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Journal of Resources and Ecology >

2025 , Vol. 16 >Issue 2: 297 - 305

DOI: https://doi.org/10.5814/j.issn.1674-764x.2025.02.002

Ecosystem Quality and Ecosystem Services

The Valuation of Forest Ecosystem Service Function and Influencing Factors in Yunnan Province

  • ZOU Zaijin ,
  • ZOU Yunzi , *
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  • School of Economics and Management, Southwest Forestry University, Kunming 650224, China
* ZOU Yunzi, E-mail:

ZOU Zaijin, E-mail:

Received date: 2024-03-22

  Accepted date: 2024-07-10

  Online published: 2025-03-28

Supported by

The Humanities and Social Sciences Research Project for Educational Co-operation among Provincial Institutes and Universities in Yunnan Province(SYSX202107)

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Abstract

As a large province with forest resources, assessing the value of forest ecosystem services in Yunnan is of great significance to maintain the sustainable development of Yunnan’s economy. Based on the latest survey data of Yunnan Province, i.e., the forest resources type II survey data, and in accordance with the Specification for Forest Ecosystem Service Function Assessment (GB/T 38582-2020), the value of forest ecosystem service function of 16 cities (prefectures) in Yunnan was assessed, and the ridge regression method was used to study the main factors affecting the value differences among cities (prefectures). The results show that: (1) The value of forest ecosystem services in Yunnan is 982.926×109 yuan yr-1, of which the value of carbon fixation and oxygen release is the largest. (2) The top four cities (prefectures) in terms of value of services are Pu’er City > Chuxiong Prefecture > Diqing Prefecture > Dali Prefecture; the bottom four cities (prefectures) are Kunming City > Yuxi City > Dehong Prefecture > Zhaotong City; (3) The main factors affecting the value of the service function of each city (prefecture) are forested land area, forest cover, GDP and population density. The findings of this study provided a reference for the sustainable development of the ecological environment in the prefectures and cities of Yunnan Province.

Key words： forest ecosystems; valuation; influencing factors; Yunnan Province

Cite this article

ZOU Zaijin , ZOU Yunzi . The Valuation of Forest Ecosystem Service Function and Influencing Factors in Yunnan Province[J]. Journal of Resources and Ecology, 2025 , 16(2) : 297 -305 . DOI: 10.5814/j.issn.1674-764x.2025.02.002

1 Introduction

Forests are an indispensable part of terrestrial ecosystems (Zuo et al., 2021). It has special functions and can provide all kinds of materials and energy; at the same time, it is also one of the basic resources on which human beings rely for survival and development, and it has received people’s attention by virtue of its special ecological function and great economic value. Only scientific assessment of forest ecosystem value can effectively guide the rational exploitation and utilization of forest resources, thus providing a strong guarantee for maintaining the benign development of forest ecosystems, which is of great significance for forest protection, sustainable forest management and ecological compensation (Yu et al., 2005; Li, 2007).
Ecosystem service function, which is the embodiment of the benefits that people derive from the ecosystem. Currently, domestic and foreign scholars’ research in this field mainly focuses on the assessment of the value of service functions. Research on the assessment of forest ecosystem services began in the early 20th century, first focusing on the assessment of the benefits of forests in water conservation, and then gradually expanding to the benefits of farmland protection and environmental protection. Since 1980, scholars have begun to pay extensive attention to the quantitative and evaluation of forest ecosystem services (Biao et al., 2010). In 1982, Zhang (1982) first estimated the forest functions in Yunnan and other regions by using the substitution cost method and the shadow engineering method, which laid the foundation for research on assessing the value of forest ecosystem service functions in China. In 1997, Costanza et al. (1997) made an innovative and complete summary of the forest ecosystem service system, which made the research on estimating forest value reach a new level. In the 21 st century, the research of domestic scholars on assessing the value of forest ecosystem service function has continued to increase. Scholars have taken provinces, municipalities, national forest parks and nature reserves as the objects of their studies, and have selected different indicators and methods for value assessment (Xu and Zhong, 2007; Liu et al., 2017; Zuo et al., 2021).
Summarizing the research results of previous scholars, it is found that most of the current research on forest ecosystem service function focuses on assessing its value, and there is less literature on further analyzing the influencing factors that lead to the differences in the value of service function in different regions. Therefore, taking Yunnan as the research object, the value of forest ecosystem services in 16 cities (prefectures) was evaluated, and the main influencing factors leading to the difference of service function value in each city (prefecture) were further analyzed. Based on the results of the analyses, relevant suggestions were put forward to provide reference for improving the quality of forests and building a good ecological environment in each city (prefecture) in Yunnan.

