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Article

Eco-Asset Variations and Their Driving Factors in the Qinghai–Tibet Plateau, China, under the Context of Global Change

by
Xingming Yuan
1,2,
Bing Guo
2,3,*,
Miao Lu
4,*,
Wenqian Zang
5,6,
Chuan Liu
6,
Baoyu Wang
6 and
Xiangzhi Huang
5,6
1
School of Architecture and Information Engineering, Shandong Vocational College of Industry, Zibo 256414, China
2
School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
3
Key Laboratory of National Geographic Census and Monitoring, Ministry of Natural Resources, Wuhan 430072, China
4
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
5
Research Institute of Aerospace Information, Chinese Academy of Sciences, Beijing 100101, China
6
Langfang Research and Development Center for Spatial Information Technology, Langfang 065000, China
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(9), 7466; https://doi.org/10.3390/su15097466
Submission received: 25 February 2023 / Revised: 27 April 2023 / Accepted: 27 April 2023 / Published: 1 May 2023
(This article belongs to the Special Issue Climate Change and Enviromental Disaster)
Browse Figures
Review Reports Versions Notes

Abstract

:
The Qinghai–Tibet plateau (QTP), as the “roof of the world” and the “Asian Water Tower”, provides important ecological resources for China and other Asian countries. The changing trend of ecological assets and their dominant influencing factors in different sub-regions and periods are not yet clear. In order to reveal the differences in driving mechanisms among sub-regions under the context of global changes, this study quantitatively analyzed the ecological assets and their spatial and temporal evolution patterns during 2000–2015 by using the value equivalent method. Then, the Geodetector was introduced to reveal and clarify the dominant factors of ecological asset changes in different ecological sub-regions. The results show the following. (1) From 2000 to 2010, the total value of ecological assets in Nakchu County was the highest, followed by Kangding County, while that in 2015 was the highest in Kangding County, followed by Nakchu County. (2) During 2000–2015, the average value of ecological assets of the Qinghai–Tibet plateau gradually decreased from east to west, while the average ecological asset value in the southern Qinghai–Tibet plateau was lower. (3) The QTP showed the highest value in 2005 with an increasing trend from 2000 to 2005, followed by a subsequent decrease from 2005 to 2015. (4) Between 2000 and 2015, the area of the stable zone (slight or no change) of ecological assets was the largest, followed by that of the decreasing zone. (5) During all the study period, the spatio-temporal evolution of ecological assets in different ecological sub-regions was mainly affected by natural factors, which were the main driving variables rather than human activities. These results could provide important support for decisions regarding the protection of ecosystems and resources in the Qinghai–Tibet plateau.

