*2.2. Data Sources*

We used a digital elevation model (DEM) and multiple datasets, including land use, meteorological, normalized difference vegetation index (NDVI), PM 2.5, and socioeconomic data and statistics. The basic data used in this study and the data sources are listed in Table 1.

**Table 1.** Data information and sources.


### **3. Material and Methods**

*3.1. Research Framework*

This study was conducted within a framework of four steps (Figure 2): the first step introduces the realistic needs, theoretical basis, and literature gaps related to ES. Then, the paper describes the study area and data sources, constructs an evaluation index system for ES, and introduces the methods. The third step analyzes the results. The last step expounds the conclusions and policy implications.

**Figure 2.** The research framework used in this study. **Figure 2.** The research framework used in this study.

#### *3.2. Construction of the Index System 3.2. Construction of the Index System*

terns and ESVs is as follows:

Ecological security is a complex system involving nature, the economy, and human society [20,42]; the level of ES in any area is constantly changing [30,43]. In the past, the assessment of ES generally involved the construction of an index system from the perspective of ecosystem structure and function [12], but it did not consider human beings as external factors. In reality, human beings have already become an indispensable part of the Earth's ES system. Supporting the sustainable development of the economy and society is an important reason to study ES [43]. Therefore, the present study evaluates the state of ES in border areas from five subsystems: economy, society, environment, landscape pattern, and ecosystem service value (ESV). The process of calculating the landscape pat-Ecological security is a complex system involving nature, the economy, and human society [20,42]; the level of ES in any area is constantly changing [30,43]. In the past, the assessment of ES generally involved the construction of an index system from the perspective of ecosystem structure and function [12], but it did not consider human beings as external factors. In reality, human beings have already become an indispensable part of the Earth's ES system. Supporting the sustainable development of the economy and society is an important reason to study ES [43]. Therefore, the present study evaluates the state of ES in border areas from five subsystems: economy, society, environment, landscape pattern, and ecosystem service value (ESV). The process of calculating the landscape patterns and ESVs is as follows:

fect ES [18]. A landscape index can describe the change in landscape pattern. Landscape indices are used to determine the relationship between landscape pattern and the process

(1) Selection and calculation of landscape metrics.

(1) Selection and calculation of landscape metrics.

The evolution of landscape pattern leads to spatial changes in the landscape results, which directly reflect the changes in ecosystem structure and composition and finally affect ES [18]. A landscape index can describe the change in landscape pattern. Landscape indices are used to determine the relationship between landscape pattern and the process of landscape change [36]. In this study, the number of patches (NP), patch density (PD), largest patch index (LPI), edge density (ED), landscape shape index (LSI), splitting index (SPLIT), Shannon's diversity index (SHDI), and aggregation index (AI) were selected to reflect the level of landscape pattern subsystem [44,45]. The selected landscape index was calculated using FRAGSTATS 4.2 software.

(2) Calculation of ESV.

Ecological security depends on the level of ecosystem services provided by an ecosystem to human beings [46]. At present, ES has become an important research topic designed to bring ecosystem services into the evaluation of ES systems [46,47]. In 1997, Costanza et al. [48] put forward the method of evaluating global scale ecosystem services with ESV, and Xie, a Chinese scholar [49], summarized an equivalent factor of ESV per unit area according to the actual situation of China. In this study, we referred to their methods and modified the above-mentioned equivalent factor. For the border areas analyzed here, the following standards were used: cropland corresponds to dry land; the equivalent factor for forest is taken as the average value of coniferous, mixed, and broad-leaved forests; grassland corresponds to prairie; snow/ice corresponds to glacier and snow [50]; and impervious areas were assigned "0" [51] (Table S1).

The economic value of one ecological service equivalence factor is 1/7 the grain output value per unit area [49], and the economic value of the equivalent factor in border areas was calculated according to Equation (1). To eliminate the impact of crop price fluctuation on the total value, the area, yield, and average price of the three main crops (rice, corn, and wheat) were selected as the basic data. The calculation process is as follows:

$$V\mathbb{C}\_0 = \frac{1}{7} \sum\_{i=1}^{n} \frac{m\_i p\_i q\_i}{M} (n = 1, 2, 3) \tag{1}$$

According to Equation (1), the economic value of one equivalent factor of ESV in border areas is 1817.76 yuan/ha, and the ESV coefficient per unit area of land use type was obtained (Table S2). Referring to the method of Hu et al. [36], the sensitivity index of the ESV coefficient for all land use types was obtained (Table S3). The sensitivity index of the ESV coefficient was all less than 1, which indicates that the estimated total ESV in the study area is not elastic to the equivalent factor.

The index system of ES is shown in Table 2.




