Land Use Optimization from the Perspective of Multiple Stakeholder Groups: A Case Study in Yongsheng County, Yunnan Province, China

Abstract

With China’s rapid economic development in recent years, enhancing the sense of well-being among citizens has become a critical objective. However, the interests of various stakeholder groups are often overlooked in decision-making surrounding land use. In this study, Yongsheng County, Yunnan Province serves as a case study for land use scenario simulations. The equivalent factor method is combined with Participatory Rural Appraisal (PRA) to investigate the relationship between ecosystem multifunctionality (EMF) and the equity index of multiple stakeholder groups in various land use scenarios. We also explore whether an optimal combination of land use types exists. The results indicate that (1) The current ecosystem service value in Yongsheng County is primarily driven by climate regulation and biodiversity conservation, with a relatively high functional value index but a comparatively low equity index; (2) Different stakeholder groups mainly prioritize food production and ecosystem services impacting food production, such as water resource provision and climate regulation; (3) A land use allocation pattern of 20% farmland, 4% water bodies, 21% mixed forest, 20% coniferous forest, and 35% grassland appears to provide the optimal EMF index while simultaneously achieving the optimal equity index across stakeholder groups. This research may offer valuable insights for optimizing land use planning while taking into account the well-being of diverse stakeholder groups. It also may have practical implications for the formulation of innovative land use management strategies.

1. Introduction

Ecosystem services are closely linked to human well-being [1], and land use is a primary determinant of the availability and efficacy of these services [2]. As the global demand for ecosystem goods and services continues to intensify, land resource scarcity has become increasingly prominent and land use patterns have shifted significantly. The value of ecosystem services differs across groups of varying needs and interests, so the scarcity of land resources and drastic changes in land use patterns are likely to trigger conflicts among various stakeholders [3].

In the context of ecosystem services research, “stakeholder groups” are defined as individuals or collectives of people that can influence or be influenced by ecosystem services [4]. Local residents who rely on resource extraction for their livelihoods, urban consumers, or local policymakers can all be defined as stakeholder groups [5], as they either manage ecosystems, benefit from ecosystems, or both [6,7]. The demands of different stakeholder groups for specific land use patterns and ecosystem services depend on their roles and interests within the ecosystem. If the interests of certain groups cannot be reconciled during the land use decision-making process, conflicts among groups become inevitable; these conflicts can indeed adversely impact the utilization of ecosystem services as well as the sustainability of economic and social development. Therefore, it is necessary to consider equity among different stakeholder groups in land use decisions [8]. However, this factor is rarely taken into account in assessments of ecosystem services in China.

Equity across multiple stakeholder groups is crucial for sustainable land resource development and should be a key consideration in formulating land use management strategies [9]. In ecosystem services research, equity is generally examined in two dimensions: procedural equity and distributive equity [10,11]. Procedural equity ensures that all groups have equal opportunities to participate in land use decisions, while distributive equity emphasizes the fair distribution of benefits based on individual needs, acknowledging the unique circumstances of certain groups [10]. Studies have shown that equity influences the willingness of different stakeholder groups to participate in land resource utilization and governance [12,13], through which they may benefit from subsequent land use transitions and support their livelihoods [14]. This willingness and resulting engagement create a positive feedback loop that reinforces and perpetuates participation.

The ecological environment of Yunnan Province’s mountainous canyon areas is highly fragile, featuring intricate and variable land use patterns. The degradation of the region’s ecosystem and severe economic development constraints are linked closely to the cultivation practices of ethnic minority groups and the area’s complex terrain and geology. Comprehensively enhancing ecosystem service functions in this area and promoting coordinated economic development across various ethnic groups have become urgent imperatives.

We conducted a case study in Yongsheng County, Yunnan Province to explore potential balance among multiple groups with varying interests in land use decision-making. We focused on unique ethnic minority stakeholder groups and specific land use patterns in Yunnan’s mountainous canyon regions, targeting the impact of land use changes on ecosystem multifunctionality (EMF) and a multi-stakeholder equity index and seeking to identify land use type combinations that balance these two factors. This work yields an in-depth analysis of the relationship between multi-stakeholder group equity and ecosystem services, offering a sound foundation for local governments to formulate scientific land use policies and promote the coordinated development of ecological protection, community participation, and economic growth.