1. Introduction

The ecological assets are defined as the value of tangible and invisible service functions provided by different ecosystem types in a specific region, which will differ with the change in ecosystem type, area, and quality in the region. Moreover, it will also show differences with the dynamic changes in spatial location and time [1]. Since the 20th century, the rapid growth of the global economy has increased the consumption of resources [2], and the imbalance between the demand for ecological assets for the purposes of regional economic development and ecological supply has become more evident [3]. Recently, experts and scholars in China and other countries have begun to assess the value of natural resources and the human well-being they produce [4]. With continuous research on the mechanisms of sustainable development, the value assessment of natural resources, ecological assets estimation, and ecosystem services have attracted widespread attention from researchers all over the world [5]. Ecological asset refers to a natural resource that provides services and goods to humans [6]. Nowadays, the analysis and evaluation of the value of ecological resources have become important issues in ecology and economics [7]. It is not only the material basis for human survival and development but also a necessary wprerequisite for a national economic accounting system, regional ecological security evaluation, and designated ecological environment compensation policy [8].
Scholars have carried out long-term and systematic research on the quantification of the value assessment of ecological services [9]. Limited by the research methods, such as index quantification, value conversion, and the systematic evaluation index system [10], the evaluation accuracy and objectivity of ecological assets are often affected [11]. With the widespread application of remote sensing and geographic information system technology, an increasing number of studies have been conducted on ecological asset assessment at different scales [12]. According to Costanza’s evaluation index system, Chen et al. [13] evaluated the total value of terrestrial and marine ecosystems in China and found that the total value of terrestrial ecosystems and marine ecosystems was approximately Chinese Yuan (CNY) 56,098.44 billion and CNY 21,736.04 billion, respectively. Xin et al. [14] applied the shadow project method and conditional value method to investigate the ecological assets of Panjin wetland in Liaohe Delta. Xu et al. [15] utilized the method of environmental economics to calculate the ecosystem value of Guangzhou. Shi et al. [16] established a set of standards and specifications for remote sensing monitoring of ecological assets by using 3S technology and a field sampling survey. Pan et al. [17] used National Oceanic and Atmospheric Administration/advanced very-high-resolution radiometer (NOAA/AVHRR) data to analyze the value of terrestrial ecosystem ecological assets in China by means of remote sensing, and then explored the spatial patterns of the ecological assets in China. Zhou et al. [18] combined remote sensing and GIS technology to evaluate the ecological assets in arid areas. Based on the assessment of ecological parameters, Zhu et al. [19] measured the value of terrestrial ecosystem services in China by establishing an ecological asset calculation model. Xu et al. [20] simulated and analyzed the spatio-temporal variations of ecological assets in the Yangtze River Delta by combining remote sensing and economic evaluation methods. Jiang et al. [21] investigated the changes to ecological assets in Beijing–Tianjin–Hebei by calculating the changes in ecological product supply value, ecological footprint, ecological carrying capacity and ecological deficit. Zhang et al. [22] applied improved remote sensing inversion and a value system of key parameters of terrestrial ecosystems to construct a quantitative remote sensing inversion model of watershed ecological assets, and then comprehensively evaluated the spatio-temporal changes of ecological assets in Changdang Lake Basin from 2000 to 2015. Li et al. [23] constructed an ecological asset value measurement model to calculate the ecological asset value and quantitatively analyzed its spatial distribution pattern in Gansu Province in 2000 and 2010. Bai et al. [24] analyzed the connotation, correlation, and accounting system of ecological assets and GEP of Yunnan Province, and then evaluated the status of ecological assets in 2010. However, the above studies were mostly conducted using ecological asset evaluation methods to examine the spatio-temporal change patterns of ecological assets. Research on the quantitative analysis of the driving mechanisms of ecological asset changes are relatively scarce. At the same time, global change, climate warming, and human activities have become the dominant factors affecting the evolutionary patterns of regional ecological assets [25]. Due to the special geographical location of the Qinghai–Tibet plateau (QTP) with high altitude, cold climate, and scarcity of data, the driving mechanisms of ecological assets are still not clear. Ji et al. [26] explored the variation characteristics of the normalized difference vegetation index and its response to driving factors in the grassland of the Qinghai–Tibet plateau. Zhang et al. [27] measured the overall characteristics of settlement evolution in Naqu County and discussed its driving mechanism. Qi et al. [28] used GIS technology to quantitatively evaluate the ecological service function of the QTP. In the context of global change, the dominant factors affecting the spatial and temporal evolution of ecological assets in different regions of the QTP are different and have changed to some extent. However, the differences in the dominant factors across different regions along the last 20 years are still unclear and need to be further studied. The QTP is the indicator and amplifier of ecological environment change [29], and small fluctuations in climate can greatly affect the plateau ecosystem [30], thus changing the pattern and process of this region.
This study quantitatively evaluated and analyzed the ecological assets and their spatial and temporal evolution patterns on the QTP during 2000–2015, and then introduced Geodetector to reveal and clarify the dominant factors of ecological asset changes in different ecological sub-regions. Through the monitoring of ecological assets of the QTP, we can find out the category, quantity, nature, and spatial distribution of ecological assets. This aims to provide a basis for decision making regarding the rational utilization of resources, the improvement of the ecological environment and its governance [31].