**Table 2.** *Cont.*

#### *3.3. Entropy Weight TOPSIS Model*

The entropy weight method is an objective weighting method, which uses entropy to indicate the information's size. Generally, the larger the gap between feature values, the larger the size of information it possesses [56]. The technique for order preference by similarity to ideal solution (TOPSIS) is a multi-objective decision-making method. The principle is to rank the evaluation objects according to the closeness of positive and negative ideal solutions. At present, the combination of entropy weight method and TOPSIS method has been widely used in land use planning, sustainable development assessment, and other fields [57,58]. This study selected entropy weight TOPSIS model to evaluate the level of the ES in border areas. The calculation process is as follows:

(1) Data standardization was completed using the following equation:

.

$$\begin{cases} \text{ } g\_{ij} = \frac{y\_{ij} - y\_{\text{min}}}{y\_{\text{max}} - y\_{\text{min}}}, \text{ where } \text{g}\_{ij} \text{ is a positive indicator} \\\text{ } g\_{ij} = \frac{y\_{\text{max}} - y\_{ij}}{y\_{\text{max}} - y\_{\text{min}}}, \text{where } \text{g}\_{ij} \text{ is a negative indicator} \end{cases} \tag{2}$$

where *gij* is the normalized value; and *ymax* and *ymin* represent the maximum and minimum values of the *j th* index, respectively.

(2) The information entropy of each index was calculated using the following equation:

$$H\_{\hat{j}} = -\frac{1}{\ln n} \left( \sum\_{i=1}^{n} f\_{i\hat{j}} \ln f\_{i\hat{j}} \right) \tag{3}$$

where *fij* = 1 + *gij* / *n* ∑ *i*=1 1 + *gij*  (3) The index weights were determined using the following equation:

$$w\_j = \frac{1 - H\_j}{m - \sum\_{j=1}^{m} H\_j} \tag{4}$$

(4) The weight normalization matrix was constructed using the following equation:

$$\mathcal{L}\_{\text{ij}} = \mathcal{g}\_{\text{ij}} \* w\_{\text{j}} \tag{5}$$

where *cij* is the weighted normalized decision matrix; and *w<sup>j</sup>* is the weight value of the *j th* index.

(5) The positive and negative ideal solutions, *C <sup>+</sup>* and *C* −, respectively, were calculated using the following equation:

$$\left\{ \begin{array}{c} \mathbb{C}^{+} = \left\{ \max c\_{ij} \middle| i = 1, 2, \dots, m \right\} = \left\{ c\_{1} \overset{+}{\cdot}, c\_{2} \overset{+}{\cdot}, \overset{-}{\cdot}, c\_{j} \overset{+}{\cdot} \right\} \\\\ \mathbb{C}^{-} = \left\{ \min c\_{ij} \middle| i = 1, 2, \overset{+}{\cdot}, \dots, m \right\} = \left\{ c\_{1} \overset{-}{\cdot}, c\_{2} \overset{-}{\cdot}, \overset{-}{\cdot}, c\_{j} \overset{-}{\cdot} \right\} \end{array} \tag{6}$$

where *C <sup>+</sup>* and *C* − refer to the optimal and the least considered decision schemes, respectively. (6) The distance from the index value of each evaluation object to *C <sup>+</sup>* and *C* − was

calculated using the following equations:

$$\begin{cases} \mathcal{S}\_{j}^{+} = \sqrt{\sum\_{j=1}^{n} \left(c\_{j}^{+} - c\_{i j}\right)^{2}}, i = 1, 2, \cdots, m \\\ S\_{j}^{-} = \sqrt{\sum\_{j=1}^{n} \left(c\_{j}^{-} - c\_{i j}\right)^{2}}, i = 1, 2, \cdots, m \end{cases} \tag{7}$$

where *S<sup>j</sup> <sup>+</sup>* and *S<sup>j</sup>* − refer to the distance of the assessment vector to the positive and negative ideal solutions, respectively.

(7) The closeness of each evaluation object to the ideal solution was calculated using the following equation:

$$R\_i = \frac{\mathcal{S}\_{\dot{j}}^{-}}{\mathcal{S}\_{i}^{+} + \mathcal{S}\_{i}^{-}} \tag{8}$$

where *R<sup>i</sup>* is the closeness of the evaluation object to the optimal solution, and the range of *Ri* is 0–1. The greater the value of *R<sup>i</sup>* , the higher the ES level. Referring to the research of Cui et al. [59], we divided the level of ES into five levels (Table 3).

#### *3.4. Trend Surface Analysis*

Trend surface analysis is a method that can be used to simulate the spatial distribution law and change trend in geographical elements with a smooth mathematical surface [60]. The actual surface is divided into two components: a regional trend and residual values. The regional trend is calculated by using a polynomial surface with continuous power, and the residual values are the arithmetic difference between the original data and the trend surface, indicating local fluctuations. This study used this method to analyze the spatial differentiation trend in ES [61], as follows:

$$Z\_i(\mathbf{x}\_i, y\_i) = T\_i(\mathbf{x}\_i, y\_i) + \varepsilon\_i \tag{9}$$

where *Z<sup>i</sup>* (*xi* , *y<sup>i</sup>* ), *T<sup>i</sup>* (*xi* , *y<sup>i</sup>* ), and *ε<sup>i</sup>* represent the observed, trend, and residual values of variable *Z* at location (*x<sup>i</sup>* , *y<sup>i</sup>* ), respectively.


