Improving Traditional Metrics: A Hybrid Framework for Assessing the Ecological Carrying Capacity of Mountainous Regions

Abstract

Ecological carrying capacity (ECC) is a crucial indicator for assessing sustainable development capabilities. However, mountain ecosystems possess unique complexities due to their diverse topography, high biodiversity, and fragile ecological environments. Addressing the current shortcomings in mountain ECC assessments, this paper proposes a novel hybrid evaluation framework that integrates improved ecological footprint (EF) and ecosystem service value (ESV) approaches with spatial econometric models. This framework allows for a more comprehensive understanding of the dynamic changes and driving factors of the mountain ecological carrying capacity index (ECCI), using Pingbian County as a case study. The results indicate the following: (1) Land use changes and biodiversity exert varying impacts on the ECCI across different regions. The ECCI decreased by 42% from 2003 to 2021 (from 4.41 to 2.54), exhibiting significant spatial autocorrelation and heterogeneity. (2) The ecological service value coefficient is the main factor increasing the ECCI, while the energy consumption value and per capita consumption value inhibited the increase in the ECCI. For every 1% increase in the ecosystem service value coefficient, the ECCI increased by 0.66%, whereas every 1% increase in energy consumption value and per capita consumption value reduced the ECCI by 0.18% and 0.28%, respectively. (3) The overall spatial distribution pattern of the ECCI is primarily “southwest to northeast”, with the distance of centroid migration expanding over time. Based on these key findings, implementing differentiated land use practices and ecological restoration measures can effectively enhance the mountain ECCI, providing scientific support for the sustainable management of mountain areas.

1. Introduction

Ecological carrying capacity (ECC) is a vital indicator for assessing ecosystem stability and is crucial for understanding and managing the sustainable development of mountainous ecosystems [1]. Mountainous regions exhibit unique ecological characteristics and biodiversity, providing essential ecosystem services for the health of local communities and the broader environment [2]. However, these regions are increasingly threatened by human activities such as deforestation [3], urbanization [4], and climate change [5,6], which can significantly impair their ecological functions and services, lead to land degradation [7], and cause a decline in biodiversity [8], raising concerns about their long-term sustainability. Consequently, assessing and enhancing the ecological carrying capacity of mountainous ecosystems has become a core task for environmental scientists and policymakers.

Concepts such as ecological footprint (EF) [9], service value [10], and ecological carrying capacity are crucial for understanding the interactions between human activities and natural systems [11]. EF represents the human demand on nature, quantifying the area of biologically productive land and water required to produce the resources consumed, and assimilate the waste generated by a specific population [12]. Traditional ecological footprint (EF) methodologies, including frameworks developed by the Global Footprint Network (GFN) and the European Environment Agency (EEA), have been widely adopted to evaluate the sustainability of human activities across global and regional scales. With advancing research, EF assessments have progressively diversified into specialized subfields. For instance, the “carbon footprint”, centered on carbon dioxide emissions [13], has emerged as a prominent environmental indicator and is widely applied in the evaluation of nations, cities, and industries [14] under the context of climate change [15,16]. In recent years, the concept of ecosystem service value (ESV) has gained attention as a complementary tool to EF analysis. ESV assesses the benefits humans derive from ecosystem functions, such as carbon sequestration, water purification [17], and soil retention. By integrating ESV with EF, a more comprehensive evaluation of ecological carrying capacity can be achieved, providing deeper insights into the sustainability of mountainous ecosystems. This integrated approach allows for a detailed understanding of how ecological functions and human activities interact, informing more effective and targeted management strategies.

The ecological carrying capacity of a region refers to the maximum level of human activity that can be sustained without causing long-term degradation of the ecosystem’s ability to provide these services [11]. Enhancing ecological carrying capacity not only means improving the supply capacity and stability of ecosystems but is also key to promoting the harmonious coexistence of ecological, economic, and social factors [18,19]. Over time, the scope of ECC has expanded to include the ecosystem’s ability to sustain human existence and self-regulate resources [20,21]. From a supply–demand perspective, given that the ecological footprint is considered the human demand for ecological assets, ECC can be defined as the ecological assets provided by natural ecosystems to human society [22]. The ecological footprint model uses fixed equivalence and yield factors, employing “virtual area” to represent carrying capacity, but potentially overlooks intra-regional differences [23,24]. To address this issue, Wang Hengbo combined the theories of ecological footprint and service value, enhancing their accuracy [25]. However, due to their unique topographical features, mountainous ecosystems exhibit significant heterogeneity and multilayered characteristics, with complex components, diverse structures, and intricate ecological processes [26]. Therefore, improving the assessment of ecological carrying capacity in mountainous regions, based on changes in biodiversity, using enhanced ecological footprint and service value methods, presents a valuable breakthrough and entry point.