By integrating the perspectives of diverse groups, it becomes possible to foster local residents’ sense of identification with and participation in land use planning, thereby making policy implementation more effective. Moreover, our findings may be useful in advancing land use optimization theory from a multi-stakeholder perspective supported by empirical data and interdisciplinary insights. This research provides scientific support for decision-makers while potentially contributing to further land use optimization research in similar regions.

2. Study Area and Data Sources

2.1. Study Area Overview

Yongsheng County (100°22′–101°11′ E, 25°59′–27°04′ N) is situated in an ecologically fragile area typical of the northwest region of Yunnan Province, China. The county administers 18 townships, covering a total land area of 4950 km². Mountainous terrain comprises 92.42% of the total area, characterized by poor soil quality and severe soil erosion. High-altitude cold mountainous regions comprise nearly one-third of Yongsheng’s total area. The average annual precipitation is approximately 1000 mm, with mean annual temperatures ranging from 7.9 °C to 10.5 °C.

Yongsheng County is home to 10 indigenous ethnic minority groups, including the Mosuo, Lisu, and Naxi, with ethnic minorities constituting 30.49% of the total population [15,16]. Addressing the needs of these minority groups in land use decision-making is crucial for the socio-economic development of the Yongsheng region on the whole.

2.2. Data Sources

Land use data for Yongsheng County were obtained from the 2022 China 30 m land cover dataset published by the National Cryosphere Desert Data Center (http://www.ncdc.ac.cn, accessed on 1 October 2023). To analyze the ecological impacts of various land use types, land classifications were aligned with ecosystem types and integrated with China’s current land use classification system. Nine types were identified considering the current land use status and land resource characteristics of the area (Figure 1): Farmland, broadleaf forest, coniferous forest, mixed forest, shrubland, grassland, meadow, water bodies, and built-up land. The composition of these land use types in Yongsheng County is as follows: farmland (6%), meadow (6%), water bodies (4%), mixed forest (2.6%), broadleaf forest (3.5%), coniferous forest (40%), grassland (37%), built-up land (0.8%), and shrubland (0.1%).

Data for food crop planting areas, grain production, and net profit from grain production were sourced from statistical yearbooks, including the Yongsheng Statistical Yearbook 2022, China Statistical Yearbook 2022, and National Agricultural Product Cost–Benefit Data Compilation 2022 [17,18,19].

3. Methodology

We employed the Participatory Rural Appraisal (PRA) method in this study to investigate the interests of various stakeholder groups. The PRA method, widely recognized as a mainstream approach to field surveys in China [20,21], was applied across local government sectors including agriculture, resources, and environmental management. Informal interviews were conducted with local residents to gain insights into the actual conditions of the study area. Stakeholder groups were delineated according to the county’s diverse ethnic minority populations. We used the equivalent factor method to make ecosystem value (ESV) calculations and to comprehensively estimate trends in equity and multifunctionality indices under shifting land use conditions.

3.1. Stakeholder Group Survey and Reliability Testing

From May to July 2023, our team conducted field visits and distributed questionnaires in six villages across Yongsheng County. The primary respondents to the survey were long-term residents of the area. The rationale for selecting this group was their ability to provide accurate and clear insights into local conditions, ensuring reliable data for subsequent analyses.

The survey centered on assessing the demand–satisfaction levels for six major ecosystem services: food production, water supply, aesthetic value, biodiversity conservation, climate regulation, and raw material production. Participants from five ethnic minority groups, including the Han and Naxi, were involved, encompassing 38 households and 217 individuals.

The Cronbach’s alpha coefficient is a widely used indicator of the reliability of surveys in social science research [22,23]. We used the Cronbach’s alpha coefficient to measure the reliability of our questionnaire, where values closer to 1 indicate higher reliability. A coefficient of 0.6 is considered within the acceptable range, while values above 0.8 represent high reliability.