**Table 3.** Evaluation criteria of ecological security.

### *3.5. Obstacle Degree Model*

Based on the ES evaluation of border areas, identifying the key factors that directly influence the ES can help to present adaptation measures designed to help land managers preferably maintain the ES in border areas and promote sustainable regional development. Therefore, our study used an obstacle degree model to analyze the factors creating obstacles and influencing ES. The calculation process is as follows [62]:

$$P\_{\rm ij} = 1 - g\_{\rm ij} \tag{10}$$

$$\mathcal{M}\_{ij} = \frac{\mathcal{R}\_{ij} P\_{ij}}{\sum\_{\substack{m\\i,j=1}}^{m} \mathcal{R}\_{ij} P\_{ij}} \times 100\% \tag{11}$$

where *Mij* represents the obstacle degree of the *i th* indicator in year *j*, and *Pij* is the degree of deviation for indicator *i* in year *j*.

#### *3.6. GM (1,1) Gray Prediction Model*

The gray system theory was first put forward by Deng [63]. A gray prediction model (GM) (1,1) adopts the basic concept of the gray system theory and has been widely used in the fields of ecology and social economics [64,65]. It has unique advantages in predicting and analyzing objects and process systems with small amounts of data, no obvious change law of data, has an unclear structural relationship, and has an operation mechanism. The prediction calculation process is simple and accurate. Considering that the change in ES has fuzzy and uncertain characteristics [43], and predicting its change is a typical gray evaluation process, GM (1,1) is selected to predict the ES level of border areas in 2025 and 2030. The calculation process is as follows:

(1) Preprocess the data. The original sequence *Y<sup>i</sup>* (0) of all data from 2004 to 2019 is set as follows:

$$\mathbf{Y}\_{\mathbf{i}}^{(0)} = \left[ \mathbf{Y}\_{\mathbf{i}}^{(0)}(\mathbf{1}), \mathbf{Y}\_{\mathbf{i}}^{(0)}(\mathbf{2}), \dots, \mathbf{y}\_{\mathbf{i}}^{(0)}(\mathbf{1}6) \right] \tag{12}$$

Use Equation (13) to obtain a new sequence *Y*<sup>i</sup> (1) (*k*):

$$Y\_{\dot{l}}^{(1)}(k) = \sum\_{i=1}^{k} Y\_{\dot{l}}^{(0)} = Y\_{\dot{l}}^{(1)}(k-1) + Y\_{\dot{l}}^{(0)}(k) \tag{13}$$

$$\mathbf{Y}\_{i}^{(1)} = \left[ \mathbf{Y}\_{i}^{(1)}(1), \mathbf{Y}\_{i}^{(1)}(2), \dots, \mathbf{Y}\_{i}^{(1)}(16) \right] \tag{14}$$

(2) Set up the gray differential equation:

$$Y\_i^{(1)}(k+1) = \left[Y\_i^{(0)} - \frac{b}{a}\right]e^{-ak} + \frac{b}{a} \tag{15}$$

where the values of parameters a and b are calculated by the least squares method [66].

(3) Predict the data. Based on Equation (16), the ES level of border areas in 2025 and 2030 is predicted as follows:

$$
\stackrel{\wedge}{Y}\_{l}^{(0)}(k+1) = \stackrel{\wedge}{Y}\_{l}^{(1)}(k+1) - \stackrel{\wedge}{Y}\_{l}^{(1)}(k) \tag{16}
$$

(4) Test the accuracy of the prediction data. After the fitting value ∧ *Yi* (0) of ES is obtained using Equation (16), the correctness of the model needs to be tested according to the original *Y<sup>i</sup>* (0) and fitting ∧ *Yi* (0) sequences. The mean absolute percent error (MAPE) is usually used to test the accuracy of the model:

$$MAPE = \frac{1}{n} \sum\_{k=1}^{n} \left| \frac{\mathbf{Y}\_{i}^{(0)}(k) - \mathbf{Y}\_{i}^{(0)}(k)}{\mathbf{Y}\_{i}^{(0)}(k)} \right| \tag{17}$$

We referred to the studies of Wang et al. [66] and Wang and Li [67]; when the MAPE is less than 10%, the forecasting is highly accurate. After calculation, the MAPE of all the data that were predicted in this paper was less than 10% (Table 4), which meets the requirement needed to verify prediction accuracy.

**Table 4.** Mean absolute percent error (MAPE) for each county, which was used to test the accuracy of the model; a MAPE > 10% indicates that the predicted results are highly accurate.