2 Research region and method

2.1 Overview of study area

Yunnan Province is located in the southwest border of China, bordering Myanmar, Vietnam and Laos. As a major province of forest resources in China, it is known as the “Kingdom of Plants”, with 19365 species of higher plants in the province. According to the fourth Yunnan Forest Resources Type II Survey (Zhang and Hu, 2018), the forest land area of the province reaches 2.2736×107 ha, the forest land area reaches 2.6071×107 ha, and the total standing wood stock reaches 1.913×109 m3, which are all ranked the second in the country, and the forest coverage reaches 59.3%, which is located in the fifth in the country, and the specific forest land area of each city (prefecture) is shown in Figure 1.
Figure 1 Spatial distribution of forest land area in cities (prefectures) of Yunnan Province, 2021

2.2 Data sources

At the value assessment stage, taking into account the authority, accessibility and completeness of the data, the data from the most recent forest resources survey in Yunnan Province, i.e., the fourth Class II forest resources survey in Yunnan Province, were selected for value assessment. The relevant social public data used in the conversion between the material quality and value of forest ecosystem services are mainly based on the disclosure of national authorities, as shown in Table 1. In the research stage of influencing factors, the relevant variable data were selected from the 2021 data and are all derived from the 2021 Yunnan Statistical Yearbook.
Table 1 Data on public social resources
Name Unit Numerical Source
Water resources market transaction price yuan m-3 6.11 Tu et al., 2023
Cost of water purification yuan t-1 2.09
Diammonium phosphate fertilizer prices yuan t-1 2400
Potassium chloride fertilizer prices yuan t-1 2200
Price of carbon fixation yuan t-1 1200
Negative ion production costs 10-18 yuan units-1 5.8185
Cost for excavation and transportation of soil per unit volume yuan m-3 63 Refer to China Forest Resources Accounting Research Project Group (2015)
Nitrogen content of diammonium phosphate % 14
Phosphorus content of diammonium phosphate % 15.01
Potassium content of potassium chloride % 50
Organic matter prices yuan t-1 800
Manufacturing oxygen prices yuan t-1 1126 Referring to China Forest Resources Accounting Research Project Group (2015), according to the price index (pharmaceutical manufacturing industry), the price of 1108 yuan t-1 in 2013 is converted to the current price of 1126 yuan t-1 in 2016
Sulfur dioxide treatment costs yuan kg-1 1.42 Referring to China Forest Resources Accounting Research Project Group (2015), based on the China Forest Ecosystem Services Accounting Social Public Data Sheet (2013 Recommended Use Prices), with 2013 prices converted to 2016 current prices through the Industrial Producer Ex-factory Price Index
Fluoride treatment costs yuan kg-1 0.82
Nitrogen oxide treatment costs yuan kg-1 0.74
Dust abatement and clean-up costs yuan kg-1 0.18

2.3 Indicator system and methodology

According to the Specification for Forest Ecosystem Service Function Assessment (GB/T 38582-2020) (National Forestry and Grassland Administration, 2020), five functions and 17 indicators, including carbon sequestration and oxygen release, water conservation, etc., were selected to quantify and assess the service function of forest ecosystems in various prefectures and cities in Yunnan Province. The accounting method of material quality and value is mainly based on the “Specification for Forest Ecosystem Service Function Assessment”, while the ridge regression method was used for the study of influencing factors.

3 Results and analysis

By quantifying the ecological benefits brought by the forest resources in each city (prefecture) in Yunnan, thus obtaining the value of the output of forest resources in each city (prefecture) in Yunnan. At the same time, the spatial change of forest ecosystem service function of each city (prefecture) was mapped according to the map with review number GS(2022)1873, with no modification of the base map.