3.2. Ecosystem Service Value Assessment

As mentioned above, we evaluated six primary local ecosystem services: Food production, water supply, aesthetic value, biodiversity conservation, climate regulation, and raw material production. The assessment was designed with reference to the ESV coefficient data table proposed by Xie [24], which is tailored to Chinese ecosystems. To adapt this methodology to local conditions, the food production correction method [25] was employed to adjust the ESV equivalent per unit area. The results are listed in

Table 1. Ecosystem multifunctionality equivalent per unit area.

Primary Classification Categories	Secondary Classification Categories	Farmland	Mixed Forest	Broadleaf Forest	Coniferous Forest	Shrubland	Grassland	Meadow	Water Bodies	Built-Up Land
Provisioning services	Food production	0.39	0.14	0.13	0.10	0.09	0.05	0.10	0.36	0
	Energy production	0.18	0.32	0.30	0.23	0.19	0.06	0.15	0.10	0
	Water supply	0.01	0.17	0.15	0.12	0.10	0.36	0.08	3.73	0
Regulating services	Climate regulation	0.16	3.16	2.93	2.28	1.90	0.60	1.36	1.03	0
Supporting services	Biodiversity	0.06	1.17	1.09	0.85	0.71	0.25	0.57	1.15	0.01
Cultural services	Aesthetic value	0.03	0.51	0.48	0.37	0.31	0.11	0.26	0.85	0

.

$$\lambda ={\displaystyle \frac{Q}{Q_0}}$$

(1)

$$E_{ij}=\mathsf{\lambda }\times E_{0ij}$$

(2)

where $\lambda$ represents the regional adjustment coefficient for ecosystem service equivalence; and $Q$ and $Q_0$ denote the grain yield per unit area of farmland in the study area and the national average, respectively.

In Equation (2), E_i represents the adjusted ESV equivalent for the $i$-th land use type in the study area, and E_0ij represents the national average ESV equivalent for the $i$-th land use type. The index $i$ ranges from 1 to 9, corresponding sequentially to dry farmland, mixed coniferous and broadleaf forest, broadleaf forest, coniferous forest, shrubland, grassland, meadow, water bodies, and built-up land. The index j ranges from 1 to 6, corresponding sequentially to food production, raw material production, water supply, climate regulation, biodiversity, and aesthetic value.

As per the Yongsheng Statistical Yearbook 2022, China Statistical Yearbook 2022, and National Agricultural Product Cost–Benefit Data Compilation 2022 [17,18,19], we determined that wheat, rice, and corn were the main grain crops in Yongsheng County during the study period. Data on the sowing area, yield, and prices of these major crops were obtained (

Table 2. Grain production price, output, and sown area statistics for Yongsheng County.

Major Crops	Price (CNY/t)	Average Yield (t/ha)	Sown Area (ha)
Wheat	2461.2	3.3	1613
Rice	2711.4	8.6	6615
Corn	2531.6	5.7	19767

). The ESV equivalent factor method defines the economic value of one ESV equivalent factor as one-seventh of the market value of the average annual grain yield per unit area in the study region. Calculations revealed that the ESV of one standard equivalent factor in the Yongsheng area is 2309.49 yuan/ha.

$$\mathrm{P}={\displaystyle \frac{1}{7}}\times \sum _{i=1}^j{\displaystyle \frac{{{q_{i}p}_{i}m}_i}{M}}$$

(3)

where P represents the ESV of one standard equivalent factor, $i$ is the type of grain crop, j is the total number of main grain crop types, m_i is the sowing area of the $i$-th grain crop, p_i is the average yield of the $i$-th grain crop, q_i is the average price of the $i$-th grain crop, and M is the total sowing area of the main crops.