Hybrid frameworks have emerged as innovative tools to address the complexity of ecological systems by integrating multiple methodologies to provide comprehensive evaluations. Unlike conventional frameworks, our hybrid evaluation framework combines improved EF and ESV methodologies with spatial econometric models. This integration allows for a nuanced understanding of ecological carrying capacity by incorporating biodiversity (represented by habitat quality) dynamics and spatial heterogeneity. For Pingbian County, which possesses high biodiversity and a typical mountainous ecosystem, this enhancement has improved the accuracy and applicability of the ecological carrying capacity index (ECCI) calculation. It offers more precise management strategies to address the ecological, social, and economic challenges faced by Pingbian County in its pursuit of sustainable development. Meanwhile, organizations such as the Asian International Rivers Center (AIRC) and the World Wildlife Fund (WWF) have analyzed the ecosystem supply and services in Pingbian County using the Millennium Ecosystem Assessment framework in 2003 [27]. Building on this foundation, this paper explores the spatiotemporal changes in the ecological environment of Pingbian County from the perspective of geospatial patterns, aiming to continuously monitor and study the dynamic evolution of the county’s ecological environment and its sustainable development status.

This study aims to address the following research questions: (1) What are the spatiotemporal characteristics of the ecological carrying capacity index (ECCI) in Pingbian County from 2003 to 2021? And (2) what are the main driving factors affecting the ECCI. Meanwhile, in order to comprehensively reflect the diverse ecological and socio-economic conditions of the county, this study selected 98 villages as samples to ensure that the study can capture the spatial heterogeneity and ecological dynamics of the entire county. The objectives of this study are as follows: (1) To utilize an improved evaluation framework to analyze the spatiotemporal characteristics of the ECCI in the mountainous region of Pingbian from 2003 to 2021, using 98 villages as samples; (2) To explore the spatial effects and main driving factors of the ECCI; And (3) to provide sustainable management strategies for the reasonable planning of industrial layout, biodiversity conservation, and the support of local community well-being in mountainous regions.

2. Materials and Methods

2.1. Study Area

Pingbian Miao Autonomous County, colloquially known as Pingbian County, resides in the northern reaches of the Ailao Mountains, bordering Mengzhi city. Its topography ranges from a peak height of 2557 m to a low of 137 m, as depicted in Figure 1. This area serves as a quintessential mountainous ecosystem and holds a pivotal position in the ecological security network of the southwestern region.

2.2. Research Methods

In this paper, we established a hybrid analysis framework for mountainous regions to assess this capacity in Pingbian County based on the ecological footprint and service value method (Figure 2). Initially, we delved into the spatiotemporal variations in the ECCI of Pingbian County to unveil its overall characteristics. Subsequently, we calculated the global Moran’s I index to detect any spatial autocorrelation within the county’s ECCI. We then generated a LISA (Local Indicators of Spatial Association) map to highlight the spatial clustering patterns among 98 villages. Furthermore, by employing a spatial econometric model (SEM), we investigated the spatial impacts and underlying factors driving the ECCI. Additionally, we analyzed the trajectory of the center of gravity for the ECCI by using the standard deviation ellipse to identify its dominant direction. Finally, based on our insights, we propose targeted measures to bolster the ECCI in Pingbian County.

2.2.1. Ecological Carrying Capacity Model

This paper references the ecological carrying capacity model proposed by Wang Hengbo, which is a groundbreaking method for quantifying ECC through the ratio of ecosystem service value to ecological footprint value [25]. This model innovatively integrates ecological footprint and ecosystem service value theories by substituting “virtual area” in the evaluation with more universally applicable value metrics and characterizing ECC with ecological carrying capacity index (ECCI). This index represents the multiple relationship between supply and demand by the ratio of ecological service value to ecological footprint value, thereby providing a more intuitive reflection of the overall state of the regional ecological environment.