3.1 Material quality analysis

One of them is the analysis of material quality results. As can be seen from Table 2, the quality of soil conservation substances of Yunnan forest ecosystem reaches 909.4582 million t yr-1; the quality of forest nutrient fixation substances reaches 617320 t yr-1; the quality of water conservation substances reaches 9.43×1010 m3 yr-1; the quality of carbon fixation and oxygen release substances reaches 380.4427 million t yr-1; in the function of purifying the atmospheric environment, the total amount of substances providing the negative ions index is 22.32×1025 units yr−1, the quality of absorbing gas pollutants substances reaches 3.017 million t yr−1, and the amount of dust catching index is 425.6017 million t yr−1.
Table 2 Quality of forest ecosystem services in Yunnan Province
Cities
(prefectures)		 Soil conservation
(104 t yr-1)		 Forest nutrient fixation
(104 t yr-1)		 Water conservation
(105 m3 yr-1)		 Carbon fixation and oxygen release
(104 t yr-1)		 Atmospheric purification
Soil consolidation Fertilizer
retention		 Nitrogen
immobilization		 Phosphorus
fixation		 Potassium
fixation		 Regulating flow Water purification Fixed
carbon		 Releasing oxygen Provide negative ions
(1025 units yr-1)		 Absorbing gas pollutants
(104 t yr-1)		 Dust catching
(104 t yr-1)
Kunming 4165.11 109.13 1.84 0.29 0.77 17408.52 17408.52 637.16 1150.82 1.05 14.18 2064.64
Qujing 4973.77 130.31 2.19 0.35 0.92 18473.39 18473.39 760.87 1374.26 1.25 16.93 2877.42
Yuxi 3482.32 91.24 1.54 0.24 0.65 16924.83 16924.83 532.71 962.17 0.88 11.85 1732.09
Baoshan 5023.94 131.63 2.22 0.35 0.93 27996.07 27996.07 768.55 1388.12 1.27 17.10 2316.60
Zhaotong 2881.64 75.50 1.27 0.20 0.54 8640.36 8640.36 440.82 796.20 0.73 9.81 1285.43
Lijiang 5742.76 150.46 2.53 0.40 1.07 33709.81 33709.81 878.51 1586.73 1.45 19.55 3156.85
Pu’er 12553.24 328.90 5.54 0.87 2.33 74063.78 74063.78 1920.35 3468.48 3.16 42.73 5812.74
Lincang 5436.26 142.43 2.40 0.38 1.01 30144.58 30144.58 831.62 1502.04 1.37 18.51 2192.48
Chuxiong 7945.26 208.17 3.51 0.55 1.48 44854.11 44854.11 1215.44 2195.29 2.00 27.05 4233.63
Honghe 5844.01 153.11 2.58 0.41 1.09 23679.29 23679.29 894.00 1614.71 1.47 19.89 2395.62
Wenshan 4644.47 121.69 2.05 0.32 0.86 16816.36 16816.36 710.49 1283.27 1.17 15.81 2118.78
Xishuangbanna 4437.4 116.26 1.96 0.31 0.82 30729.69 30729.69 678.82 1226.06 1.12 15.11 1411.49
Dali 6999.31 183.38 3.09 0.49 1.30 36484.01 36484.01 1070.73 1933.92 1.76 23.83 3729.36
Dehong 3184.03 83.42 1.41 0.22 0.59 18772.07 18772.07 487.08 879.75 0.80 10.84 1083.60
Nujiang 4493.04 117.72 1.98 0.31 0.84 29004.49 29004.49 687.33 1241.43 1.13 15.29 2242.97
Diqing 6817.33 178.61 3.01 0.48 1.27 43845.12 43845.12 1042.89 1883.64 1.72 23.21 3906.47
The second is the analysis of spatial variation of material quality. It can be seen from Figure 2 that the physical quality of cities (prefectures) in Yunnan Province showed significant changes under different indicators. Among them, the indicators with the largest variation are the provision of negative ions and the amount of soil fixation, with the city or prefectures providing the highest negative ions up to 3.16×1025 units and the lowest only 0.73×1025 units, while the amount of soil consolidation is up to 125.5324 million t in the city or prefecture with the highest amount of soil fixation and the lowest 28.8164 million t; the indicators with the smallest variations are the potassium fixation and the phosphorus fixation. Their changes range from 5440 to 23300 t and from 2000 to 8700 t, respectively.
Figure 2 Spatial variation of forest ecosystem service function and quality in cities (prefectures) of Yunnan Province, 2021
The material quality of forest ecosystem service function in Pu’er City, Chuxiong Prefecture and Diqing Prefecture ranked the top three in the province. While Yuxi City, Dehong Prefecture and Zhaotong City ranked among the bottom three in the province in terms of the quality of soil conservation, forest nutrient fixation, carbon fixation and oxygen release. In terms of water conservation function, Yuxi City, Wenshan Prefecture and Zhaotong City were located in the last three places in the province; in terms of the function of purifying the atmospheric environment, Zhaotong City and Dehong Prefecture were relatively backward.

