three northern counties of Langfang. Compared with the findings of Liu et al. (2021) [84], which were based on the ecological footprint method, this study not only verifies the improvement in ecological security levels in the surrounding areas of Beijing but also identifies key obstacle factors affecting land ecological security, including land economic density, fixed asset investment per unit of land, and GDP growth rate. The results indicate significant spatial variations in the degree to which these factors act as obstacles across different counties, aligning with the conclusions of Guo et al. (2021) [85] regarding the Beijing–Tianjin–Hebei region. Furthermore, this study analyzes the spatial heterogeneity of these obstacle factors, underscoring its novelty. Furthermore, to explore the coordination between economic development and land ecological security, this study introduces nighttime light index and NPP as indicators of their relationship, further confirming that the eco-economy and its associated indicators are the primary obstacle factors influencing land ecological security.

5.3. Countermeasures and Suggestions

Based on the above research, it is recommended that region-specific development strategies be adopted. For high-value areas such as the Langfang Urban Area, emphasis should continue to be placed on advancing green economic development by integrating green spaces into urban planning to enhance long-term environmental quality. This approach would promote complementary and coordinated development across the surrounding areas of Beijing, fostering interconnected industrial chains, supporting industrial clusters, enabling resource sharing, and leveraging complementary advantages, ultimately driving the overall economic development of the region.

For low-value areas such as Chicheng County and Xinglong County, priority should be given to advancing ecological and economic innovation and optimizing the industrial structure, particularly through the development of renewable energy industries, modern service sectors, and other emerging industries. This would help cultivate regionally distinct industrial clusters, improve industrial competitiveness, and facilitate the monetization and capitalization of natural resources.

5.4. Limitations and Prospects

Land ecological security is influenced by multiple dimensions, including ecological, economic, social, and policy factors, making its driving mechanisms highly complex. Although this study constructs an evaluation indicator system from multiple perspectives, it overlooks the impact of qualitative factors such as policy interventions on land ecological security. Future research should refine the evaluation system by incorporating qualitative factors and exploring quantitative methods for their assessment to enhance the comprehensiveness and accuracy of the results. In terms of research methodology, the obstacle degree model used in this study does not fully account for the influence of policy interventions and other external factors, which may lead to an underestimation of the impact of certain obstacle factors on land ecological security. Additionally, while this study identifies the key obstacle factors affecting land ecological security, it does not delve deeply into the intrinsic relationships and mechanisms among these factors. Future research should further explore the causal relationships behind these factors, as well as the socio-economic mechanisms, policy interventions, and regulatory frameworks that influence them. This will contribute to a more comprehensive theoretical foundation for ecological civilization construction and regional sustainable development.

6. Conclusions

This study aims to evaluate land ecological security in the surrounding areas of Beijing, analyze its spatiotemporal variation characteristics, and identify the primary obstacle factors. The main contribution of this research lies in the development of a land ecological security evaluation system that integrates single-indicator quantification, multi-indicator integration, and multi-criteria comprehensive assessment across four dimensions: ecological environment, eco-economy, ecological livability, and ecological civilization. By employing the obstacle degree model and geographically weighted regression analysis, this study identifies the key obstacle factors affecting land ecological security and examines their spatial distribution patterns, thereby advancing the theoretical understanding of the relationship between land ecological security and sustainable economic development. The key findings are as follows:

(1)

The comprehensive land ecological security index in the Surrounding areas of Beijing exhibited an overall upward trend from 2012 to 2021. The southeastern areas of Beijing and the three northern counties of Langfang experienced relatively rapid growth, maintaining a higher level of land ecological security. In contrast, the western and northern areas of Beijing showed lower levels of land ecological security with slower growth. Land ecological security in the surrounding areas of Beijing demonstrated distinct spatial distribution characteristics, displaying a symmetrical spatial distribution pattern along the north–south axis. High-security areas were mainly concentrated in the Langfang Urban Area, Sanhe City, and Dachang County, whereas low-security areas were predominantly found in Chicheng County and Zhuolu County. Hotspot and coldspot analysis further revealed that the Langfang Urban Area was identified as the primary hotspot, followed by Dachang Hui Autonomous County as a secondary hotspot. Conversely, Chicheng County was classified as a secondary coldspot within the study area, while other regions exhibited no significant clustering patterns.

(2)

The main obstacle factors affecting land ecological security in the surrounding areas of Beijing include land economic density, fixed asset investment per unit of land, and GDP growth rate. Eco-economy is the primary obstacle factor, followed by ecological environment, ecological civilization, and ecological livability.

(3)

The results of the geographically weighted regression (GWR) analysis show significant spatial heterogeneity in the distribution of different factors. The proportion of secondary industry output to GDP is negatively correlated with land ecological security, while the proportion of tertiary industry output to GDP, fixed asset investment per unit of land, and annual precipitation are positively correlated with land ecological security. The impact of each factor varies significantly across different regions.