3.3. Ecosystem Service Index Evaluation

To define various combinations of land use types, we comprehensively simulated land use scenarios while assigning values from 1 to 10 to each land use type. An EMF was calculated for the various services and total value corresponding to each simulated scenario. The specific calculation formula is as follows:

$${ESV}_j=\sum A_i\ast R_{ij}$$

(4)

$$ESV=\sum {ESV}_j$$

(5)

where ESV_j is the value of the j-th ecosystem service, A_i is the area of the i-th ecosystem type, and R_ij is the equivalent factor coefficient of the j-th ecosystem service for the i-th ecosystem type. In Equation (5), ESV is the sum of

ESV_j. The multifunctionality for each stakeholder group was derived by multiplying the normalized ecosystem service provision value by the stakeholder group priority score:

$$M_i=\sum {\displaystyle \frac{\left(Ev{m}_{ij}-{Ev1}_j\right)\ast \alpha }{{Ev1}_j}}$$

(6)

where M_i is defined as the aggregate ecosystem value of the i-th simulated scenario, Evm_ij is the ESV of the j-th category for the i-th simulated scenario, and Ev_1j denotes the ESV of the j-th category under the existing ecosystem configuration. The term α signifies the prioritization score assigned to each stakeholder group cohort.

3.4. Assessment of Ecosystem Service Equity

To better assess the fairness in land use allocation across the study area, we introduced the Gini coefficient to analyze the supply–demand relationship of ecosystem services among multiple stakeholder groups and to quantify the equity of ecosystem service acquisition for each group [26]. The ecosystem services potentially obtained by each group were weighted according to priority scores. The Gini coefficient for each group under each scenario was then calculated using the Gini function in R-4.2.1. A larger Gini coefficient indicates weaker fairness, while a smaller Gini coefficient reflects greater fairness. Accordingly, we used the 1−Gini coefficient to represent the fairness score for each scenario.

4. Results and Analysis

4.1. Demographic Profile of Surveyed Residents

Table 3. Basic demographic information of respondents.

Demographics	Group	Percentage
Gender	Male	70.37%
	Female	29.63%
Ethnicity	Han	27.65%
	Lisu	25.39%
	Mosuo	26.71%
	Naxi	20.25%
Education level	Primary school or below	37.04%
	Junior high school	29.63%
	High school	25.93%
	Undergraduate degree or higher	7.41%
Age	<18	3.7%
	18–24	0%
	25–34	11.11%
	35–44	14.81%
	45–54	25.93%
	>55	44.44%
Length of residence	<5 year	0
	5–10 years	0
	11–20 years	4.55%
	21–30 years	0
	31–40 years	13.64%
	>40 years	81.82%

outlines the demographic characteristics of the surveyed residents. The sample exhibits ethnic diversity, with relatively balanced representation across various groups. Educational attainment among residents was predominantly concentrated at the primary or lower secondary level. Specifically, 37.04% of respondents had completed primary education, 29.63% had attained lower secondary education, 25.93% had completed upper secondary or vocational education, and 7.41% held tertiary qualifications.

The age distribution of the sample was skewed towards residents aged 45 and above. The age cohort distribution was as follows: 3.7% were under 18 years of age, 11.11% were between 25 and 34, 14.81% were between 35 and 44, 25.93% were between 45 and 54, and 44.44% were 55 years old or older.

The sample was predominantly composed of long-term local inhabitants. With respect to duration of residence, 4.55% had resided in the area for 11–20 years, 13.64% for 31–40 years, and 81.82% for more than 40 years. The preponderance of residents with over 40 years of local residency suggests a more nuanced and comprehensive understanding of the temporal changes in local natural and economic conditions among the survey respondents.

4.2. Ecosystem Service Satisfaction Levels among Diverse Stakeholder Groups

Yongsheng County, located in the northwestern mountainous area of Yunnan Province, is home to a diverse range of ethnic groups, including the Lisu and Mosuo minorities. Each group has distinct lifestyles, cultural backgrounds, and production methodologies. Differences in geographical environments, historical contexts, and economic conditions have led to divergent approaches to land utilization among these groups.

To address the unique land use needs and satisfaction of various ethnic minorities, we stratified households of different ethnicities into discrete stakeholder groups (farmers, local governments) concerning the six major ecosystem services of food production, water supply, aesthetic value, biodiversity conservation, climate regulation, and raw material production.

In terms of demand, groups such as the Mosuo and Naxi exhibit significant needs for food production and the associated services of water supply and climate regulation. The Lisu show a much higher demand for biodiversity conservation compared to other groups. In Yongsheng County overall, minority groups display high levels of need for climate regulation services and water supply, reflecting the fact that local agricultural production is a crucial source of livelihood among these minority populations (see

Table 4. Degree of demand by stakeholder group.

