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Journal of Resources and Ecology >

2021 , Vol. 12 >Issue 6: 777 - 790

DOI: https://doi.org/10.5814/j.issn.1674-764x.2021.06.006

Ecological Restoration in Altay Region

Trade-off and Synergy Relationships of Ecosystem Services and Driving Force Analysis based on Land Cover Change in Altay Prefecture

  • LIU Hao ,
  • SHU Chang , * ,
  • ZHOU Tingting ,
  • LIU Peng
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  • Chinese Research Academy of Environmental Sciences, Beijing 100012, China
*SHU Chang, E-mail:

LIU Hao, E-mail:

Received date: 2021-02-14

  Accepted date: 2021-05-06

  Online published: 2021-11-26

Supported by

The National Natural Science Foundation of China(41871196)

The Scientific Research Project in Altay Prefecture, Xinjiang Uygur Autonomous Region of China(2019-529)

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Abstract

Altay Prefecture plays a vital role as an ecological barrier in Northwest China. Studying the ecosystem service value is of great significance for promoting regional green high-quality development and maintaining ecological security. Based on Global ESA land cover data from 2000 to 2015, the trade-off and synergy relationships and driving force factors between ecosystem services in Altay Prefecture were analyzed in this study. The analysis produced four main results. (1) The ecosystem service value in Altay Prefecture continued to increase from 113.521Ⅹ109 yuan in 2000 to 115.777Ⅹ109 yuan in 2015, for an increase of about 1.98%. (2) The distribution of ecosystem service value had obvious spatial agglomeration characteristics, with hot spot areas mainly concentrated in the "two rivers and one lake" and the mountainous areas in the northwest, while the cold spot areas were mainly the forest and grass-covered areas in the northern mountainous areas and within Jimunai County. (3) The trade-off and synergy relationship among ecosystem services was mainly synergistic, with a total of 77.78% of ecosystem service relative relationships showing a significant positive correlation at the 0.01 level. (4) Economic factors and industrial structure are important factors affecting ecosystem service value in Altay Prefecture. Ecosystem service value is positively correlated with per capita GDP and the output value of the tertiary industry, but negatively correlated with the output value of the secondary industry.

Key words： ecosystem service; trade-off and synergy relationships; Altay Prefecture; driving force analysis

Cite this article

LIU Hao , SHU Chang , ZHOU Tingting , LIU Peng . Trade-off and Synergy Relationships of Ecosystem Services and Driving Force Analysis based on Land Cover Change in Altay Prefecture[J]. Journal of Resources and Ecology, 2021 , 12(6) : 777 -790 . DOI: 10.5814/j.issn.1674-764x.2021.06.006

1 Introduction

In the 1970s, Wilson et al. first proposed the concept of ecosystem services, and since then different scholars have conducted in-depth studies on ecosystem services (Wilson and Matthews, 1970; Costanza et al., 1997; Zhao et al., 2006; Xie et al., 2008). Although there are differences in the definitions and classification schemes of ecosystem services among scholars, the basic meaning and connotation is the same, that is, natural ecosystems affect human survival through ecosystem conditions and processes. Ecosystem services show dynamic changes due to the diversity of ecosystem services, the heterogeneity of spatial distributions, and the selectivity of human uses. That is, trade-off/synergy relationships are shown between various ecosystem services (Li et al., 2013; Dai et al., 2015; Liu et al., 2019). It is essential to carry out research on the trade-off/synergy relationships and the driving forces of ecosystem service value (ESV) to clarify the correlation characteristics and gain a comprehensive understanding of various ecosystem services (Zheng et al., 2013).
Although research on the trade-off/synergy relationships between ecosystem services has gradually deepened, the related research is still at the preliminary stage. It is difficult to accurately reflect the interactions between trade-off/synergy relationships of ecosystem services and changes in ecosystem structure due to human activities through trade- off/synergy analysis alone. The driving factors can be effectively identified and the mechanisms of ecosystem service changes can be explored for better ecosystem management by analyzing the driving forces of ecosystem services. The analysis method based on a hotspot analysis tool can identify the spatial distribution characteristics of ESV in order to reveal the spatial differences of ecosystem service supply capacity.
Altay Prefecture is an important ecological barrier and key ecological function area for Xinjiang and even the whole country. It is of great significance to study the characteristics of temporal and spatial changes and trade-off/ synergy relationships between ESV and to identify the main factors affecting the ESV in Altay Prefecture. Such an analysis will also play an important role in studying the impact of human activities on the regional ecosystem, maintaining the living community in the basin with the Irtysh River as the core, and building an ecological security barrier in Northwest China. This paper took Altay Prefecture as the study area and used the revised equivalent table to calculate the ESV in Altay Prefecture from 2000 to 2015 in order to thoroughly explore the relationships of trade-off/synergy and identify the main driving factors of ecosystem services in Altay Prefecture. On this basis, this article used the ESTD model to determine the trade-off/synergy relationships among ecosystem services and identify the main driving factors which affect ecosystem services by constructing a regression model, and to clarify the relationships within the ecosystem and the interactions between humans and ecosystem services. This analysis provides not only a scientific basis for regional ecosystem management and ecological protection planning but also a decision-making reference for the land use structure adjustments.