#### **4. Results**

#### *4.1. Temporal Changes in ES*

Based on an entropy weight TOPSIS model, we calculated the ES of each border county. From 2004 to 2019, the ES of all border counties showed a positive upward trend (Figure 3). Tengchong, Jinghong, and Ruili ranked among the top three counties in terms of the increase in ES. Among them, the ES of Tengchong increased from 0.4124 in 2004 to 0.6417 in 2019, an increase of 0.2294. The ES of Jinghong increased from 0.4798 in 2004 to 0.6882 in 2019, an increase of 0.2084. The ES of Ruili increased from 0.1994 in 2004 to 0.3537 in 2019, an increase of 0.1544.

**Figure 3.** Evaluation scores for temporal changes in ecological security and subsystems (economic, social, environmental, landscape pattern, and ecosystem service value (ESV)) in 25 border counties of Yunnan Province, China. **Figure 3.** Evaluation scores for temporal changes in ecological security and subsystems (economic, social, environmental, landscape pattern, and ecosystem service value (ESV)) in 25 border counties of Yunnan Province, China.

We also analyzed the time trend in five subsystems, namely, the economic, social, environmental, landscape pattern, and the ecosystem service value subsystems (Figure 3). All border counties generally showed an upward trend for their economic and social subsystems, with the economic subsystem of Tengchong rising the most, from 0.1186 in 2004 to 0.6871 in 2019, an increase of 0.5685. The social subsystem of Mangshi rose the most, from 0.3296 in 2004 to 0.7634 in 2019, an increase of 0.4338. The counties of Cangyuan, Fugong, Funing, Gengma, Jinping, Jinghong, Longchuan, Lvchun, Malipo, Menghai, Mengla, Menglian, and Yingjiang showed a fluctuating upward trend in their environmental subsystems, while the level of the environmental subsystems of other counties generally decreased. Jinping experienced the largest increase in terms of landscape pattern subsystem, from 0.3966 in 2004 to 0.5095 in 2019, an increase of 0.1128. the ESV sub-We also analyzed the time trend in five subsystems, namely, the economic, social, environmental, landscape pattern, and the ecosystem service value subsystems (Figure 3). All border counties generally showed an upward trend for their economic and social subsystems, with the economic subsystem of Tengchong rising the most, from 0.1186 in 2004 to 0.6871 in 2019, an increase of 0.5685. The social subsystem of Mangshi rose the most, from 0.3296 in 2004 to 0.7634 in 2019, an increase of 0.4338. The counties of Cangyuan, Fugong, Funing, Gengma, Jinping, Jinghong, Longchuan, Lvchun, Malipo, Menghai, Mengla, Menglian, and Yingjiang showed a fluctuating upward trend in their environmental subsystems, while the level of the environmental subsystems of other counties generally decreased. Jinping experienced the largest increase in terms of landscape pattern subsystem, from 0.3966 in 2004 to 0.5095 in 2019, an increase of 0.1128. the ESV subsystem of all border counties changed little during the study period.

#### system of all border counties changed little during the study period. *4.2. Spatial Changes in ES*

*4.2. Spatial Changes in ES*  Our results show the spatial changes in ES in border areas in 2004, 2009, 2014, and 2019 (Figure 4). In 2004, the ES level of all border counties was lower than the good level, among which Lincang, Jinghong, and Mengla had the highest ES level and were classified as being in a sensitive state. In 2009, the level of ES in Mangshi changed from critical to the unstable state. In 2014, the level of ES in Jinghong first reached the good state. Tengchong changed from the unstable to the sensitive state, while Longling, Zhenkang, Our results show the spatial changes in ES in border areas in 2004, 2009, 2014, and 2019 (Figure 4). In 2004, the ES level of all border counties was lower than the good level, among which Lincang, Jinghong, and Mengla had the highest ES level and were classified as being in a sensitive state. In 2009, the level of ES in Mangshi changed from critical to the unstable state. In 2014, the level of ES in Jinghong first reached the good state. Tengchong changed from the unstable to the sensitive state, while Longling, Zhenkang, and Lvchun changed from the critical to the unstable state. In 2019, the number of border counties in the critical state had decreased, with only Ximeng, Menglian, and Malipo in the critical

and Lvchun changed from the critical to the unstable state. In 2019, the number of border counties in the critical state had decreased, with only Ximeng, Menglian, and Malipo in

state, showing that the ES level in the southwestern part of the study area was higher than that in the west and east. the critical state, showing that the ES level in the southwestern part of the study area was higher than that in the west and east.

**Figure 4.** Spatial change in ecological security in 25 border counties in Yunnan Province, China for: (**a**) 2004; (**b**) 2009; (**c**) 2014; and (**d**) 2019. Note: Maps were created by authors using ArcGIS 10.7. **Figure 4.** Spatial change in ecological security in 25 border counties in Yunnan Province, China for: (**a**) 2004; (**b**) 2009; (**c**) 2014; and (**d**) 2019. Note: Maps were created by authors using ArcGIS 10.7.