Group		Food Production	Water Supply	Aesthetic Value	Biodiversity	Climate Regulation	Energy Production
Farming households	Lisu	1	4	2	6	5	3
	Mosuo	6	5	2	1	3	4
	Han	3	4	2	1	6	5
	Naxi	6	5	3	1	4	2
Local government	Forestry and Grassland Bureau	6	4	1	3	5	2
	Agriculture and Rural Affairs Bureau	6	1	5	2	3	4
	Natural Resources Bureau	5	3	6	2	4	1

Note: 1–6 represent the degree of demand, with higher numbers indicating a higher degree of demand.

).

The satisfaction survey results suggest that the majority of groups have sufficient access to food production services. However, with the exception of the Lisu, all other groups reported inadequate access to water supply services from the ecosystem, highlighting the salience of water-related issues in the Yongsheng region. Notably, entities such as the Forestry and Grassland Bureau and the Agriculture and Rural Affairs Bureau exhibit relatively low levels of satisfaction with aesthetic values. The Mosuo demonstrate consistently low satisfaction levels across all six categories of ecosystem services evaluated. This may be attributable to the comparatively low living standards currently prevalent in Mosuo-concentrated areas, which potentially impede their ability to meet basic production and livelihood needs (

Table 5. Satisfaction levels by stakeholder group.

Group		Food Production	Water Supply	Aesthetic Value	Biodiversity	Climate Regulation	Energy Production
Farming households	Lisu	Basically satisfied	Basically satisfied	Exceeded satisfaction	Exceeded satisfaction	Basically satisfied	Basically satisfied
	Mosuo	Difficult to satisfy	Difficult to satisfy	Difficult to satisfy	Difficult to satisfy	Difficult to satisfy	Difficult to satisfy
	Han	Exceeded satisfaction	Difficult to satisfy	Basically satisfied	Exceeded satisfaction	Basically satisfied	Exceeded satisfaction
	Naxi	Basically satisfied	Difficult to satisfy	Basically satisfied	Difficult to satisfy	Difficult to satisfy	Basically satisfied
Local government	Forestry and Grassland Bureau	Basically satisfied	Difficult to satisfy	Difficult to satisfy	Basically satisfied	Difficult to satisfy	Difficult to satisfy
	Agriculture and Rural Affairs Bureau	Basically satisfied	Difficult to satisfy	Difficult to satisfy	Basically satisfied	Difficult to satisfy	Difficult to satisfy
	Natural Resources Bureau	Difficult to satisfy	Difficult to satisfy	Basically satisfied	Basically satisfied	Difficult to satisfy	Basically satisfied

).

Reliability analysis was conducted on the questionnaire using SPSS software (https://www.ibm.com/spss, accessed on 1 October 2023), yielding a Cronbach’s alpha coefficient of 0.611. This value exceeds the commonly accepted threshold of 0.6, indicating the acceptable internal consistency of the measurement instrument (

Table 6. Cronbach’s alpha coefficient judgment table.

Coefficient Range	Evaluation Effect	Entire Scale
Cronbach’s α < 0.5	Unsatisfactory, discard	Unsatisfactory, discard
0.5 ≤ Cronbach’s α < 0.6	Marginally acceptable	Unsatisfactory, revise questionnaire
0.6 ≤ Cronbach’s α < 0.8	Acceptable	Acceptable
0.8 ≤ Cronbach’s α	High reliability	High reliability

).

4.3. Quantitative Assessment of Ecosystem Multifunctionality

The ESV for Yongsheng County was quantified using the unit area equivalent factor method, creating a comprehensive valuation of ecosystem services under the existing landscape configuration (

Table 7. Ecosystem service value in CNY.

Food Production	Energy Production	Water Supply	Climate Regulation	Biodiversity	Aesthetic Value
2.82 × 108	4.31 × 108	4.49 × 108	1.40 × 109	1.55 × 109	7.07 × 108

). Climate regulation and biodiversity conservation services hold comparatively high values in the region. This is largely due to the implementation of the Grain for Green Program at the turn of the century, which has resulted in a substantial increase in forest cover in Yongsheng County relative to the previous century. Furthermore, the significant proportion of aesthetic value enhances the region’s capacity to attract tourists, thereby generating economic value [27].