In this study, we use equivalence factors and land use type to calculate the value of services (see Supplementary Information (SI), Table S3), and biological and energy accounts to calculate the value of footprints, ensuring data consistency across periods by converting them to 2021 prices (see Supplementary Information (SI), part 2). The following formulas govern the computation of the ecological footprint and ecosystem service values:

$${EF}_{it}=E{B}_{it}+E{CV}_{it}$$

(1)

$$E{SV}_{it}=D_t\cdot \sum F_{ijt}\cdot S_{ijt}$$

(2)

where EF_it represents the ecological footprint value, EB_it denotes the biological resource consumption value, ECV_it is the energy consumption value, ESV_it indicates the ecosystem service value, D_t denotes the standard equivalence factor value (see SI, part 2), F_ijt represents the ecological service value equivalent factor, and S_ijt is the land area.

In mountain ecosystems, due to their unique geographical and climatic conditions, a rich diversity of ecosystem types is formed. The loss of ecosystem diversity can disrupt the structure and function of ecosystems, and reduce their stability and carrying capacity. Moreover, traditional ecological footprint models also focus on biodiversity [28]. Therefore, in calculating the ECCI for this mountainous region, this paper places particular emphasis on incorporating changes in biodiversity, using habitat quality as a representative coefficient for biodiversity change, in order to ensure the accuracy of the assessment results (Figure 3). The following improved ecological carrying capacity model has been established:

$$ECC{I}_{it}={\displaystyle \frac{\eta E{SV}_{it}}{{EF}_{it}}}$$

(3)

The ECCI_it is the ecological carrying capacity index and η denotes the biodiversity change coefficient, calculated by the InVEST model. A higher ECCI indicates better ecological environmental quality and stronger sustainable development capabilities, and vice versa. Since the ECCI to some extent also reflects the supply–demand relationship within a region, we can preliminarily assess this relationship based on the calculation results of the ECCI [29]. Specifically, an ECCI < 1 indicates an insufficient supply of ecosystem services in the region. The dynamic changes in the primary variables are detailed in Table S4 in SI.

2.2.2. Ecological Services Assessment

The InVEST model is a spatially visualized method and tool used to assess ecological services [30]. This paper selected the habitat quality (HQ) module, the carbon storage (CS) module and the water yield (WY) module of the InVEST model to evaluate biodiversity, carbon storage, and water yield in Pingbian (see SI, part 3).

2.2.3. Moran’s I Analysis

The global Moran’s I measures spatial autocorrelation among data, while the local Moran’s I identifies clustering or outliers [31]. The stronger the spatial dependency and the tighter the relationship between regions are, the closer the absolute value of Moran’s I is to 1. A value of 0 indicates no significant spatial autocorrelation with neighboring areas [32]. This study conducted a global Moran’s I calculation and local spatial autocorrelation test on the ECCI of 98 villages in Pingbian County (see SI, part 4) and produced local spatial autocorrelation LISA maps.

2.2.4. Spatial Econometric Model

To investigate the extent of the influence of relevant factors on the ECCI, regression analysis can be conducted. However, traditional models fail to capture spatial autocorrelation. Hence, this study employed spatial econometric models such as the spatial lag model (SLM), spatial error model (SEM), and spatial Durbin model (SDM) [33] to analyze their relationships [34]. The model is set as

$$E{CCI}_{it}=\rho WY+X\beta +\theta WX+\epsilon$$

(4)

$$\lambda \epsilon =W\epsilon +\mu$$

(5)

where Y and X represent the dependent variable and independent variables, W denotes the spatial weight matrix, and $\rho$ stands for the spatial lag coefficient, comprising various explanatory and control variables. β signifies the coefficients of the independent variables, and θ represents the spatial Durbin coefficient. ε is the error term, indicating the unexplained portion of the model, λ denotes the spatial error coefficient, and μ represents the random disturbance term.

To minimize potential biases arising from omitted variables and enhance the scientific rigor and accuracy of the model, this study selected influencing factors based on the calculation characteristics of the ecological carrying capacity model, the primary service types of mountainous ecosystems, and relevant research conducted by scholars [25,35]. The core variables included the ecosystem service value coefficient, energy consumption value, and per capita consumption value. Control variables included environmental variables reflecting the mountainous ecosystem, such as forest area, carbon storage, and water yield. Finally, a collinearity test was conducted on the above variables using Stata 18 software to extract key variables [36]: the ecological service value coefficient (ESVC), water yield (WY), energy consumption value (ECV), and per capita consumption value (PCV).