3.2 Value quantity analysis

The first is the analysis of the value of the results. As can be seen from Figure 3, the total value of carbon fixation and oxygen release of Yunnan forest ecosystem is the highest, accounting for 44.6% of the total value of forest ecosystem service function in Yunnan. While the total value of forest nutrient fixation is the lowest, accounting for 0.38% of the total value. The total value of the remaining service functions, in descending order, are water conservation, atmospheric purification, and soil conservation (Table 3).
Figure 3 Value volume share of each function
Table 3 Valuation of forest ecosystem service functions in Yunnan Province
Cities
(prefectures)		 Soil conservation
(109 yuan yr-1)		 Forest nutrient fixation
(109 yuan yr-1)		 Water
conservation
(109 yuan yr-1)		 Carbon fixation and oxygen release
(109 yuan yr-1)		 Atmospheric purification
(109 yuan yr-1)
Soil consolidation Fertilizer retention Nitrogen immobilization Phosphorus fixation Potassium fixation Fixed carbon Releasing oxygen Provide negative ions Absorbing gas
pollutants		 Dust catching
Kunming 1.749 1.659 0.070 0.030 0.073 14.275 7.646 12.958 0.037 0.200 3.716
Qujing 2.089 1.981 0.084 0.036 0.088 15.148 9.130 15.474 0.044 0.238 5.179
Yuxi 1.463 1.387 0.059 0.025 0.061 13.878 6.393 10.834 0.031 0.167 3.118
Baoshan 2.110 2.001 0.084 0.036 0.088 22.957 9.223 15.630 0.044 0.241 4.170
Zhaotong 1.210 1.148 0.048 0.021 0.051 7.085 5.290 8.965 0.025 0.138 2.314
Lijiang 2.412 2.288 0.096 0.041 0.101 27.642 10.542 17.867 0.050 0.275 5.682
Pu’er 5.272 5.001 0.211 0.090 0.221 60.732 23.044 39.055 0.110 0.601 10.463
Lincang 2.283 2.166 0.091 0.039 0.096 24.719 9.979 16.913 0.048 0.260 3.946
Chuxiong 3.337 3.165 0.133 0.057 0.140 36.780 14.585 24.719 0.070 0.381 7.621
Honghe 2.454 2.328 0.098 0.042 0.103 19.417 10.728 18.182 0.051 0.280 4.312
Wenshan 1.951 1.850 0.078 0.033 0.082 13.789 8.526 14.450 0.041 0.223 3.814
Xishuangbanna 1.864 1.768 0.075 0.032 0.078 25.198 8.146 13.805 0.039 0.213 2.541
Dali 2.940 2.788 0.118 0.050 0.123 29.917 12.849 21.776 0.062 0.335 6.713
Dehong 1.337 1.268 0.053 0.023 0.056 15.393 5.845 9.906 0.028 0.153 1.950
Nujiang 1.887 1.790 0.075 0.032 0.079 23.784 8.248 13.979 0.039 0.215 4.037
Diqing 2.863 2.716 0.115 0.049 0.120 35.953 12.515 21.210 0.060 0.327 7.032
The second is the analysis of the spatial variation of the value quantity. As can be seen in Figure 4, the annual soil conservation value of forest ecosystem in Yunnan Province ranges from 2.358×109 to 10.273×109 yuan yr-1; the value of forest nutrient fixation ranges from 0.12×109 to 0.522×109 yuan yr-1; the value of water conservation is in the range of 7.085×109 to 60.732×109 yuan yr-1; the value of carbon fixation and oxygen release ranges from 14.255×109 to 62.099×109 yuan yr-1; and the value of atmospheric purification ranges from 2.131×109 to 11.175×109 yuan yr-1.
According to Figure 4 and Figure 5, the total value of forest ecosystem service functions in each city (prefecture), in descending order, are: Pu’er City, Chuxiong Prefecture, Diqing Prefecture, Dali Prefecture, Lijiang City, Lincang City, Honghe Prefecture, Baoshan City, Nujiang Prefecture, Xishuangbanna Prefecture, Qujing City, Wenshan Prefecture, Kunming City, Yuxi City, Dehong Prefecture and Zhaotong City.
Figure 4 Spatial changes of the value of forest ecosystem services in various regions of Yunnan Province
Figure 5 Value of forest ecosystem service functions in various regions of Yunnan Province