2. Materials and Methods

2.1. Study Area

The QTP is located in the southern part of the Asian continent, with a total area of about 2.62 million km2, including Tibet, Sichuan, Qinghai, Gansu, Yunnan, and Xinjiang, provinces (Figure 1). The average altitude of the plateau is more than 4000 m [32], with an overall decreasing trend from northwest to southeast. The landforms are complex and diverse, mainly being composed of plateaus, basins, and mountains [33]. The vegetation species are complex and rich, and mainly include alpine grassland and meadow species. The average temperature of the plateau is low. The spatial differentiation of precipitation is obvious, from 2000 mm in the southeastern Shannan area to below 25 mm in the northwestern Qaidam Basin [34]. Plateau lakes, glaciers, wetlands, and marshes are widely distributed, providing an important load for national water resource storage [35].

2.2. Data Source and Preprocessing

The vegetation coverage data were derived from the MOD13Q1 NDVI product, with the spatial and temporal resolution of 16 days and 250 m, respectively. The resources were available free from the National Aeronautics and Space Administration’s (NASA’s). The MRT software [36] was used to perform data splicing, re-projection, and format conversion. Based on the dataset, the annual maximum vegetation NDVI data of the region were obtained, with a spatial resolution of 1000 m. Terrain data (90 m) were derived from the SRTM3 data obtained by the joint measurement of NASA and the National Bureau of Surveying and Mapping of the Department of Defense [37], with the elevation ranging from −12 km to 9 km. The ArcGIS 10.7 software [38] was applied to resample the data into 1000-m-resolution grids and then obtain the data for slope and aspect. The meteorological data were available at https://data.cma.cn/ with the file format of ‘.txt’. The data mainly included daily precipitation, daily average temperature, daily light hours, and other elements. The C# programming language was utilized to calculate the annual average temperature, annual total precipitation, >10 °C accumulated temperature, and annual sunshine hours. The Kriging interpolation method [39] was used to obtain the grid data with the spatial resolution of 1000 m and the Krasovaky_1940 _ Albers projection type [40]. China’s basic geographic data, gross domestic grid data, and population density grid data were available at https://www.resdc.cn/. The land-use data were available at https://www.resdc.cn/, with a 100-m spatial resolution and an overall data accuracy of 97.15%. The 1:1,000,000 soil data were obtained from the Geospatial Data Cloud Platform [41].

2.3. Methods

2.3.1. Introduction of Work Methodology

The overall technical flowchart is as follows (Figure 2):

2.3.2. Value Equivalent Method

In this study, the calculation of eco-assets (EA) can be expressed as [42]:
EA = ∑ i = 1 n ∑ j = 1 m F i j × S i × V i j
In the formula, i = 1, 2, ..., n represents the type of ecosystem, and j = 1, 2, ..., m represents the ecosystem service function. In this study, service functions such as food production, water supply, and gas regulation were selected. Fij represents the regulating factor of the jth ecosystem service function of the ith ecosystem. Si refers to the area of the ith ecosystem, and Vij refers to the unit area value of the jth ecosystem service function of the ith ecosystem. Based on the national-scale equivalent table of ecosystem service value per unit area, the equivalent factor of ecosystem service value per unit area of the Qinghai–Tibet plateau was obtained by referring to the spatial and temporal dynamic change adjustment method proposed by Xie et al. [43]. Additionally, the calculation method of Yang et al. [44] was used to take the cash income per unit yield of major grain crops in China as the economic value of one standard equivalent.
The value equivalent method has some limitations in its use. First, the ecological value per unit area is constantly changing at different times. Second, it should consider the size and type of service functions with the verifications of regions and times, and to classify ecosystems [45].