Based on the evaluation of ES in 2004, 2009, 2014, and 2019, the Geostatistical Analyst tool in ArcGIS 10.7 was applied to visualize the spatial representation of ES conditions. A spatial change trend map of the ES was obtained thereby (Figure 5). The X and Y axes indicate the east and north directions, respectively, while the *Z*-axis indicates the size of the ecological safety assessment value. The green and blue lines in Figure 5 represent the fitting curve of ES in the east–west and north–south directions [68]. In the east–west direction, the trend lines of the four years analyzed here remained stable, and all showed an "inverted U-shaped" distribution (Figure 5), characterized by the central part being higher than the western and eastern parts, while the western part was higher than the eastern part. In the north–south direction, the trend line changed from a "U-shaped" distribution to a "straight line," characterized by higher values in the southern and northern parts than in the central part, while the gap has become significantly narrow; the ES of the northern part has always remained lower than that of the southern part. These results are consistent Based on the evaluation of ES in 2004, 2009, 2014, and 2019, the Geostatistical Analyst tool in ArcGIS 10.7 was applied to visualize the spatial representation of ES conditions. A spatial change trend map of the ES was obtained thereby (Figure 5). The X and Y axes indicate the east and north directions, respectively, while the *Z*-axis indicates the size of the ecological safety assessment value. The green and blue lines in Figure 5 represent the fitting curve of ES in the east–west and north–south directions [68]. In the east–west direction, the trend lines of the four years analyzed here remained stable, and all showed an "inverted U-shaped" distribution (Figure 5), characterized by the central part being higher than the western and eastern parts, while the western part was higher than the eastern part. In the north–south direction, the trend line changed from a "U-shaped" distribution to a "straight line," characterized by higher values in the southern and northern parts than in the central part, while the gap has become significantly narrow; the ES of the northern part has always remained lower than that of the southern part. These results are consistent with the spatial distribution pattern of high in the southwest and low in the east and west.

with the spatial distribution pattern of high in the southwest and low in the east and west.

**Figure 5.** Spatial change trend in ecological security in 25 border counties in Yunnan Province, China for: (**a**) 2004; (**b**) 2009; (**c**) 2014; and (**d**) 2019. **Figure 5.** Spatial change trend in ecological security in 25 border counties in Yunnan Province, China for: (**a**) 2004; (**b**) 2009; (**c**) 2014; and (**d**) 2019.

## *4.3. Diagnosis of Obstacle Factors for ES*

#### *4.3. Diagnosis of Obstacle Factors for ES*  4.3.1. Analysis of Obstacle Factors at the Index Level

4.3.1. Analysis of Obstacle Factors at the Index Level Based on the degree of obstacle model, the factors creating obstacles that affect ES in the studied border areas were determined (Figure 6; however, only the factors in 2004 and 2019 are listed). By analyzing the degree of obstacles for each factor [69], the five top obstacle factors were found to be the following: fixed asset investment (X5), per capita fiscal revenue (X6), per capita GDP (X2), food production (X27), and water regulation (X33). In 2004, the average obstacle degree of X5, X6, X2, X27, and X33 was 14.11%, 8.86%, 7.90%, 4.74%, and 4.21%, respectively. In 2019, the average obstacle degree of X5, X6, X2, X27, Based on the degree of obstacle model, the factors creating obstacles that affect ES in the studied border areas were determined (Figure 6; however, only the factors in 2004 and 2019 are listed). By analyzing the degree of obstacles for each factor [69], the five top obstacle factors were found to be the following: fixed asset investment (X5), per capita fiscal revenue (X6), per capita GDP (X2), food production (X27), and water regulation (X33). In 2004, the average obstacle degree of X5, X6, X2, X27, and X33 was 14.11%, 8.86%, 7.90%, 4.74%, and 4.21%, respectively. In 2019, the average obstacle degree of X5, X6, X2, X27, and X33 was 11.10%, 7.07%, 6.31%, 5.42%, and 4.73%, respectively.

#### and X33 was 11.10%, 7.07%, 6.31%, 5.42%, and 4.73%, respectively. 4.3.2. Analysis of Obstacle Factors at the Element Level

4.3.2. Analysis of Obstacle Factors at the Element Level Based on the calculations of the obstacle degree of each indicator, the obstacle degree of factors at the element level was also obtained (Figure 7). The obstacle degrees at the element level in border areas varied between 2004 and 2019. In 2004, the economic subsystem was the main obstacle to improving the ES in Jinghong, Lancang, and Mengla, while the ESV subsystem was the main obstacle to improving the ES in the other 22 border counties. In 2019, the obstacle degree of the economic subsystem increased in Langcang, Based on the calculations of the obstacle degree of each indicator, the obstacle degree of factors at the element level was also obtained (Figure 7). The obstacle degrees at the element level in border areas varied between 2004 and 2019. In 2004, the economic subsystem was the main obstacle to improving the ES in Jinghong, Lancang, and Mengla, while the ESV subsystem was the main obstacle to improving the ES in the other 22 border counties. In 2019, the obstacle degree of the economic subsystem increased in Langcang, while the obstacle degree of the economic subsystem of all the other 24 border counties decreased. The obstacle degrees of social, environmental, and landscape pattern subsystems changed little.