4.4. Land Use Multi-Scenario Analysis

In this study, we juxtaposed the multifunctionality of ecosystem services and the equity indices of 10⁷ simulated scenarios with those derived from the current landscape composition for analysis, observing notable disparities in these values between them, as illustrated in Figure 2. The vertical axis in the figure represents the multifunctionality index; the horizontal axis represents the equity index, with the origin denoting the multifunctionality index and equity index provided by the current landscape composition. Relative to the present landscape composition, most multifunctionality indices are concentrated below the zero point for multifunctionality, implying that the current landscape configuration provides a relatively high level of EMF.

To determine the optimal land use type combination, we assigned equal weight to multifunctionality and equity to seek the configuration with the highest overall value. Under these conditions, the optimal land use type composition was determined to be 20% farmland, 4% water bodies, 21% mixed forests, 20% coniferous forests, and 35% meadow. This configuration, marked with a red star in Figure 2, results in a 4.2% decrease in EMF but an 8.5% increase in the equity index compared to the current land use scheme.

Changes in land use types directly affect both the multifunctionality of ecosystem services and the equity index. By simulating landscape scenarios and assessing these factors comprehensively, we sought to identify configurations maximizing both multifunctionality and equity simultaneously.

In the simulated scenarios, when the proportion of other landscape types remains constant and forests consist equally of coniferous and broadleaf types, with no mixed types, the equity index decreases by 0.8% while the multifunctionality index increases by 5% compared to the current landscape composition. When all forests are converted to broadleaf forests, the equity index decreases by 1.5% while the multifunctionality index increases by 12% relative to the current landscape. These results indicate that in the Yongsheng area, as the proportion of broadleaf forests increases, the equity index decreases while the multifunctionality index increases.

With the proportion of other landscape types kept constant, with mixed and coniferous forests occupying equal proportions of all forested areas, the equity index increases by 1% while the multifunctionality index decreases by 8% compared to the current landscape. When all forests are converted to mixed forests, the equity index decreases by 2.2% and the multifunctionality index increases by 17% compared to the current landscape. These findings suggest that in the Yongsheng area, there is an initial increase and subsequent decrease in the equity index as the proportion of mixed forests increases from small to large.

When the proportion of other landscape types remains unchanged while meadow and shrubs occupy equal proportions, the equity index decreases by 0.06%, and the multifunctionality index increases by 7% compared to the current landscape composition, where meadow coverage currently far exceeds shrub coverage. When all meadow land is converted to shrub land, the equity index decreases by 1.5% while the multifunctionality index increases by 15% compared to the current landscape. These results indicate that meadows exert a more positive effect on the equity index than shrubs, though they provide a lower multifunctionality index.

In summary, the Yongsheng area has a fragile ecological environment [28]. Increasing the proportion of coniferous forests and meadows as land use types may improve the equity index in this region, fostering equality among multiple stakeholders in Yongsheng, though at the expense of some loss of ecosystem service multifunctionality.

5. Discussion

5.1. Selection of the Optimal Land Use Type

According to our simulations, increasing the equity index tends to decrease the level of multifunctionality. However, the level of equity must be appropriately improved to reduce social conflicts and promote long-term stability and sustainable development, although this may sacrifice some multifunctionality in the short term. Research by Simion et al. supports this view [29], emphasizing that in mountain ecosystems, more equitable resource management can lead to a more stable and coordinated social environment even if it briefly reduces versatility. This suggests that a modest increase in equity can lead to greater social and ecological benefits, which are not necessarily visualized in the figure. The current landscape configuration provides a relatively high level of multifunctionality but a relatively low level of equity; improving equity should be prioritized over improving multifunctionality. In view of this, the optimal land type combination as per the findings of this case study is, effectually, the optimal land use type combination for the Yongsheng area.