2.3.3. Geodetector

Geodetector is composed of four forms of detection: differentiation and factor detection, interaction detection, risk area detection, and ecological detection [46,47].
(1)
Differentiation and factor detection:
The explanatory power of factor X to the spatial differentiation of Y can be measured by the PD value [48]. The expressions are as follows:
PD = 1 − ∑ h = 1 L N h σ h 2 N σ 2 = 1 − SSW SST
SSW = ∑ h = 1 L N h σ h 2 , SST = N σ 2
In the formula, PD refers to the explanatory power of the impact factor to the ecological assets. H = 1, 2, ..., L represents the classification or partition of variable Y or factor X. SSW and SST are the sum of intra-layer variance and total regional variance, respectively.
(2)
Interaction detection:
The q values of the factors X1 and X2 are calculated by the interactive detection, and then the q values of the interaction between the factors X1 and X2 are calculated. According to the relationship between q (X1), q (X2) and q (X1 ∩ X2), the interaction can be divided into nonlinear weakening, single-factor nonlinear weakening, two-factor enhancement, and independent and nonlinear enhancement.

3. Results

3.1. Spatial Distribution of Ecological Assets in the Qinghai–Tibet Plateau from 2000 to 2015

The value of ecological assets in QTP during 2000–2015 showed a decreasing trend from southeast to northwest. However, the spatial distribution of different sub-regions differed greatly. As shown in Figure 3a, the total value of ecological assets in QTP in 2000 was CNY 7.92 × 1011. Nakchu County in the central region had the highest value of CNY 1.24 × 1011, accounting for 15.6% of the total value, followed by Kangding County, with ecological assets of CNY 9.42 × 1010, accounting for 11.9%. The value of ecological assets in Jiuquan City was the lowest, with a value of CNY 1.82 × 109, accounting for only 0.23% of the total value. According to Figure 3b, the total value of ecological assets in QTP in 2005 was CNY 8.49 × 1011. Nakchu County had the highest value of ecological assets, with a value of CNY 1.38 × 1011, accounting for 16.2%. Kangding County had the second highest value of ecological assets, with a value of CNY 9.93 × 1010, accounting for 11.7%. The value of ecological assets in Jiuquan City was the lowest, with a value of CNY 2.21 × 109, accounting for only 0.26% of the total value. According to Figure 3c, the total value of ecological assets in QTP in 2010 was CNY 6.18 × 1011/km2. Among the different counties, the value of ecological assets in Nakchu County was the highest, with a value of CNY 9.30 × 1010, accounting for 15.0%. The value of ecological assets in Kangding County was the second highest, with a value of CNY 8.50 × 1010, accounting for 13.8%. The value of ecological assets in Jiuquan City was the lowest, with a value of CNY 1.08 × 109, accounting for only 0.17%. According to Figure 3d, the total value of ecological assets in QTP in 2015 was CNY 5.36 × 1011. Among them, Kangding County had the highest value of ecological assets, with a value of CNY 7.82 × 1010, accounting for 14.6%, followed by Nakchu County, for which the value of ecological assets was CNY 7.47 × 1010, accounting for 13.9%. The ecological asset value of Jiuquan City was the lowest, with a value of CNY 9.29 × 108, accounting for only 0.17%.