2 Study area

Altay Prefecture is located in the northwest frontier of China (85°31′36″- 91°04′23″E, 45°00′00″-49°10′45″N), bordering Mongolia in the east, Kazakhstan and Russia in the west and north, respectively. The total length of the frontier line is 1197 km, and the total area is about 1.18×105 km2, accounting for 7.2% of the total area of ​​Xinjiang. The terrain of the region is high in the west and low in the east. The average elevation of the study area is about 1153 m. Altay Prefecture is one of the regions in Xinjiang that is rich in water resources. There are three major rivers in the territory, the Irtysh River, the Ulungur River, and the Jimunai County River. The main types of land use are forests, grassland and bare areas. There are vast deserts in the south, while in the northern mountainous region, there are abundant water resources and tree/grass mixed areas. The climate type belongs to the temperate continental cold climate. The annual average temperature is 3.3 ℃, and the annual average precipitation is about 197.1 mm. At the end of 2019, Altay Prefecture had jurisdiction over 31 townships of six counties and one county-level city, with a total population of about 657300, and the Kazaks population accounts for more than half of the total population. The study area’s annual GDP is 33.916×109 yuan, and the proportion of industrial structure is 16.7:36.1:47.2.
Altay Prefecture is located in the core area of ​​the North Xinjiang Passage of the Silk Road Economic Belt. It is an important strategic point in the northwest frontier of China, and also an important area related to the image of China’s ecological power and ecological rights and interests. As one of the China’s 25 important ecological function areas, the Irtysh River Basin in Altay Prefecture is not only the important ecological barrier of ​​the Belt and Road Initiative core area, but also the core area that directly affects the national ecological security pattern.
Fig. 1 Location and basic features of Altay Prefecture

3 Materials and methodology

3.1 Data sources

The land use data for this study were obtained from the European Space Agency Climate Change Initiative (ESACCI) (http://www.esa-landcover-cci.org/). The land cover dataset has a spatial resolution of 300-m, the time span is 24 years from 1992 to 2015, and it includes 22 land cover types. The dataset has already been verified by independent data, such as ground reference data and alternative sensors. The overall accuracy of this dataset is as high as 74.4% (Tsendbazar et al., 2015; Yang et al., 2017; Li et al., 2018; Meier et al., 2018; Ruan et al., 2019), so it has high data quality and is suitable for land use research on a large spatial scale. The data set has been widely used directly in the study of land use change at regional scales, like the Three Gorges Reservoir Catchment (Hao et al., 2020). In this study, four periods of the dataset were selected, 2000, 2005, 2010, and 2015. Based on references to relevant literature, the reclassification function of ArcMap10.2 software was used to divide the land use into six main categories: Cropland, forest, grassland, wetland, bare areas, and water, which were used to calculate the ESV. The socio-economic data used in this study are from Altay Prefecture Statistical Yearbook (2000-2015), and mainly used to analyze the driving forces of ecosystem services. Other data used in this study include net primary productivity (NPP) data (http://www.resdc.cn/data.aspx?DATAID=204) and vector data (http://www.resdc.cn/data.aspx?DATAID=201) of administrative divisions, which come from the Resource and Environment Science and Data Center supported by Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. The main crop yield per unit area data of China and Xinjiang Uygur Autonomous Region (2000-2015) are from the annual statistics of regions on the website of the National Bureau of Statistics of China (http://www.stats.gov.cn/).