3.3 Regression analysis

In order to further explore the main influencing factors of the differences in the value of the service function of each city (prefecture), a multiple linear regression model was established for research. In the process of researching the value of forest ecosystem services, some scholars have found that the value is affected by population, economic development level, environment, sulphur dioxide emissions and the size of the study area (Zhao et al., 2021; Zhou, 2022; Song et al., 2023). Therefore, this paper combined the existing studies and selected five independent variables and one dependent variable based on the principles of scientificity, typicality and quantification. The five independent variables were forest land area, population density, forest coverage, sulfur dioxide emissions, and GDP. Among them, the forest land area reflects the health of the ecosystem of a region; the population density reflects the allocation of resources, the provision of services and the impact of human activities on the environment; the forest coverage reflects the abundance of forest resources and the ecological balance; sulphur dioxide emissions reflect the industrial activities and the level of environmental management of a region; and the GDP is a reflection of the overall economic situation and the level of development of a region. The dependent variable was the service function value of each city (prefecture). The specific indicators are shown in Table 4.
Table 4 Indicators
Variable Symbolic
representation		 Logarithmic
symbol
Forest land area LD lnLD
Population density RK lnRK
Forest coverage SL lnSL
Sulfur dioxide emissions EY lnEY
GDP GDP lnGDP
Service function value JZ lnJZ