while the obstacle degree of the economic subsystem of all the other 24 border counties

#### decreased. The obstacle degrees of social, environmental, and landscape pattern subsys-*4.4. Prediction of Changes in ES for the Period 2025–2030*

tems changed little. According to the respective ES level of each border county from 2004 to 2019, predicted values for ES in 2025 and 2030 were obtained (Figure 8). The ES of border areas is predicted to maintain an upward trend. In 2025, Jinghong would first reach a secure state; Lancang, Mengla, and Tengchong would reach a good state; and Menghai, Ruili, Gengma and Funing would reach a sensitive state. Ximeng would maintain a critical state, and the other 16 counties would be in an unstable state. In 2030, Jinghong and Tengchong would reach a secure state; Lancang, Mengla, and Ruili would reach a good state; and Funing, Gengma, Hekou, Longling, Menghai, Yingjiang, and Zhenkang would reach a sensitive

state. Cangyuan, Fugong, and another 10 counties would be in an unstable state. However, Ximeng would remain in a critical state. *Land* **2022**, *11*, 892 14 of 21 *Land* **2022**, *11*, 892 14 of 21

**Figure 6.** The obstacle degree of each indicator of ecological security in 25 border counties in Yunnan Province, China in: (**a**) 2004 and (**b**) 2019 (%). **Figure 6.** The obstacle degree of each indicator of ecological security in 25 border counties in Yunnan Province, China in: (**a**) 2004 and (**b**) 2019 (%). **Figure 6.** The obstacle degree of each indicator of ecological security 25 border counties in Yunnan Province, China in: (**a**) 2004 and (**b**) 2019 (%).

**Figure 7.** The obstacle degree of each element of ecological security in 25 border counties in Yunnan Province, China for economic, social, environmental, landscape pattern, ecosystem service value (ESV). Note: 04 and 19 indicate the years of 2004 and 2019, respectively.


**Figure 7.** The obstacle degree of each element of ecological security in 25 border counties in Yunnan Province, China for economic, social, environmental, landscape pattern, ecosystem service value

According to the respective ES level of each border county from 2004 to 2019, predicted values for ES in 2025 and 2030 were obtained (Figure 8). The ES of border areas is predicted to maintain an upward trend. In 2025, Jinghong would first reach a secure state; Lancang, Mengla, and Tengchong would reach a good state; and Menghai, Ruili, Gengma and Funing would reach a sensitive state. Ximeng would maintain a critical state, and the other 16 counties would be in an unstable state. In 2030, Jinghong and Tengchong would reach a secure state; Lancang, Mengla, and Ruili would reach a good state; and Funing, Gengma, Hekou, Longling, Menghai, Yingjiang, and Zhenkang would reach a sensitive state. Cangyuan, Fugong, and another 10 counties would be in an unstable state. How-

(ESV). Note: 04 and 19 indicate the years of 2004 and 2019, respectively.

*4.4. Prediction of Changes in ES for the Period 2025–2030* 

ever, Ximeng would remain in a critical state.

**Figure 8.** Prediction of the ecological security in 25 border counties of Yunnan Province, China in 2025 and 2030. **Figure 8.** Prediction of the ecological security in 25 border counties of Yunnan Province, China in 2025 and 2030.

#### **5. Discussion**

#### **5. Discussion**  *5.1. Discussion of Change in ES*

*5.1. Discussion of Change in ES*  The main objective of this study was to analyze the ES of 25 counties with international borders in Yunnan Province, China. First, we selected these 25 counties as the case study area and constructed an index system to evaluate the ES from the five subsystems of economy, society, environment, landscape pattern, and ESV. We found that human and natural systems interact to affect the ES, which is consistent with previous studies [30]. Efforts related to ecological protection need to be coordinated with social and economic development, which can provide more comprehensive guidance for land management by environmental decision makers [70,71]. Second, we found that the ES of border areas showed a spatial distribution pattern of high in the southwest and low in the west and east. Jinghong had the highest level of ES, because Jinghong has adhered to the implementation of a project designed to return rubber plantation land to forest and has delimited an ecological red line, resulting in the ES of Jinghong ranking first among the border counties. Third, we found that from 2004 to 2019, the ES of border areas showed an overall upward trend, indicating that remarkable achievements have been made in ecological protection in Yunnan Province. However, on the premise of maintaining the existing development mode, we predicted the ES status of border areas in 2025 and 2030. We found that only one county will reach the classification of secure status in 2025 and two counties The main objective of this study was to analyze the ES of 25 counties with international borders in Yunnan Province, China. First, we selected these 25 counties as the case study area and constructed an index system to evaluate the ES from the five subsystems of economy, society, environment, landscape pattern, and ESV. We found that human and natural systems interact to affect the ES, which is consistent with previous studies [30]. Efforts related to ecological protection need to be coordinated with social and economic development, which can provide more comprehensive guidance for land management by environmental decision makers [70,71]. Second, we found that the ES of border areas showed a spatial distribution pattern of high in the southwest and low in the west and east. Jinghong had the highest level of ES, because Jinghong has adhered to the implementation of a project designed to return rubber plantation land to forest and has delimited an ecological red line, resulting in the ES of Jinghong ranking first among the border counties. Third, we found that from 2004 to 2019, the ES of border areas showed an overall upward trend, indicating that remarkable achievements have been made in ecological protection in Yunnan Province. However, on the premise of maintaining the existing development mode, we predicted the ES status of border areas in 2025 and 2030. We found that only one county will reach the classification of secure status in 2025 and two counties will reach the secure status in 2030, which is still a certain distance from the goal of establishing an ES barrier. Therefore, local governments should continue to pay more attention to the ES in border areas and formulate environmental policies according to the natural endowment and economic development of border areas.