3.2. Dynamic Changes of Ecological Assets on the Qinghai–Tibet Plateau from 2000 to 2015

As shown in Figure 4 and Table 1, the average value of ecological assets in QTP increased first and then decreased from 2000 to 2015, with the highest ecological asset value occurring in 2005. However, the standard deviation of ecological assets in QTP in 2015 was lower than that in 2000, which indicated that the difference in ecological assets among sub-regions of QTP in 2015 was reduced, and the spatial distribution of ecosystem services was more uniform.
The total area of this study region with ecological assets is 1.32 × 106 km2. As shown in Figure 5a and Figure 6, from 2000 to 2005, the area of the stable region was 1.07 × 106 km2, accounting for 80.5%. This was mostly located in the northern part of Nakchu County, the eastern part of Hotan City, Korla City, Jiuquan City, the northwestern part of Xiahe County, the northern part of Kangding County, and the northern part of Gar County. The area of the decreasing zone was 1.51 × 105 km2, which accounted for 11.3%. It was mainly concentrated in the west of Gaer County, the southwest of Xiahe County, the northwest of Maqin County, the north of Lhasa City, and most of Shigatse City. The area of the increasing zone was 1.09 × 105 km2, accounting for 8.2%, mostly located in the south of Atushi City, the northwest of Hotan City, the southeast of Gar County, the west of Nakchu County, the middle of Zhidoi County, the middle of Gonghe County, the southeast of Maqin County, and the middle of Pingan County.
As shown in Figure 5b and Figure 6, the area of the stable zone of ecological assets in QTP from 2005 to 2010 was 9.94 × 105 km2, accounting for 72.1%, mainly distributed in the northern part of Gaer County, the northeastern part of Korla City, the northern part of Nakchu County, the northwestern part of Ulan County, western Xiahe County, northern Barkam County, and central Jiuquan City. The area of the decreasing zone was 3.05 × 105 km2, accounting for 22.1%, which was mostly located in the east of Atushi City, Zhangye City, the west and east of Ulan County, the south of Gonghe County, Pingan County, the east of Xiahe County, the north of Hotan City, the south of Gar County, the northwest of Shigatse City, and the middle and east of Zhidoi County. The area of the increasing zone was 7.96 × 104 km2, accounting for 5.8%, mainly distributed in the central and southern parts of Barkam County, most of Kangding County, southern Shigatse City, and central Chamdo City. This kind of spatial pattern was mainly affected by temperature, precipitation, and human activities (social and economic development, land reclamation, returning farmland to grassland, returning farmland to forest).
As shown in Figure 5c and Figure 6, the area of the stable zone of ecological assets on the Qinghai–Tibet plateau from 2010 to 2015 was 1.22 × 106 km2, accounting for 91.2%. It was mostly concentrated in the east and west of Atushi City, the east of Hotan City, Korla City, Ulan County, Jiuquan City, Zhangye City, the northwest of Zhidoi County, the north of Barkam County, and Xiahe County. The area of the decreasing zone was 8.12 × 104 km2, accounting for 6.0%, mostly located in the southeastern part of Gar County, the central and northern parts of Nakchu County, the northeastern part of Zhidoi County, the central part of Chamdo City, the southern part of Barkam County, and the northwestern part of Zhongdian County. The area of the increasing zone was 3.70 × 104 km2, accounting for 2.8%, scattered in the southeastern part of Zedang City, northern Kangding County, and southern Maqin County.
As shown in Figure 5d and Figure 6, the area of the stable zone in QTP from 2000 to 2015 was 8.92 × 105 km2, accounting for 67.0%, mainly distributed in the northern part of Gaer County, the northern part of Nakchu County, Korla City, the eastern part of Hotan City, the northwestern part of Ulan County, and the western part of Xiahe County. The area of the decreasing zone was 3.71 × 105 km2, accounting for 27.9%. It was mainly distributed in the southern part of Gar County, Shigatse City, Lhasa City, southern Nakchu County, eastern Atushi City, eastern Kashgar City, northern Hotan City, Golmud City, Zhidoi County, northern Barkam County, Chamdo City, Menyuan Hui Autonomous County, and Zhangye City. The area of the increasing zone was 6.83 × 104 km2, accounting for 5.1%, mainly distributed in Kangding County, southern Maqin County, and central Gonghe County.