3.3.1 Ridge regression analysis

Before the establishment of the ridge regression model, it is first necessary to test the indicators for multicollinearity by building a linear regression model. In general, there are two indicators for detecting multicollinearity, namely tolerance (T) and variance inflation factor (VIF). It is generally believed that T<0.2 or VIF>5 indicates a strong correlation between the variables, i.e., the existence of multicollinearity. It can be found from Table 5 that the VIF value in the model is greater than 5, which means that there is a collinearity problem in the model.
Table 5 Regression results
Variable Non-standardized coefficient Standardized coefficient t-value P-value Covariance diagnosis
Beta Standard error Beta VIF Tolerance
Constant 1.494 0.727 - 2.054 0.067 - -
lnLD 0.9 0.106 0.755 8.454 <0.001 2.346 0.426
lnRK -0.09 0.092 -0.167 -0.978 0.351 8.545 0.117
lnSL 1.663 0.265 0.723 6.284 <0.001 3.891 0.257
lnEY -0.02 0.057 -0.056 -0.349 0.734 7.607 0.131
lnGDP 0.205 0.081 0.447 2.532 0.030 9.162 0.109
Partial least squares, ridge regression and principal component regression are commonly used to solve multicollinearity problems. These methods can effectively deal with covariance without excluding explanatory variables. In the article, the ridge regression method is adopted to solve the multicollinearity problem, and the ridge regression is fitted using the SPSS27.0 software so as to obtain the ridge trace map (Figure 6).
Figure 6 Ridge trace map
As shown in Figure 6, when K>0.157, the change trend of most independent variables tends to be gentle, so K=0.157 is selected as the ridge value. Ridge regression was performed according to K=0.157, and the regression results were obtained, as shown in Table 6.
Table 6 Results of ridge regression (Dependent variable: lnJZ)
Variable Non-standardized coefficient Standardized coefficient t-value P-value R² Adjustment of R² F-test
Beta Standard error Beta
Constant 2.754 0.576 - 4.776 0.001 0.929 0.894 26.25
lnLD 0.753 0.089 0.632 8.51 <0.001
lnRK -0.112 0.047 -0.208 -2.398 0.037
lnSL 1.118 0.208 0.486 5.372 <0.001
lnEY -0.008 0.032 -0.023 -0.255 0.804
lnGDP 0.113 0.039 0.247 2.872 0.017
According to Table 6, the adjusted R² is 0.894, which indicates that the regression model has a superior fit and F=26.25, P<0.001, which indicates that the model passes the F-test and the regression model is significant.
This leads to the ridge regression equation, i.e:
lnJZ=0.753lnLD-0.112lnRK+1.118lnSL-0.008lnEY+0.113lnGDP+2.754
The coefficient of the ridge regression equation mainly reflects the elasticity of each independent variable to the value of the service function, that is, the percentage of the corresponding change in the value of the service function for each 1% change in each independent variable; the absolute magnitude of the standardized coefficients can be used to measure the degree of influence of each independent variable on the value of the service function. Through t-test, it can be seen that the P-values of lnLD, lnRK, lnSL and lnGDP are less than 0.05, indicating that forest land area, population density, forest coverage and GDP have a significant effect on the value of service function; while the P-value of lnEY is greater than 0.05, indicating that sulphur dioxide emissions does not have a significant effect on it. Among them, from the standardized coefficients, the degree of influence of forest land area, forest coverage, population density and GDP on the value of service function is in the following order: forest land area (0.632) > forest coverage (0.486) > GDP (0.247) > population density (-0.208).

3.3.2 Regression result

According to the results of ridge regression, the difference of service function value of cities (prefectures) in Yunnan Province was analyzed. It was found that the total amount of forest land area and the total amount of forest coverage of the top three (Pu’er City, Chuxiong Prefecture, Diqing Prefecture) ranked in the value of service functions were higher than those of the bottom three (Yuxi City, Dehong Prefecture, Zhaotong City), and that the population density of the top three were lower than those of the bottom three. Among them, there is a difference of 3.9983 million ha in forest land area, 32.34 % in forest coverage, and 354.4 persons km-2 in population density. This also confirms that the greater the forest land area and forest coverage, the greater the value of service function; the greater the population density, the smaller the value of the service function. However, the GDP of the top three is less than the GDP of the latter three, and the GDP difference is 144.003×109 yuan yr-1, which is contrary to the empirical results. The main reason is that Yuxi City in the bottom three is at the forefront of the development of cities in Yunnan Province as a core city of the city cluster in central Yunnan Province, and also has one of the highest per capita GDP in Southwest China, which leads to the opposite results.

4 Discussion

As a large forest resource province, it is of great significance to study the value of forest ecosystem services in Yunnan Province. This study evaluates the value of forest ecosystem services in Yunnan Province from various aspects and reveals the factors that affect the value differently, and the results of the study provide a reference for the sustainable development of the ecological environment in Yunnan Province. The study found that based on the latest data, the value of forest ecosystem service function in Yunnan Province reached 982.926×109 yuan yr-1, which is similar to the results of the study by Tu et al. (2023). In addition, the study found that forest land area, population density, forest coverage, and GDP were the main factors affecting the change of value. When studying the value of the ecosystem service function of urban forests, Zhao et al. (2021) found that population, economic factors, and the size of the study area affected the change in value, a finding that is consistent with the conclusions of this study. Due to the availability of data, this study only covers up to 2016, and more research on the recent situation can be carried out in the future. In addition, this paper only assesses the value of forest ecosystem service functions in Yunnan Province, and future research could be expanded to compare the value of Yunnan’s forest ecosystem service functions with those of other provinces as a means of determining Yunnan’s position in the national context.