3.2 Research methodology

3.2.1 Estimation of the ESV

The estimation method of ESV was based on the unit area value equivalent factor (equivalent factor method), which has been widely used in the estimation of ESV (Xie et al., 2003). According to the relevant literature, one standard equivalent factor of the terrestrial ecosystem in China was 3406.5 yuan ha‒1 in 2010 (Zhang et al., 2017). By combining the value equivalent factor table of the terrestrial ecosystem in China proposed by Xie et al. (2003) with the ESV of one standard equivalent factor, we can calculate the ESV of Altay Prefecture.
Usually, using the national average of an ecosystem service to directly calculate the regional ESV tends to give a result that deviates from the real value, so it is necessary to revise it. The ESV correction coefficient of cropland was revised based on grain yield per unit area. The ESV correction coefficients of forest and grassland were revised based on the NPP mean values of forest and grassland based on the NPP spatial data in Altay Prefecture and China, respectively (Li, 2015; Xie et al., 2015). The correction coefficients of cropland, forest and grassland are 13.1, 0.09 and 0.22, respectively. Considering that the areas of other land use types are relatively small and the coefficient correction model is complex, the ESV per unit area coefficients of these minor types were not revised in this study. The revised coefficients of ESV per unit area in the study area are shown in Table 1.
Table 1 Coefficients of ESV per unit area in Altay Prefecture(Unit: yuan ha-1 yr-1)
Land use types Regulating services Supporting services Provisioning services Cultural services Total
Gas
regulation		 Climate regulation Water supply Soil
conservation		 Waste disposal Biological diversity Food
production		 Raw material production Aesthetic landscape
Cropland 2231.26 3971.64 2677.51 6515.27 7318.52 3168.39 4462.52 446.25 44.63 30835.98
Forest 1073.05 827.78 981.07 1195.68 401.63 999.47 30.66 797.12 392.43 6698.88
Grassland 599.54 674.49 599.54 1461.39 981.75 816.88 224.83 37.47 29.98 5425.87
Wetland 6131.70 58251.15 52800.75 5825.12 61930.17 8516.25 1021.95 238.46 18906.08 213621.62
Bare areas 0 0 102.20 68.13 34.07 1158.21 34.07 0 34.07 1430.73
Water 0 1566.99 69424.47 34.07 61930.17 8482.19 340.65 34.07 14784.21 156596.81
Based on the value coefficients of ecosystem services per unit area in Altay Prefecture, the study area’s ESV can be calculated. The calculation formula of ESV is expressed as follows:
$ESV=\underset{k}{\mathop \sum }\,\underset{j}{\mathop \sum }\,\left( {{A}_{k}}\times V{{C}_{jk}} \right)$
where ESV is the total value of ecosystem service in Altay Prefecture (yuan); Ak refers to the area of land use type k (ha); and VCjk is the value coefficient for land use type k with the ecosystem service function j (yuan ha‒1).

3.2.2 Hotspot analysis

Hotspot analysis can identify statistically significant spatial clusters of high values (hot spots) and low values (cold spots). The hotspot analysis tool (Getis-Ord Gi) of ArcMap10.2 software was used to calculate and analyze the spatial agglomeration characteristics of ESVs at the grid-scale (Wang et al., 2019; Fu et al., 2020). The calculation formula is expressed as follows:
$G_{i}^{*}=\frac{\sum\limits_{j=1}^{n}{{{w}_{i,j}}{{x}_{j}}}-\bar{x}\sum\limits_{j=1}^{n}{{{w}_{i,j}}}}{S\sqrt{\frac{n\sum\limits_{j=1}^{n}{w_{i,j}^{2}}-{{\left( \sum\limits_{j=1}^{n}{{{w}_{i,j}}} \right)}^{2}}}{n-1}}}$
where xj is the attribute value of element j; wi,j is the spatial weight between elements i and j; $\bar{x}$ and S are the mean value and standard deviation of the element, respectively; and n is the number of elements. In this way, z-scores and P-values are used to judge whether the elements have complete spatial randomness or not.

3.2.3 Correlation analysis

Correlation analysis can quantitatively describe the degree of linear correlation and clarify the correlation direction between two continuous variables (Stow et al., 2003; Guo et al., 2017). In this study, it was used to analyze the correlations between ecosystem services. The calculation formula is expressed as follows:
${{R}_{xy}}=\frac{\sum\limits_{i=1}^{n}{\left( {{x}_{i}}-\bar{x} \right)\left( y-\bar{y} \right)}}{\sqrt{\sum\limits_{i=1}^{n}{{{\left( {{x}_{i}}-\bar{x} \right)}^{2}}}}\sqrt{\sum\limits_{i=1}^{n}{{{\left( {{y}_{i}}-\bar{y} \right)}^{2}}}}}$
where Rxy is a correlation coefficient and ranges from 1 to ‒1, where 1 indicates a perfect positive relationship, ‒1 indicates a perfect negative relationship, and 0 indicates no relationship exists between the two variables. If the value is near ± 1, then it said to be a perfect correlation: as one variable increases, the other variable tends to also increase (if positive) or to decrease (if negative); n is the number of samples; xi and yi are the individual values indexed with i; and $\bar{x}$ and $\bar{y}$ are the average values of variables x and y, respectively.