2.2.5. Standard Deviation Ellipse

The standard deviation ellipse (SDE) is a spatial statistical method proposed by American sociologist Welty Lefever and others to reveal the spatial patterns of social, economic, and natural environmental elements [37]. Unlike traditional clustering analysis methods, SDE intuitively illustrates the directional deviations of research elements in spatial distribution. In our research, we employed the utilization of standard deviation ellipses, specifically for the years 2003, 2013, and 2021, to precisely illustrate the distinguishing features of the ECCI and its interconnected variables within Pingbian County.

2.3. Data Requirements and Preparation

This study primarily utilizes land use data, biological resources accounts, energy accounts, and climate data. Land use data were obtained from the PIE Cloud services (https://engine.piesat.cn/engine-studio/dataset) (accessed on 5 October 2023). Biological and energy accounts were obtained from the Statistical Information Manual and the work report of Pingbian County (To ensure price comparability, the prices of all agricultural, forestry, livestock, fishery, and energy products are based on the prices from 2021). The climatic data, encompassing annual average precipitation and reference evapotranspiration, were procured from the National Meteorological Information Center. For detailed information on these variables, please refer to Table S1 in the Supplementary Information (SI). All the data were projected to WGS_1984 Albers.

3. Results

3.1. Land Use and Biodiversity Change

From 2003 to 2021, the cultivated land area decreased by 67.96 km², and the forest area increased by 41.74 km². In 2021, the forest area was 1434.90 km², comprising 78% of the total territory (see Table S2 in the SI). From 2003 to 2013, a notable decrease in cultivated land occurred in the northern section of Pingbian County, with a corresponding significant increase in forest coverage. Conversely, in the southern part, this trend was reversed (Figure 4). The cultivated land in Heping and Baiyun towns were concentrated in 2003 and distributed sporadically in 2021, with a decrease in cultivated land area and a continuous increase in forest area. However, in Baihe Town, an opposing trend was observed. This phenomenon may be attributed to the varying impacts of the Grain for Green Program and economically driven agricultural expansion across different regions.

From the perspective of biodiversity, the average biodiversity value was 0.78 in 2003, 0.81 in 2013, and 0.81 in 2021, showing a relatively stable trend overall. In terms of spatial distribution, biodiversity increased in the northern region while it decreased in the southern region, with a particularly noticeable decline in Baihe Town. This indicates that the changes in land use and biodiversity in mountain ecosystems are significant both temporally and spatially. These findings underscore the need for targeted conservation strategies that account for regional differences in land use and biodiversity dynamics.

3.2. Spatiotemporal Variation Characteristics in the ECCI

The spatiotemporal variation in the ECCI in Pingbian County from 2003 to 2021 is shown in the figure (Figure 5). The average index of the county decreased from 4.41 to 2.54 (see Table S4 in SI). The different townships exhibited diverse patterns: Baihe, Yuping, and Wantang gradually decreased from 4.56, 4.69, and 4.47 in 2003 to 2.82, 2.21, and 2.94 in 2013, respectively, and then further decreased to 1.69, 1.72, and 2.65 in 2021 (see Table S4 in the SI). On the other hand, Heping, Baiyun, Xinxian, and Xinhua displayed a downward trend from 2003 to 2013, followed by a rebound after 2021, but failed to regain the high levels observed in 2003.

At the village level in Pingbian County, the ECCI ranged from 0.89 to 6.22 between 2003 and 2021 (Figure 5). In 2003, villages with low indices (1.50–3.00) clustered in Heping Town’s southeastern region. By 2013, this trend expanded to Yuping, Baihe, Wantang, and Xinhua, showing spatial clustering. Temporally, most village indices decreased from 2003 to 2013; the indices of some villages in Heping and Xinxian towns were exceptions. In 2021, Pingbian’s northern region experienced an increase in the index, while its southeastern region experienced a decrease. Notably, the lowest-index village (0.89) appeared in Baihe Town in 2021, indicating that the ecosystem service value of this village could not meet the consumption demand. By conducting assessments at village scales, it is possible to gain a more comprehensive understanding of the spatiotemporal characteristics of ecological carrying capacity indices and the complexities within the region. These findings underscore the need for spatially targeted management strategies that address the unique ecological and socioeconomic conditions of different regions.