5 Conclusions and suggestions

5.1 Conclusions

Through the evaluation, it was found that the value of forest ecosystem services in Yunnan Province was as high as 982.926×109 yuan yr-1. Among the various services, the value of carbon fixation and oxygen release is the largest, and the value of forest nutrient fixation is the smallest. The value of forest ecosystem service functions of 16 cities (prefectures) in Yunnan Province was the highest in Pu’er, reaching 144.802×109 yuan yr-1. The service functions of the 16 cities (prefectures) showed obvious heterogeneous changes in space, with Southwest Yunnan (Pu’er City, Lincang, and Xishuangbanna Prefecture), West Yunnan (Baoshan City, Chuxiong Prefecture, Dali Prefecture, Dehong Prefecture, and Nujiang Prefecture), Northwest Yunnan (Lijiang City and Diqing Prefecture), South Yunnan (Honghe Prefecture), Central Yunnan (Kunming City, Yuxi City), Southeast Yunnan (Wenshan Prefecture), East Yunnan (Qujing City), and Northeast Yunnan (Zhaotong City), the value of forest ecosystem services is 259.1×109 yuan yr-1, 315.423×109 yuan yr-1, 149.956×109 yuan yr-1, 57.996×109 yuan yr-1, 79.828×109 yuan yr-1, 44.836×109 yuan yr-1, 49.491×109 yuan yr-1, and 26.295×109 yuan yr-1, respectively. On the whole, the spatial characteristics are mainly “the western and southwestern regions show higher values, followed by the central region, and the eastern and northeastern regions have relatively smaller values”.
Based on this difference, a multiple linear regression model is developed to explore the influencing factors that lead to different values of service functions in each city (prefecture). When performing ordinary least squares regression, the problem of multicollinearity was found, and to eliminate this problem, ridge regression method was used in the paper. It is found that forest land area, population density, forest coverage and GDP are the main influencing factors affecting the differences in the value of service functions, and the degree of influence is in descending order: forest land area > forest coverage > GDP > population density.

5.2 Suggestions

5.2.1 Promote ecological civilization and strengthen the protection of forest resources

The value of forest ecosystem service function in Yunnan Province reached 982.926×109 yuan yr-1, which shows that the value brought by forest resources is huge, so it is necessary to further strengthen the protection of forest resources and improve the quality of forests. The Chinese Government re-emphasized the need to promote green development and harmonious coexistence between human beings and nature. It is necessary to strengthen the protection of forest land and the management of forest rights, maintain the stability of forest resources and enhance the concept of sustainable management. At the same time, it is also necessary to insist on both forestry reform and development. On the one hand, it should continue to increase the construction of ecological civilization, and on the other hand, it should actively guide farmers to participate in afforestation and greening, achieve the goal of improving the ecological environment in rural areas, and put the building of an ecological civilization in a more prominent position, so that the value of forest ecosystems can be stabilized and continue to rise.

5.2.2 Strengthening public awareness of ecological and environmental protection and providing ecological and environmental awareness

With the increase of population density, the impact of human behavior on the ecosystem will become greater, which will lead to a decline in the value of forest ecosystem service functions. This is because human beings will increase their demand for ecological products in order to meet the needs of survival, and at the same time, land types such as forest land will also be transformed into cultivated land, resulting in a decrease in forest land area and damage to the ecosystem. Therefore, it is necessary to improve the consciousness and initiative of the masses to participate in environmental protection; to strengthen the publicity and education of environmental protection, to create a good atmosphere of public opinion in which the whole society works together to protect the ecological environment, and to raise the public’s awareness of ecological and environmental problems.

5.2.3 Promoting green transformation of industries and weighing economic and ecological development

With the emergence of the concept of solidly promoting green development and harmonious coexistence between human beings and nature proposed by China, the growth of GDP has contributed to the increase in the value of forest ecosystem service functions. In the current period of rapid economic development, how to effectively realize the scientific operation and management of forest resources is also one of the important issues facing China. The Chinese Government emphasized that “we must firmly establish and practice the concept that green mountains are golden mountains, and plan development at the height of the harmonious coexistence of man and nature”. Therefore, while developing the economy, we should manage and use forest resources with the concept of green development, promote the economic and social transformation to greening, accelerate the green development mode, and realize the win-win situation between GDP growth and a better ecological environment.

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