3.2.4 Trade-off and synergy relationship analysis

In this study, the ecosystem services trade-off degree (ESTD) model was used to quantitatively evaluate the relationships between ecosystem services (Liu et al., 2018; Gao et al., 2019; Lan et al., 2020). ESTD is a method based on the linear fitting of data to reflect the direction and degree of interaction among ecosystem services. It aims to evaluate the interaction of ecosystem service change as a whole in the study area. The calculation formula of ESTD is expressed as follows:
$EST{{D}_{ij}}=\frac{ES{{C}_{ib}}-ES{{C}_{ia}}}{ES{{C}_{jb}}-ES{{C}_{ja}}}$
where ESTDij is the ecosystem service trade-off degree between ecosystem services i and j; and ESCjb and ESCja are the values of ecosystem services with j at the times b and a, respectively. ESTD represents the balance and direction of the interaction between the two ecosystem services. When ESTD is negative, the relationship between them is a trade-off; when ESTD is positive, the relationship between them is synergy. The absolute value of ESTD represents the degree of change in the ecosystem service of type i compared with that of type j.

3.2.5 Driving factors analysis

The driving factors of spatiotemporal differences of ESV mainly include natural and human factors (Zhu et al., 2019; Cheng et al., 2021). The change of ESV in a short period is mainly affected by human activities (Yao et al., 2009; Wang et al., 2014; Luo et al., 2018; Han et al., 2019; Xu et al., 2019). Since the meteorological data in the study area are classified data, the climate factors were ignored and only the impact of human factors on ESV was included in this study. Altay Prefecture has abundant natural and tourism resources, with tourism, agriculture, and pastoral industry representing the mainstay industries in the study area. Consequently, after referring to relevant literature and considering the availability of data, we selected a total of 25 driving factors, including population, economic, industrial structure, tourism, and agriculture, for correlation analysis.
Table 2 Driving force factors of ESV’s change
Driving factors Influence factors
Population factors Total population (X1), urban population (X2), population density (X3), natural growth rate of population (X4), urbanization rate (X5)
Economic factors Gross domestic product (X6), per capita GDP (X7), the output value of primary industry (X8), the output value of secondary industry (X9), the output value of tertiary industry (X10), the gross Industrial Value (X11), government revenue (X12), average annual salary of employees (X13), per capita income of rural households (X14)
Industrial structure factors The proportion of primary industry (X15), the proportion of secondary industry (X16), the proportion of tertiary industry (X17)
Tourism factors Number of domestic visitors (X18), tourism earnings (X19)
Agricultural factors Crop yields (X20), number of livestock (X21), the proportion of gross agricultural value (X22), the proportion of gross forestry value (X23), the proportion of gross pastoral value (X24), the proportion of gross fishery value (X25)

4 Results and analysis

4.1 Analysis of land use changes

Based on the land use data in 2000 and 2015, this paper analyzes the land use changes in Altay Prefecture during the past 15 years. The spatial distribution of land use change in Altay Prefecture (Fig. 2) is mainly concentrated in the north-central part of Altay Prefecture and distributed around the Irtysh River Basin. There are two main directions of land use transfer changes. The first is the transformation of bare areas and other unused lands into ecological land, which mainly includes grassland (212.13 km2), cropland (440.71 km2) and water (39.08 km2). This change is mainly distributed in the southern part of Habahe County, Burjin County, Altay City and Jimunai County, and the middle part of Fuhai County, Fuyun County and Qinghe County. The second is the transformation of ecological land to bare areas, which mainly refers to the degradation of forest. In the past 15 years, a total of 414.46 km2 of forest has been transformed into bare areas, mainly in Fuhai County. This finding is consistent with the actual situation. The relevant data from the website of Fuhai County People’s government show that the area of land desertification was expanding, and the natural forest resources in the valley were shrinking. Fuhai County has been facing serious ecological problems in recent years.
Fig. 2 Spatial variation of land use in Altay Prefecture from 2000 to 2015
The land use transfer matrix (Table 3) shows that the water in Altay Prefecture has increased. This is mainly due to the rapid population growth and the gradual increase in urbanization in Altay Prefecture in recent years. Meanwhile, industry, agriculture and domestic water demand for water resources increased. To strengthen the development, utilization and management of water resources, human societies have vigorously developed water conservancy projects. Through the construction of dams, reservoirs, hydropower stations, diversion channels and other measures to regulate the spatial distribution of water resources, the lake water level rises and the area increases, which leads to the conversion of unused land into water area. For example, the project of “diverting water from the Irtysh River to the sea” has gradually changed the Ulungur Lake from a natural lake to an artificial lake, and formed several small lakes in the northeastern corner of the lake.
As for the wetland ecosystem, Altay Prefecture has realized the importance of the wetland ecosystem and has continued to carry out wetland ecological protection and restoration projects. In 2001, the Altay Kekesu Wetland Nature Reserve was established, and in 2005, the Irtysh River Koktoghay Wetland Nature Reserve was established. The ecological restoration of wetlands has been strengthened through measures such as ecological water replenishment. Therefore, the area of wetlands in Altay Prefecture has been restored in recent years. At the same time, Altay Prefecture actively responded to the national policy and continued to implement the project of returning grazing land to grassland. The pressure on the natural grassland was alleviated, and the degradation of the natural grassland was restrained by the banning of grazing, and by the use of resting grazing and zoning rotation grazing. The result of these measures is that the grassland area increased significantly, the grassland resources were used sustainably, and the productivity and ecological function of the grassland was restored and enhanced significantly. In Altay Prefecture, forest decreased by 323 km2 over the 15 years. The main reason is that the development of agriculture, animal husbandry and other industries in the two river basins of the Ulungur River and the Irtysh River in Altay Prefecture have caused serious deforestation of the natural forests. Moreover, under natural conditions, the regeneration capacity of natural forests is low, which leads to a low proportion of young, middle and nearly mature forests in natural forests, and the age structure of natural forests is gradually aging.
Table 3 Land use transition matrix in Altay Prefecture from 2000 to 2015 (Unit: km2)
2015 2000
Grassland Bare areas Cropland Forest Wetland Water Sum
Grassland 24076.05 212.13 109.75 167.38 0.11 0.96 24566.38
Bare areas 49.53 60669.84 83.79 414.46 1.40 0.67 61219.67
Cropland 183.30 440.71 9934.19 128.13 5.71 1.88 10693.93
Forest 82.77 346.15 63.76 17591.30 1.68 4.10 18089.75
Wetland 0.20 1.05 4.21 3.06 802.25 3.86 814.62
Water 1.61 39.08 1.34 19.63 0.01 1884.45 1946.12
Sum 24393.45 61708.96 10197.05 18323.95 811.16 1895.91 117332.50