3.3. Ecological Asset Changes in Different Sub-Regions of the QTP

According to Figure 7, the changes in ecological assets in QTP showed differences between ecological sub-regions from 2000 to 2015. The area of the stable zone of ecological assets was ranked as follows: E (2.69 × 105 km2) > F (1.44 × 105 km2) > H (1.11 × 105 km2) > A (1.05 × 105 km2) > I (7.72 × 104 km2) > D (7.66 × 104 km2) > C (6.88 × 104 km2) > G (3.67 × 104 km2) > B (3.93 × 103 km2). The area of the decreasing area of ecological assets was ranked as follows: E (1.03 × 105 km2) > F (7.58 × 104 km2) > B (7.19 × 104 km2) > C (3.69 × 104 km2) > D (3.40 × 104 km2) > A (3.19 × 104 km2) > I (8.95 × 103 km2) > G (7.95×103 km2) > B (849 km2). The area of the increasing area of ecological assets was ranked as follows: A (3.30 × 104 km2) > B (1.20 × 104 km2) > F (1.18 × 104 km2) > C (5.36 × 103 km2) > E (3.41 × 103 km2) > B (1.47 × 103 km2) > I (779 km2) > G (139 km2).
During the 2000–2015 period, the area of the increasing zone in the eastern alpine gorge temperate semi-humid hard broad-leaved forest-dark coniferous forest ecological areas was 1089 km2 larger than that of the decreasing zone, which indicated that the ecological assets in these ecological sub-regions increased. The total ecological assets in H increased, while those in the remaining ecological sub-regions decreased to some degree.

3.4. Dominant Influencing Factors of Ecological Assets in Different Ecological Sub-Regions of QTP

By comparing the interactions of driving factors on ecological assets among ecological sub-regions, it was found that the explanatory power of the same factor differed between ecological sub-regions. Additionally, the q value of most interactive factors was larger than that of any single factor. Table 2 shows that in 2000, the dominant interactive factors of A, C, H, F, and G were vegetation coverage (FVC) ∩ altitude with different q values. Among them, FVC ∩ altitude had the strongest explanatory power in C, with the largest q value of 0.912. FVC ∩ aspect were the dominant interactive factors for D (q-value: 0.545) and I (q-value: 0.847), and the q values were 0.545 and 0.847, respectively. The dominant interactive factor in E was the accumulated temperature ∩ FVC. The dominant interactive factor of H was altitude ∩ slope, and the q value was 0.954. The dominant single factor in A and H was altitude, and the q values were 0.453 and 0.810, respectively. The dominant single factor of other ecological sub-regions was FVC, but the q values were different. Among them, the q value of C was the highest at 0.870, followed by that of E, with a q value of 0.805. The explanatory power of FVC in D was the lowest, with a q value of 0.459 (Figure 8).
Table 3 shows that in 2005, the dominant interactive factors in C and D were soil type ∩ FVC, with q values of 0.878 and 0.608, respectively. Meanwhile, the dominant single factor was FVC, with q values of 0.842 and 0.473, respectively. The dominant interactive factors and single factor of F and G were the same, namely FVC ∩ altitude and FVC, respectively. The dominant interactive factors of A were slope ∩ altitude, with a q value of 0.569, while the dominant single factor was DEM, with a q value of 0.394. The dominant interactive factors of B were FVC ∩ aspect, with a q value of 0.775, and the dominant single factor was FVC, with a q value of 0.644. FVC was the dominant single factor in E, H, and I, with q values of 0.787, 0.785, and 0.670, respectively, but the dominant interactive factors were different, being temperature ∩ FVC, soil type ∩ altitude, and slope ∩ FVC, respectively (Figure 9).
Table 4 shows that in 2010, FVC ∩ aspect were the dominant interactive factors in D, F, and G, with q values of 0.549, 0.918, and 0.901, respectively. The dominant interactive factors of B and H were FVC ∩ altitude, with q values of 0.739 and 0.984, respectively. The dominant interactive factors of A were soil type ∩ altitude, with a q value of 0.520. The dominant interactive factors in C were precipitation ∩ FVC, and the q value was 0.893. The dominant interactive factors in E were the accumulated temperature ∩ FVC, and the q value was 0.804. The dominant interactive factors in I were slope ∩ FVC, with a q value of 0.878. The altitude was the dominant single factor in A and H, with q values of 0.360 and 0.859, respectively. The dominant single factor of the other ecological sub-regions was FVC. The explanatory power of FVC in C was the highest, with a q value of 0.814, followed by that of F, with a q value of 0.783. The q value (0.433) of FVC in D was the smallest (Figure 10).
Table 5 shows that in 2015, the dominant interactive factors of D, G, H, and I were FVC ∩ altitude. Among them, the explanatory power of FVC ∩ altitude in H was the largest, with a q value of 0.988, followed by that of G. The q value (0.575) in D was the lowest. Precipitation ∩ FVC were the dominant interactive factors in C and E, with q values of 0.718 and 0.781, respectively. The dominant interactive factors of A, C, and F were soil type ∩ FVC, hours of sunshine ∩ FVC and FVC ∩ aspect, respectively. The dominant single factor of H was altitude, with a q value of 0.862. The dominant single factor of the other ecological sub-regions was FVC. The q value (0.804) of FVC in F was the highest, followed by that (0.795) of C. The q value (0.332) of FVC in A was the lowest (Figure 11).