3.3. Spatial Autocorrelation Analysis

The ECCI of 98 villages in Pingbian County displayed spatial autocorrelation (Figure 6). The magnitude of the variations observed in the global Moran’s I index values across the three specified periods exhibited a substantial and gradual upward trend, indicating a progression towards a more pronounced spatial autocorrelation. Moran scatter plots revealed clustering of high (HH) and low (LL) index values, with clustering intensity increasing yearly. In 2021, the percentage of HH quadrant villages increased by 27%, while that of LL quadrant villages remained stable. Since 2013, most villages in the northern and western townships have been in the HH quadrant, while most villages in the southern part have been in the LL quadrant. The increasing spatial autocorrelation of the ECCI highlights the influence of socioeconomic activities and policy interventions on ecological carrying capacity.

The LISA map (Figure 6) distinctly reveals a prominent clustering pattern in the ECCI. Initially, in 2003, the clusters were primarily HH and LL, with a few HL clusters in Yuping Town. Over time, the HH clustering areas fluctuated in the western and northern regions. The LL clustering areas expanded southwards, covering Yuping and Baihe towns with increasing significance. These findings underscore the need for spatially targeted management strategies that address the unique ecological and socioeconomic conditions of different regions.

3.4. Spatial Effects and Driver Analysis of ECCI

The results of Moran’s I for the ECCI panel data of Pingbian County indicate that the use of a spatial econometric model is appropriate (global Moran’s I = 0.77 and a significant result at the 1% level) (detailed panel data are shown in

Table 1. Descriptive statistics of key influencing variables.

Variable Category	Variable	Code	Description Category	Time
				2003	2013	2021
Explained variable	Ecological carrying capacity index (village level)	ECCI	Mean	4.41	2.98	2.99
			S.D.	0.76	0.79	1.34
Ecological service value variable	Ecosystem service value coefficient (104 CNY/ha)	ESVC	Mean	2.53	2.69	2.71
			S.D.	0.39	0.33	0.37
	Water yield (mm)	WY	Mean	422.73	167.9155	119.48
			S.D.	92.18	47.4033	69.31
Ecological footprint value variables	Energy consumption value (106 CNY)	ECV	Mean	1.67	4.0304	4.65
			S.D.	1.73	4.2078	4.44
	Per capita consumption value (103 CNY per person)	PCV	Mean	6.02	9.2370	10.47
			S.D.	2.75	5.06	8.15

). The panel data selected are detailed in Table 1. LM tests were conducted on the variable data for the three periods mentioned above, and the SEM exhibited stronger significance (both the Lagrange multiplier and robust Lagrange multiplier achieving significance at the 1% level). Consequently, in this study, the spatial error model was ultimately adopted to describe spatial effects [38,39].

The spatial econometric model used in this study is designed to account for spatial dependencies. The results from the three spatial econometric models (

Table 2. Statistics of spatial associations between ECCI and key variables.

Variables	OLS	SEM	SLM	SDM
ESVC	0.7020 ***	0.6636 ***	0.3456 ***	0.5870 ***
ECV	−0.4267 ***	−0.1843 ***	−0.1651 ***	−0.1040 **
PCV	−0.2428 ***	−0.2829 ***	−0.1750 ***	−0.1630 ***
WY	0.3325 ***	0.0409	0.0216	0.0103
R-sq	0.5142	0.3885	0.6883	0.2593
observation	294	294	294	294

** p < 0.01, *** p < 0.001.

, all variables were standardized) indicated that ecosystem service value had a significant positive impact on the ECCI and promoted growth. Conversely, the energy consumption value and per capita consumption value had negative effects, inhibiting the index’s increase. According to the OLS regression, water production positively influenced this index, but the spatial models did not exhibit the same effect. The SEM lambda showed a significant positive spatial effect on the index, indicating the spatial correlation between neighboring areas. In terms of the main effects of the SEM, a 1% rise in the ecosystem service value coefficient translated to a 0.66% augmentation in the ECCI. Conversely, a 1% increment in energy consumption and per capita consumption resulted in respective declines of 0.18% and 0.28% in the ECCI. These findings highlight the comprehensive impact of driving factors on ecological carrying capacity.