#### *5.2. Discussion on Obstacle Factors*

In China, significant differences in socio-economic development and natural conditions among regions have caused the factors influencing ES in different regions to vary [12,17,22,26,72–75] (Figure 9). For the border areas in Yunnan Province, we found that the fixed asset investment, per capita fiscal revenue, per capital GDP, and food production are the main factors creating obstacles to improving the level of ES, which is consistent with previous research results [73]. Fixed asset investment plays a major role in promoting both the economy and the upgrading of industrial infrastructure, so as to reduce the pressure of industrial development on the environment. The higher the per-capita GDP and fiscal revenue, the more capable people are of protecting the environment. In addition, the border areas are located in the upstream regions of the Nujiang, Lancang, and other rivers.

This region serves as an important area for supplying and regulating water resources in Asia [10]. Therefore, the capacity to regulate water availability significantly affects the ES in border areas. In short, economic development and the provision of water-related ecosystem services are the main factors creating obstacles that affect the ES in border areas. sources in Asia [10]. Therefore, the capacity to regulate water availability significantly affects the ES in border areas. In short, economic development and the provision of waterrelated ecosystem services are the main factors creating obstacles that affect the ES in border areas.

will reach the secure status in 2030, which is still a certain distance from the goal of establishing an ES barrier. Therefore, local governments should continue to pay more attention to the ES in border areas and formulate environmental policies according to the natural

In China, significant differences in socio-economic development and natural conditions among regions have caused the factors influencing ES in different regions to vary [12,17,22,26,72–75] (Figure 9). For the border areas in Yunnan Province, we found that the fixed asset investment, per capita fiscal revenue, per capital GDP, and food production are the main factors creating obstacles to improving the level of ES, which is consistent with previous research results [73]. Fixed asset investment plays a major role in promoting both the economy and the upgrading of industrial infrastructure, so as to reduce the pressure of industrial development on the environment. The higher the per-capita GDP and fiscal revenue, the more capable people are of protecting the environment. In addition, the border areas are located in the upstream regions of the Nujiang, Lancang, and other rivers. This region serves as an important area for supplying and regulating water re-

*Land* **2022**, *11*, 892 16 of 21

endowment and economic development of border areas.

*5.2. Discussion on Obstacle Factors* 

**Figure 9.** Spatial distribution of influencing factors of ecological security in China. Eight regions are labeled as follows: (I) Border areas in Yunnan Province, (II) Yunnan Province [73], (III) Qinghai Province [22], (IV) the Pearl River Delta Urban agglomeration [12], (V) the Chaohu Lake Basin [17], (VI) the East-Liao River basin [26], (VII) Gansu Province [74], (VIII) the Beijing–Tianjin–Hebei Region [72]. Note: Map was created by authors using ArcGIS 10.7 based on the digital elevation model (DEM) data from the Resources and Environment Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 4 January 2022). **Figure 9.** Spatial distribution of influencing factors of ecological security in China. Eight regions are labeled as follows: (I) Border areas in Yunnan Province, (II) Yunnan Province [73], (III) Qinghai Province [22], (IV) the Pearl River Delta Urban agglomeration [12], (V) the Chaohu Lake Basin [17], (VI) the East-Liao River basin [26], (VII) Gansu Province [74], (VIII) the Beijing–Tianjin–Hebei Region [72]. Note: Map was created by authors using ArcGIS 10.7 based on the digital elevation model (DEM) data from the Resources and Environment Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 4 January 2022).