4. Discussions

4.1. Reasons for the Spatial Distribution of Ecological Assets on the Qinghai–Tibet Plateau

During the 2000–2015 period, the average value of ecological assets in QTP showed a decreasing trend from southeast to northwest. Among them, the average ecological asset values of Jiuquan City, Atushi City, Zhangye City, Kashgar City, Xining City, Hotan City, and Korla City in the south of the Qinghai–Tibet plateau were lower. The reason for this lay in the fact that these regions had low vegetation coverage, scarce water resources, poor carbon sequestration capacity, lower grass yield, and high ecological vulnerability [49,50]. The average ecological asset values of Nakchu County, Zhidoi County in the central part of QTP and Kangding County, Barkam County, Chamdo City, and Maqin County in the eastern part were high. The main reason for this was that these cities were distributed in zones with strong sand fixation ability and high soil conservation capability [51].
The southeast of Nakchu County had higher vegetation coverage and carbon fixation ability, large grass production, and stronger grassland biological product supply capacity [52]. Kangding County is concentrated in the southeast of QTP, and had low altitude, a dense river network, and high vegetation coverage and carbon fixation ability [53].

4.2. Reasons for the Changes in Ecological Assets in QTP

From 2000 to 2005, the stable zone on the Qinghai–Tibet plateau occupied the highest proportion of land, followed by the decreasing and increasing zones. From 2005 to 2010, the area of the increasing and stable zones of ecological assets decreased, while that of the decreasing zone rose [54]. The main reason was attributed to the rapid economic development, the destruction of vegetation, the reduction in vegetation oxygen release, and carbon fixation capacity in this region. From 2010 to 2015, the area of the stable and increasing zones increased, while that of the decreasing zone diminished. The main reason for this was that the disorder and overexploitation of natural resources were reduced in this period, and the environment of QTP was greatly improved [55]. The effectiveness of windbreaks, sand fixation, and the oxygen release and carbon fixation capacity of vegetation were thus enhanced, and the ecosystem services value was further improved [56].