3.5. Standard Deviation Ellipse Analysis

The trajectory of the center of gravity movement of the ECCI ellipse of Pingbian County shifted significantly from 2003 to 2021. Initially, the flatness of the standard deviation ellipse for the index was only 1.04, with no clear directionality. However, from 2013 onwards, a clear “southwest–northeast” overall spatial distribution pattern emerged. The center of gravity of the ECCI ellipse moved northwards, with an expanding distance trend and a shrinking radiation range (Figure 7).

Based on the SEM results, standard deviation ellipse analysis was conducted on the three driving factors—ecosystem service value coefficient, regional energy consumption value, and per capita consumption value—that significantly influence the ECCI. The center of gravity movement of the first two factors showed very small variations and could be ignored. However, per capita consumption exhibited a “southeast-northwest” overall spatial distribution pattern, with the center of gravity moving southwards, which was opposite to the ECCI ellipse (Figure 7). These findings suggest that future land use and socioeconomic changes, particularly in the southern region, could further impact the spatial distribution of the ECCI.

3.6. Model Validation

Due to the lack of empirical data for evaluating the ecological carrying capacity (ECC) of Pingbian County, a comparison was made with another region (the northern Shaanxi area). The average (ECCI) in this study (2003–2013) was 3.69, compared to 1.96 for the northern Shaanxi area (2005–2015) [25], This difference may be attributed to the inclusion of biodiversity parameters in the hybrid framework employed in this study. Additionally, the higher forest coverage in Pingbian County relative to the northern Shaanxi likely contributed to elevated ecosystem service value coefficients and biodiversity indices. As ECCI reflects the response of regional environmental quality to human activities [40], this study conducted a spatial comparison with existing environmental quality data (data sources detailed in SI Table S1). An R² value exceeding 0.9 (Figure 8) indicates spatial consistency between ECCI and environmental quality distributions. However, certain deviations were observed, likely due to differences in the parameters used by the hybrid framework evaluation model and the environmental quality evaluation model. These findings tested the model’s sensitivity to varying data inputs and changes in environmental quality.

Additionally, the primary driving factors were validated as follows: the sources of ecosystem service value and GDP data are detailed in Table S1 of the SI. WY was validated using data reported by [41]. The results demonstrate high R² values for the ecosystem service value and WY (exceeding 0.9) and approximately 0.7 for biological and energy consumption values (as shown in Figure 8). These findings underscore the scientific validity and theoretical foundation of the ecological carrying capacity model applied in this study.

4. Discussion

4.1. The Impact of Land Use Change and Biodiversity on ECCI

We observed that over the 18-year period, despite the shift in land use change towards more environmentally friendly practices (increased forest area), the average ECCI in Pingbian County actually declined by approximately 42%. This anomaly may be due to the fact that the positive outcomes of the “Grain for Green” policy (such as the increase in forest area) have not fully offset the environmental pressures brought about by economic development. This finding is consistent with studies by [42,43], which indicate that ecosystem services in Pingbian County continue to face increasing pressures. Furthermore, in the northern part of the county, the forest coverage has increased due to the reduction in cultivated land, thus, enhancing the ecosystem service value in the north, while the southern areas exhibit the opposite trend. Notably, the ECCI shows a local increase in the north and a general decline in the south, a trend closely aligned with changes in forest area and ecosystem service values. This further suggests that land use change directly affects the value of regional ecosystem services [44], which in turn affects the ECCI. In some areas, such as Heping Town and Baiyun Town, cultivated land fragmentation accompanied by the expansion of forested areas suggests a shift towards more sustainable land use practices [45]. This positively impacts ecological carrying capacity by enhancing ecosystem service value, aligning with previous research emphasizing the importance of land use patterns in determining ecological carrying capacity [46].