4.2 Analysis of ESV

4.2.1 Time characteristics

Based on the revised equivalent factor table, the total ESV and the individual ESVs of various land use types in Altay Prefecture from 2000 to 2015 were calculated (Fig. 3). From 2000 to 2015, the ESV of Altay Prefecture increased from 113.521×109 yuan to 115.777×109 yuan, for an increase of 2.26×109 yuan or 1.98%, within which the fastest growth rate was 0.95% from 2010 to 2015.
Fig. 3 ESVs of different land use types in Altay Prefecture from 2000 to 2015
From the perspective of ESVs of land use in Altay Prefecture, the ESVs of cropland and water accounted for the highest proportions. In 2015, the ESVs of cropland and water were 35.081×109 yuan and 30.651×109 yuan, accounting for 28.57% and 24.7%, respectively. While the ESV of bare areas was only 8.749×109 yuan, accounting for 7.56%. Other ESVs from largest to smallest were 17.625×109 yuan for wetland, 13.510×109 yuan for grassland, and 12.261×109 yuan for forest, accounting for 15.22%, 11.67%, and 10.50% of the total value of regional ESV, respectively. Except for forest land and bare areas, the ESVs of other land use types have shown increasing trends.
From the perspective of various types of ecosystem services, supporting services constitute the main part of the ESV in Altay Prefecture. The value of supporting services in 2015 was 58.861×109 yuan, accounting for 50.84% ​​of the total value of regional ESV. The second largest was regulating services. In 2015, its value was 43.612×109 yuan, accounting for 37.67% of the total value of regional ecosystem services, while the values of provisioning services and cultural services were relatively small. In 2015, the value of provisioning services was 7.808×109 yuan, and the value of cultural services was 5.497×109 yuan, accounting for only 6.74% and 4.75% of the total value, respectively. The values of various ecosystem services had increased, but there were certain differences in the growth rates. Compared with other types of ecosystem services, the value of regulating services had increased the most, with an average annual increase of 77×106 yuan. In contrast, the value of cultural services showed the smallest increase, with an average annual growth of only 5×106 yuan. The values of provisioning services and supporting services increased by averages of 53×106 yuan and 15×106 yuan each year, respectively.
Fig. 4 ESVs of various ecosystem services in Altay Prefecture from 2000 to 2015