#### *5.3. Limitations and Implications*

This study has several limitations and can be improved in future research. First, biodiversity is an important embodiment of ES, especially in border areas of Yunnan Province, a global biodiversity hotspot. However, biodiversity data are difficult to obtain at the county scale. In the future work, the indicators for measuring biodiversity need to be further considered. Second, when predicting the ES of border areas, this study only considered the state that the border areas are predicted to reach in 2025 and 2030 under the situation of maintaining the existing development mode, but did not consider multiple scenarios, such as models of existing development and priorities in economic and environmental development, to simulate the future ES of border areas. Future research should make up for these shortcomings.

In the process of national development, as the border areas were far away from the political and economic center of the country in the past, previous studies often ignored the border areas [76]. With the acceleration in globalization, the border areas have changed from the original inferior states to the area with increasingly close international cooperation. The border areas of Yunnan Province, China, border on Myanmar, Laos, and Vietnam, and belong to the upstream area of many international rivers [35]. The situation in this area is relatively complicated. Selecting this area as the research case can provide reference for researchers to study the ES of border areas. This study finds that the Chinese government

has effectively improved the level of ES in the border areas by implementing a series of environmental protection policies [77]. Therefore, other countries can also achieve the purpose of protecting the ecosystem by continuously increasing their attention to the ES in the border areas. However, the national boundaries divided by administrative units are not necessarily the boundaries of natural ecosystems. Carrying out mutual cooperation among countries is also an important way to improve the ES of border areas. In terms of specific implementation, first, it is essential to pay attention to the ecological restoration and protection measures in the most valuable areas [78]. By distinguishing the differences in the level of ES in different regions of the study area, it helps the local government to choose the priority of policy implementation. Second, when implementing environmental protection policies, the local government should identify the obstacle factors affecting the ES in advance, so as to implement environmental policies pertinently. Third, as an important basis for a comprehensive national security system, the amount of research on the ES of border areas needs to be increased, so as to provide reference for local environmental decision making.

### **6. Conclusions**

Improving the ES of border areas is of great significance to local sustainable development [10]. However, few studies have addressed the status of ES in border areas, especially on the county scale [79]. Therefore, 25 border counties in Yunnan Province were selected as the study case. We used an entropy weight TOPSIS model to evaluate the ES conditions of border areas from 2004 to 2019 and used trend surface analysis to evaluate the spatial differentiation trends. Then, we diagnosed the factors creating obstacles that affect ES and used a GM (1,1) model to predict the state of ES in both 2025 and 2030. The results show the following patterns:

(1) From 2004 to 2019, the level of ES in all border counties showed a positive upward trend. Tengchong, Jinghong, and Ruili counties ranked among the top three in terms of the improved ES, with increases of 0.2294, 0.2084, and 0.1544, respectively. In terms of five subsystems (economy, society, environment, landscape pattern, and ecosystem service value), the 25 counties had obvious differences. The levels of the economic and social subsystems showed an overall upward trend; the levels of environmental and landscape pattern subsystems fluctuated continuously; and the overall change in the ESV subsystem was small.

(2) The ES of border areas presented a spatial distribution pattern of high in the southwest and low in the west and east. The level of ES in Lancang was the highest in 2004 and 2009 and that of Jinghong was the highest in 2014 and 2019. The trend of spatial change in ES in border areas generally presented the characteristics of remaining stable in the east–west direction and changing in the north–south direction.

(3) In terms of index level, fixed asset investment, fiscal revenue, per-capita GDP, food production, and water regulation are the top five obstacles to improving the ES in border areas. In terms of element level, the economic subsystem is the main factor creating an obstacle to improving the ES in Jinghong, Lancang, and Mengla, while the ESV subsystem is the main factor affecting improvement in the ES in the other 22 counties.

(4) The gray prediction model GM (1,1) can effectively predict the future situation of border areas in both 2025 and 2030. The level of ES in border areas is predicted to maintain an upward trend. In 2025, the ES in Jinghong will reach 0.84 and will be in a secure state. By 2030, the number of border counties with a secure state of ES will increase to two, namely Jinghong and Tengchong. Their ES level will reach 0.98 and 0.86, respectively.

Our research provides reliable information on the ES of 25 border counties in Yunnan and puts forward targeted policy suggestions based on the research results, which will be necessary if China desires to implement sustainable development planning and management at the smallest administrative scale. In addition, this study can provide a reference for other countries to improve the level of ES in border areas.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/land11060892/s1, Table S1: Equivalent factor per unit area of ecosystem services in border areas; Table S2: The value coefficient per unit area of ecosystem services in border areas (yuan/ha/yr); Table S3: The sensitivity index of the ESV coefficient.

**Author Contributions:** Conceptualization, Z.H., Q.C. and C.W.; Writing—original draft preparation, Z.H. and X.T.; Methodology, Z.H., Z.Z. and F.Z.; Software, Z.H.; Data curation, M.Q.; Validation, M.Q., F.Z. and Q.C.; Supervision, C.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Major Project of the National Social Science Fund of China (Grant No. 20&ZD095) and the Scientific Research Foundation of the Department of Education of Yunnan Province (Grant No. 2022Y079).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.