In terms of biodiversity, although there is an overall stable trend, significant spatial differences exist, further emphasizing the complexity of ecological carrying capacity dynamics [19]. Biodiversity in the northern region has increased, while in the southern region, particularly in Baihe Town, biodiversity has significantly declined. This indicates that local ecological conditions respond differently to land use changes and other potential anthropogenic pressures. Biodiversity changes are closely related to land use change [47], with increased forest cover often accompanying enhanced biodiversity. This aligns with the findings of Chazdon et al., who argue that forests protect biodiversity and contribute to its stability [48]. These findings suggest that targeted ecological management strategies should be developed based on the ecological characteristics and land use patterns of different regions to achieve sustainable management of regional ecosystems [49]. By incorporating biodiversity indicators and spatial analysis, the framework better captures the resource characteristics of mountainous areas, thereby identifying localized ecological pressures. For instance, integrating spatial autocorrelation and heterogeneity into the analysis highlights the varying impacts of land use and biodiversity changes across regions, offering targeted insights for sustainable management. This hybrid approach not only enhances the accuracy of ECCI assessments but also establishes a replicable model for other complex ecosystems, contributing to broader discussions on ecological sustainability.

4.2. Spatiotemporal Dynamics of the ECCI and Its Driving Factors

The spatiotemporal analysis of the ECCI reveals a declining trend during the study period, with significant differences among various towns, which is consistent with the research results of Wang on spatial distribution characteristics of ecological carrying capacity in Shaanxi [25]. Notably, the ecological indices of Baihe Town, Yuping Town, and Wantang Town have decreased, indicating deteriorating ecological conditions, likely due to increased anthropogenic pressures such as energy consumption and per capita consumption. This finding is consistent with previous research emphasizing the negative impact of human activities on ecological carrying capacity [50].

In terms of clustering, we also found that spatial correlation and heterogeneity coexist in the ECCI of Pingbian County. There are significant differences in ECCI changes among different towns or villages, yet there is a phenomenon of local clustering. The number of villages in the HH quadrant has increased in the northern region, possibly due to afforestation policies (such as the conversion of large areas of sloped farmland in Heping Town into artificial economic forests). These policies have promoted the continuity of green spaces, vegetation diversity, and forest cover, thereby enhancing the ECCI [51]. Notably, villages in Heping Town have shifted from the LL cluster to the HH cluster, emphasizing the positive impact of these policies. In contrast, most villages in the southern region belong to the LL quadrant, and the LL cluster area is expanding. This could be due to increased consumption of ecosystem services, such as the continuous expansion of banana and lychee cultivation and new supplementary farmland policies [52,53]. These spatial clustering phenomena reflect the spatial imbalance in the value of ecosystem services and consumption demands within the region.

The results of the spatial econometric model indicate that the value of ecosystem services, energy consumption, and per capita consumption are the main driving factors affecting the ECCI. The value of ecosystem services has a significant positive impact on the ECCI, while energy consumption and per capita consumption have negative impacts. In the SEM, the coefficients of these three driving factors exhibit significant differences, which may indicate that the spatial distribution and changes in these influencing factors are affected by neighboring areas and geographical distance [54,55]. These results emphasize the importance of optimizing resource utilization and enhancing the value of ecosystem services. Additionally, the spatial distribution of per capita consumption is inversely related to the elliptical distribution of the ECCI, indicating that the spatial distribution of consumption demand significantly affects ecological carrying capacity. For instance, from 2013 to 2021, the ECCI and per capita consumption became increasingly concentrated in a general direction with a reduced radiation range, indicating increased spatial clustering [56]. This phenomenon may be related to the implementation of targeted regional development policies in Pingbian County, leading to the concentration of resources, capital, and population in a specific area [57].

4.3. Strategic Management Measures to Address Ecological Carrying Capacity

Based on the findings of this study, we recommend that local governments use the ECCI as a tool for land use planning and decision-making. The implementation of differentiated ecological policies, the optimization of resource allocation, and other measures can effectively enhance the ECCI of Pingbian County, which has significant mountainous characteristics. The specific suggestions are as follows:

(1) Implement differentiated ecological policies: Given the differences in the ECCI and spatial clustering characteristics among the towns of Pingbian County, targeted strategies are recommended. For the northern region, continue to promote and strengthen afforestation and ecological restoration projects in areas with high elevation and high population density (e.g., Heping Town) to further increase forest cover and biodiversity, in order to maintain the stability of the ECCI. For the southern region, it is essential to optimize agricultural land use, integrate trees into agricultural landscapes to enhance biodiversity, and improve soil health. These measures may reduce the risk of erosion caused by extreme weather, particularly in areas experiencing ECCI decline, such as Baihe Town.