4.2.2 Spatial characteristics

The ESV was divided into six spatial categories. As shown in Fig. 5, on the whole, ESV presents a characteristic spatial distribution of high in the north, low in the south, and prominent in the middle. The areas with high ESV in Altay Prefecture are mainly distributed in the Ulungur Lake, the middle and lower reaches of the Ulungur River, and the lower reaches of the Irtysh River; while the land use types in the southern region are mainly bare areas, which are areas with low ESV. From the perspective of administrative divisions, the proportions of cultivated land and waters in Altay City, Fuhai County, and Burqin County are relatively large. Therefore, the ESV is mainly distributed in this part of the area. While the land use types in Qinghe County and Jimunai County are mainly bare areas, there are many areas with a low value of ecosystem services, so the ESV in these regions is small.
Fig. 5 The temporal and spatial distribution of ESV in Altay Prefecture from 2000 to 2015
Figure 6 shows the results of hotspot analysis of ESV in Altay Prefecture from 2000 to 2015. There is an obvious spatial agglomeration of ESV, and the most significant hot spot cluster areas are mainly distributed in the lower reaches of the Ulungur River, the Ulungur Lake and the lower reaches of the Irtysh River in the middle of Altay Prefecture, including the northern part of Fuhai County, the southern part of Altay City and the southern and northern parts of Habahe County and Habahe County. The most significant cold spot cluster areas are mainly distributed in the north and southwest of Altay Prefecture, including the north of Qinghe County, the north of Fuyun County, the north of Altay, Burqin County, the middle of Habahe County and most of Jimunai County. The most significant hot spot cluster areas are mainly cultivated land and water areas, such as the delta agricultural area in the lower reaches of the Ulungur River, the valley basin agricultural area in the lower reaches of the Irtysh River and the Ulungur Lake. In the north of Habahe County and Burqin County, because of the distribution of Kanas Lake and other lakes, this area is also a very significant hot spot cluster area. The most significant cold spot cluster area is mainly covered by woodland and grassland. In contrast, the non-significant cold spot cluster area is covered by bare areas, mainly concentrated in the southern desert area of Altay Prefecture.
Fig. 6 The cold and hot spot map of ESV in Altay Prefecture from 2000 to 2015
On the whole, the ESV of Altay Prefecture is mainly non-significant, with the non-significant areas accounting for more than half of the total area, while the area of extremely significant cold spots is significantly larger than that of extremely significant hot spots. From 2000 to 2015, the proportion of extremely significant hot spot area decreased from 16.73% to 14.80%. However, the proportions of hot spot area and significant hot spot area increased significantly, from 0.07% and 0.34% in 2000 to 0.21% and 2.33% in 2015, respectively, while the proportion of non-significant area was stable. To sum up, the ESV in Altay Prefecture shows a trend of growth.

4.3 Driving factor analysis of ESV

4.3.1 Correlations between ESVs

Based on the estimation of the ESV in Altay Prefecture, the correlations among the nine ecosystem services were obtained according to the correlation analysis. As shown in Table 4, except for diagonal elements and repeated relationships, there were 36 groups of effective relative relationships among the ecosystem services in Altay Prefecture, and the correlation coefficients are all positive. The results show that the relationships among the ecosystem services in Altay Prefecture are all positively correlated with each other, which is the dominant type of relationship among ecosystem services during the period from 2000 to 2015. Among them, 28 groups were significantly positively correlated at the 0.01 level, and two groups were significantly positively correlated at the 0.05 level.
On the whole, except for raw material provision service in provisioning services, the remaining ecosystem services are well correlated with each other, and the correlation coefficients are all above 0.9. The correlation coefficients between raw material provision service and gas regulation service and aesthetic landscape are 0.506 and 0.502, respectively, and their significance levels are all above 0.05. The correlation coefficients between raw material provision service and other ecosystem services are all lower than 0.45, indicating that raw material provision services have poor correlations with the other ecosystem services.
Table 4 Correlations between different ecosystem services in Altay Prefecture
Pearson correlation Regulating services Supporting services Provisioning services Cultural services
Gas
regulation		 Climate regulation Water supply Soil
conservation		 Waste disposal Biological diversity Food
production		 Raw material production Aesthetic landscape
Regulating services Gas regulation 1 0.993** 0.962** 0.994** 0.980** 0.980** 0.988** 0.506* 0.932**
Climate regulation 1 0.967** 0.996** 0.986** 0.991** 0.994** 0.441 0.936**
Water supply 1 0.962** 0.995** 0.984** 0.961** 0.451 0.992**
Supporting services Soil conservation 1 0.984** 0.989** 0.998** 0.414 0.924**
Waste disposal 1 0.996** 0.984** 0.421 0.974**
Biological diversity 1 0.993** 0.403 0.957**
Provisioning services Food production 1 0.382 0.921**
Raw material
production		 1 0.502*
Cultural services Aesthetic landscape 1

Note:* means significance at P<0.05 level (both sides); **means significance at P<0.01 level (both sides).