5.2. Major Ecosystem Services Affecting Farmers’ Equity

The PRA survey revealed that the primary income source for farmers in the Yongsheng area is the sale of agricultural products. Consequently, farmers prioritize ecosystem services such as food production and water supply over others. Scholars have made similar observations in other regions of China, where changes in food production services more significantly impact farmers’ equity than other services [30,31]. This is primarily because farmers’ economic livelihoods are dependent on provisioning and regulating such services [32]. Therefore, increasing the EMF of provisioning and regulating services can enhance equity across different groups of farmers in a given region [33]. However, excessively high forest coverage can lead to increased plant evapotranspiration, resulting in reduced water availability, indirectly affecting farmers’ equity [34].

5.3. Land Use Optimization Strategies

From the perspective of ESV, Yongsheng County’s water supply multifunctionality is not tremendously low, and precipitation has trended upward in recent years, maintaining relatively high levels over the past decade [35]. However, the survey results indicate that, aside from the Lisu ethnic group, residents are unable to obtain sufficient water supply services from the ecosystem. This suggests that prominent issues in water use for food production and an uneven spatial distribution of water resources have led to low water utilization efficiency in Yongsheng. The county has some of the lowest national water resource utilization rates statistically, leading to low satisfaction with water supply services on the whole despite higher precipitation levels compared to previous years. Further, our survey indicates that the low satisfaction with water supply services among most groups is primarily due to an inability to fully satisfy the demands for agricultural production. Therefore, improving water resource utilization efficiency in agricultural production specifically may positively impact equity levels in Yongsheng County.

Additionally, Yongsheng County exhibits a very high multifunctionality index, largely due to its significant forest cover. Between 2000 and 2020, Yongsheng County implemented the Grain for Green Project and Natural Forest Protection Policy, adhering to strict ecological protection measures that led to a substantial increase in forest areas. Despite this large-scale afforestation, however, several challenges remain. For instance, there is a high proportion of young and middle-aged forests, suboptimal forest stand structures, and an oversized proportion of monoculture forests compared to mixed forests [36]. These factors reduce the stability, resistance, and biodiversity of forest stands, increasing the risk of fires and pest outbreaks and threatening the overall health of forest resources. They also limit the ecological benefits (e.g., biodiversity conservation) that forests can provide [37]. Through appropriate afforestation efforts, aimed at increasing the proportion of mixed forests in the Yongsheng area, it may be possible to improve both EMF and equity indices as a viable strategy for enhancing ecological as well as social outcomes.

6. Conclusions

In this study, we analyzed the relationship between EMF and equity in Yongsheng County, Yunnan Province, China by simulating different land use scenarios and exploring various impacts on diverse stakeholder groups. We found that the current EMF in Yongsheng County is primarily reflected in climate regulation and biodiversity conservation, with a relatively high multifunctionality index but a comparatively low equity index. Integrating existing theoretical frameworks, this study’s results support a trade-off between the multifunctionality of ecosystem services and equity among residents. The findings highlight a need to consider both factors in decision-making surrounding land use patterns.

We determined an optimal land use type by comprehensively considering equity and multifunctionality indices across numerous simulated scenarios: 20% farmland, 4% water bodies, 21% mixed forests, 20% coniferous forests, and 35% grassland. Yongsheng County can achieve this optimal land use combination by appropriately converting pure forests to mixed forests and increasing overall afforestation to increase the coverage of mixed forests. Agricultural investment can also be increased, improving agricultural production conditions in suitable areas while enhancing water resource utilization efficiency. Land use decisions should be made in consideration of not only multifunctionality indices but also residents’ equity, as combining these factors allows for a more comprehensive understanding of land use dynamics and supports the sustainable development of land resources. This approach would align with China’s current rural revitalization strategy and the urgent need to improve the quality of its forests.

The results of this study may provide policymakers with workable insights into the comprehensive effects of different land use types on ecosystem services and residents’ equity, enabling them to formulate more balanced land use strategies. Moreover, this study not only offers a scientific basis for land use optimization in Yongsheng County but also may serve as a valuable reference for land management strategies in similar regions. Future research could further explore the relationship between multifunctionality and equity in different regions and across broader scenarios to further validate and refine the recommendations we propose.