(2) Reduce energy consumption and environmental pressure: Promote the optimization and upgrading of the economic structure, especially in areas with rapidly growing energy consumption, by implementing clean energy technologies to reduce dependence on traditional energy sources. Encourage the development of low-carbon and environmentally friendly industries, particularly in villages with high forest cover and scenic beauty in the southern region. Vigorously develop ethnic ecological tourism and ethnic cultural industries to minimize reliance on and consumption of natural resources in the area.

(3) Enhance ecosystem service value: Across the entire county, increase the service value of forests and other ecosystems through policy support and incentive measures, encouraging sustainable ecosystem management [58]. Establish ‘Payment for Environmental Services’ schemes to compensate farmers and landowners for implementing conservation practices that enhance ecosystem services.

Regional collaboration in key areas: Based on the actual ECCI conditions of different regions, establish ecological cooperation and compensation mechanisms between various towns to promote ecological equity among regions. For instance, promote successful ecological restoration and management experiences from the northern region to ensure effective implementation of these measures in the southern region.

4.4. Innovations and Limitations

This study introduces several innovations: (1) It comprehensively considers multiple aspects including ecological, economic, and social factors, incorporates differences in biodiversity changes, and uses improved methods of ecological footprint and service value to address the limitations of traditional ECC analysis in mountainous areas, thereby establishing a more comprehensive assessment framework. (2) Through spatiotemporal dynamic analysis, it reveals the changing characteristics and driving factors of ecological carrying capacity in Pingbian County across different periods, towns, and village levels. This analytical method aids in the in-depth understanding of the dynamic changes and influencing factors of ecological carrying capacity. (3) The study employs a spatial econometric model to quantitatively analyze the influencing factors of ecological carrying capacity. The application of this model provides a more precise and scientific tool for the management of ecological carrying capacity.

Assessing ECCI typically requires a more comprehensive consideration of various ecosystem-related factors, which warrants further exploration in future research. The ecological footprint data for the Daweishan Nature Reserve in this study were interpolated based on data from Yuping Town, which may lead to an overestimation of the ecological carrying capacity index (ECCI) in this region; therefore, future assessments should focus on conducting dedicated surveys within the reserve itself. Additionally, this study is limited to Pingbian County, and the findings may have regional constraints. The considerable spatiotemporal variability of land uses and management practices presents challenges for generalizing the findings. Future research could extend to other regions or broader disciplines to validate the generalizability and applicability of the conclusions presented in this study.

5. Conclusions

This study employs the ecological footprint and service value method to investigate the spatiotemporal changes and driving factors of the ECCI in the typical mountain ecosystem of Pingbian County. The following conclusions were drawn: (1) Land use changes and biodiversity have a significant impact on ecological carrying capacity; there are spatiotemporal dynamic characteristics of ecological carrying capacity across different towns and villages; over the past 18 years, the ECCI of Pingbian County has significantly declined, indicating that the environmental pressures brought about by economic development have negatively impacted the ecosystem. The decrease in cultivated land and increase in forest area in the northern region have improved ecosystem service value, while the southern region has experienced ecological degradation due to agricultural expansion and urbanization. (2) There are significant differences in the ECCI across different towns, with spatial correlation and heterogeneity observed throughout the county. Although the northern region has improved its ecological carrying capacity through afforestation and ecological restoration projects, the southern region has experienced a decline in ecological carrying capacity due to cultivated land fragmentation and agricultural expansion. (3) The ECCI is directly influenced by driving factors such as ecosystem service value, energy consumption value, and per capita consumption value. The center of gravity has shifted northward, indicating a relationship with economic development and regional policies.

In response to these findings, we propose targeted management strategies, including the implementation of differentiated ecological policies, a reduction in energy consumption and environmental pressure, and the enhancement of ecosystem service value. These actions can strengthen the ECCI of Pingbian County and achieve sustainable development goals. Additionally, we recognize that although Pingbian County is located in the southwestern mountainous region, with unique forest resources and relatively lagging industrial development, its ecosystem services face increasing pressure. This is not an isolated case but a serious ecological challenge common to many mountainous agricultural counties, requiring our utmost attention and response.