4.3.2 Trade-off and synergy relationships of ecosystem services

From 2000 to 2005, there were 36 sets of values among the trade-off and synergy relationships of ecosystem services in Altay Prefecture, of which eight sets were negative values, 28 sets were positive values. The synergy relationships accounted for 77.78% of the total, indicating that synergy was the dominant relationship among ecosystem services in Altay Prefecture during that time period. The trade-off relationship is mainly reflected in the raw material provision and other ecosystem services. The trade-off degree between raw material provision and waste disposal is the highest (‒153.80), and the trade-off degree between raw material provision and aesthetic landscape is the lowest (-0.10). The synergy degree of the ecosystem services presents a relatively stable trend. The synergy degree between waste disposal and aesthetic landscape is the highest (15.09), and the synergy degree between gas regulation and waste disposal is the lowest (0.15).
Fig. 7 Trade-off and synergy relationships between different ecosystem services from 2000 to 2005
In the next time period, from 2005 to 2010, there were 36 sets of values among trade-off and synergy relationships of ecosystem services in Altay Prefecture, all of which are positive, indicating that synergy remained the dominant relationship among ecosystem services in Altay Prefecture. The trade-off relationships between raw materials provision and other ecosystem services was also gradually manifested as a synergy relationship. The synergy degrees between raw materials provision and waste disposal, soil conservation, water supply are relatively high, at 231.43, 142.71, and 127.48, respectively. The synergy degree between raw materials provision and aesthetic landscape is the lowest (0.08), indicating that the ecosystem services in Altay Prefecture had gradually transformed into a relationship of interdependence and mutual promotion.
Fig. 8 Trade-off and synergy relationships between various ecosystem services from 2005 to 2010
In the final time period, from 2010 to 2015, there were 36 sets of values among trade-off and synergy relationships of ecosystem services in Altay Prefecture, all of which are positive, indicating that synergy remained the dominant relationship among ecosystem services in Altay Prefecture during that period. The synergy degree between raw material provision and waste disposal is the highest (74.30), but compared with the changes from 2005 to 2010, the synergy degree had decreased significantly. The synergy degree between raw material provision and aesthetic landscape is the lowest (0.09), increasing from 2005 to 2010.

4.3.3 Definition of driving force factors of ESV

The bivariate analysis tool in SPSS 25.0 software was used to perform pairwise correlation analysis on ESV and driving factors, and preliminary screening of driving factors was based on correlation coefficients and significance levels. The pairwise correlation analysis results are shown in Table 5. Due to the poor correlations of urban population, the natural growth rate of population, urbanization rate, the proportion of the secondary industry, the proportion of the tertiary industry, number of livestock, the gross forestry value and the gross fishery value, these eight factors were excluded from this study and they were not used in the driving force analysis.
Fig. 9 Trade-off and synergy relationships between various ecosystem services from 2010 to 2015
Table 5 Pearson correlations between ESVs and driving force factors
Variations X1 X2 X3 X4 X5
Correlation coefficient 0.908 0.164 0.796 0.409 ‒0.187
Variations X6 X7 X8 X9 X10
Correlation coefficient 0.985* 0.995** 0.994** 0.959* 0.983*
Variations X11 X12 X13 X14 X15
Correlation coefficient 0.847 0.971* 0.990* 0.981* ‒0.907
Variations X16 X17 X18 X19 X20
Correlation coefficient 0.671 0.309 0.971* 0.982* 0.939
Variations X21 X22 X23 X24 X25
Correlation coefficient ‒0.306 0.991* 0.477 ‒0.964* ‒0.038

Note:* means significance at P<0.05 level (both sides); **means significance at P<0.01 level (both sides).

This study used the stepwise regression analysis method to analyze the relationships between the driving factors and the total value of ecosystem services and the values of various ecosystem services to determine the linear relationships between the independent variables and the dependent variables and construct the regression model (Zhou et al., 2020). The results are shown in Table 6. There is a positive correlation between the change of ESV and per capita GDP, which indicates that the socio-economic structure has an impact on the ESV in the study area. The same relationship is seen with the total value of ecosystem services, as per capita GDP is the first and only driving factor of regulating service value and supporting service value. Income per capita of rural households is the first and only driving factor of cultural service value. The first driving factor of provisioning service value is the proportion of agricultural output value. The second driving factor is the total output value of the secondary industry, which has a significant negative correlation. The third driving factor is the output value of the tertiary industry.
Table 6 Regression models of various ecosystem services in Altay Prefecture
ESV
(×107 yuan)		 Regression equation
Regulating service value $y=0.001{{x}_{7}}+425.679$
Supporting service value $y=0.001{{x}_{7}}+573.770$
Provisioning service value $y=0.193{{x}_{22}}-7.579\times {{10}^{-7}}{{x}_{9}}+1.453\times {{10}^{-9}}{{x}_{10}}+68.502$
Cultural service value $y=8.665\times {{10}^{-5}}{{x}_{14}}+54.045$
Total value $y=0.002{{x}_{7}}+1128.635$
By analyzing the total value of ecosystem services and the values of various types of ecosystem services, the per capita GDP is found to be the first crucial driving factor of the total value of ecosystem services, regulating service value and supporting service value. This fact shows that these ESVs are affected by regional economic development in the study area. Regarding the provisioning service value, in addition to the proportion of agricultural output value, it is also related to the output value of the secondary industry and the tertiary industry. This reflects the influence of the type of regional industrial structure on provisioning services. Industrial development and resource development are closely related, and different industries rely on natural resources to varying degrees. When the total output value of the secondary industry is higher, this means that the secondary industry is the pillar industry of the regional economic development structure. The economic development based on the secondary industry has a higher degree of natural resource development and a more significant impact on the land use structure. Simultaneously, this system causes damage to the regional ecosystem, which will lead to the reduction of the area of ecological land such as forest and grass, the decline of supply capacity, etc. In contrast, the tertiary industry's development is less dependent on natural resources, so the structure and function of the ecosystem will be less affected. Therefore, the proportion of agricultural value and the output value of the tertiary industry are positively correlated with provisioning service value, while the output value of the industry is negatively correlated with provisioning service value. Per capita income of rural households can describe and reflect the living standards and social welfare of the people to a certain extent. Therefore, when the per capita income of rural households is higher, the demand for cultural services increases, so the cultural service value is improved.

5 Conclusions and discussion

Based on a long time series of land cover data, this paper used grain yield per unit area data and NPP data to modify the equivalent table and comprehensively analyze the spatial and temporal characteristics of ESV. The correlation analysis method, which is dominated by long-term overall analysis, and the synergy degree of ecosystem service trade-offs, which is dominated by short-term dynamic change analysis, were used to analyze the trade-offs/synergies between ecosystem services. The main driving factors of ESV changes were revealed through stepwise regression analysis. This analysis revealed four main conclusions.
(1) The composition of ESV in Altay Prefecture is mainly based on supporting services, which is the main component of ESV in Altay Prefecture. From 2000 to 2015, the ESV in Altay Prefecture showed a trend of continuous growth. The total value of ecosystem services increased from 1.135×1011 yuan in 2000 to 1.158×1011 yuan, for an annual increase of 1.98%.
(2) There is an obvious spatial agglomeration of ESV in Altay Prefecture. The extremely prominent hotspot clusters are mainly distributed in Ulungur Lake, the lower reaches of the Ulungur River and the Irtysh River in the central Altay Prefecture, and the Kanas National Natural Reserve in the north of the basin. The extremely significant cold spot clusters are mainly concentrated in the areas with high vegetation coverage, such as forest and grassland on the southern slope of Altay Mountain.
(3) From 2000 to 2015, all the ecosystem services in Altay Prefecture showed synergistic relationships that benefited each other, and this was the dominant relationship among ecosystem services in Altay Prefecture. Except for raw material service in provisioning services, the other ecosystem services are well correlated with other ecosystem services, and the correlation coefficients are all above 0.9. These strong correlations indicate that under the influence of external factors such as land use change and human disturbance, two or more ecosystem services in Altay Prefecture are in a synergistic relationship.
(4) ESV in Altay Prefecture is mainly related to economic factors. Regulating service value and supporting service value are all related to per capita GDP. The supporting service value is primarily related to the proportion of gross agricultural value. The output values of the secondary industry and the tertiary industry are also two of the driving factors affecting the value of support services. Cultural services are mainly related to per capita income of rural households.
At present, ecosystem service evaluation methods mainly include the physical evaluation method and the value evaluation method, which evaluate ecosystem services from the perspectives of material quality and value quantity, respectively. Fu et al. (2016) carried out the evaluation of ecosystem services in Altay Prefecture in 2010 based on the physical evaluation method. However, due to the complexity of this method and the difficulty of data acquisition, it is difficult to carry out long-term dynamic research on regional ecosystem services. Kong et al. and Xiong carried out the evaluation of ecosystem services in Altay Prefecture based on the value evaluation method (Kong et al., 2009; Xiong, 2018). However, these studies also have shortcomings. On the one hand, the equivalent factor table had not been revised locally, and on the other hand, the driving factors affecting ESV had not been analyzed. Therefore, this study can be used as a supplementary study of ESV in Altay Prefecture. Based on the research results, three suggestions are put forward to promote ecological conservation and restoration in Altay Prefecture.
(1) Take comprehensive measures to improve the ecology of river basins. More efforts should be made to return cropland to forest or grassland, to expand the area of ecological space, and to strengthen the construction and maintenance of the forest. At the same time, additional efforts need to strengthen the ecological restoration and management of the river valley and oasis, and restore the ecology of forest, grass, river and lake.
(2) Make reasonable plans for urbanization according to local conditions. In the process of urbanization construction, the government should carry out rational planning gradually, and avoid the urbanization construction without any plan or purpose. The plan needs to control the spread of construction land, avoid the problems of a low industrial level in urban expansion, promote the reuse and renovation of existing construction land, and reduce the destruction of ecological land due to development activities.
(3) Improve the quality and efficiency of industries and accelerate the adjustment and optimization of industrial structure. Given the progress of ecological protection and restoration and the industrial structure characteristics in Altay Prefecture, it is essential to eliminate backward industries and support green industries to reduce the proportion of industrial output value. At the same time, it is necessary to vigorously develop the tertiary industry that relies on the advantages of the resource endowment to promote economic growth and realize the continuous improvement of ESV.

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