4.3. Differences in Dominant Factors Relating to Ecological Assets in QTP

The dominant factors relating to ecological assets among ecological sub-regions of QTP differed greatly. The spatial and temporal distribution of ecological assets in the eastern part of the Qinghai–Tibet plateau was mainly affected by vegetation coverage and slope. The reason for this was that the eastern and southeastern parts of QTP are located in the Hengduan Mountains with steep terrain [57]. There was a significant vertical zonality in vegetation distribution, and there were significant correlations between vegetation types and slope. In the area with a gentle slope, the soil was well developed and the hydrothermal conditions were sufficient, being conducive to the growth of vegetation [58]. The ecological assets in the western, northern, and southwestern regions of QTP were mainly affected by vegetation coverage and altitude. The reason for this was that the altitude of the region is high, with a soft topographic relief. The hydrothermal conditions were significantly affected by altitude, which determined the regional vegetation type and its growth process [59]. The explanatory power of different single factors was lower than that of the interaction between different factors. The reason for this was that the evolution process of regional ecosystems is affected by the comprehensive interactions of various factors [60]. The influence of single factors could not reflect the causes of the formation and evolution of regional ecosystems independently [61]. From 2000 to 2015, the spatio-temporal evolution of ecological assets in different ecological sub-regions is mainly affected by temperature, precipitation, slope, and light, while human activity factors have less explanatory power. Vegetation coverage is affected by both natural and human activities. The dominant factors affecting vegetation coverage in different regions are different. Therefore, vegetation coverage is a composite factor affecting the evolution of ecological assets. The reason for this was that most of the QTP has a sparse population, and the agricultural and industrial areas are mainly distributed in “one river and two rivers” basins [62]. Therefore, the overall intensity of human activities in QTP was low. Therefore, its special topography and vegetation distribution were the dominant factors affecting the spatial and temporal distribution of and changes in ecological assets in the region [63].

4.4. Suggestions on the Protection of Ecological Assets in QTP

In order to protect the ecological assets of QTP, we need to focus on many aspects. First, we must strengthen people’s awareness of ecological protection and prohibit acts that destroy the ecological environment, such as deforestation and overgrazing. Second, we should establish nature reserves to protect species diversity. Third, we should protect the cultural resources of the Qinghai–Tibet plateau, change people’s production and lifestyle practices, and increase output and wealth in the area. Fourth, we should adhere to long-term afforestation, returning farmland to forest and grassland, windbreaks and sand fixation, soil and water conservation, and other ecological engineering activities.

5. Conclusions

This study quantitatively evaluated and analyzed the ecological assets and their spatial and temporal evolution patterns on the Qinghai–Tibet plateau during 2000–2015, and then introduced Geodetector to reveal and clarify the dominant factors of ecological asset changes in different ecological sub-regions. The QTP provides important natural resources for China, and natural resources, as ecological assets, are inseparable from human life and play a vital role. The results thus provide scientific support for the ecological management and sustainable development of the Qinghai–Tibet plateau. The results show that:
(1) From 2000 to 2015, the average value of ecological assets in QTP showed a decreasing trend from east to west, and the average value of ecological assets in QTP was the lowest. From 2000 to 2015, the ecological assets of QTP increased first and then decreased, and the highest total value of ecological assets occurred in 2005. From 2000 to 2015, the area of the stable zone of ecological assets in QTP was the largest, followed by that of the decreasing zone.
(2) The dominant factors relating to the spatio-temporal distribution of and changes in ecological assets among ecological sub-regions of QTP differed greatly. The spatial and temporal distribution of ecological assets in the eastern Tibetan Plateau were mainly affected by vegetation coverage and slope, while the ecological assets in the western, northern, and southwestern Tibetan Plateau were mainly affected by vegetation coverage and altitude. From 2000 to 2015, the spatio-temporal evolution of production assets in different ecological sub-regions was mainly affected by natural factors, and the explanatory power of human activity factors was low.
(3) Strengthening people’s awareness of ecological protection, establishing nature reserves, and adhering to the ecological protection policy would improve the regional ecological assets.

Author Contributions

Conceptualization, methodology, writing—original draft preparation, X.Y. Investigation, supervision, project administration, funding acquisition, B.G., M.L. and W.Z. Investigation, C.L., B.W. and X.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Natural Science Foundation of Shandong Province, grant number ZR2021MD047, Scientific Innovation Project for Young Scientists in Shandong Provincial Universities, grant number 2022KJ224, National Natural Science Foundation of China, grant number 42101306, the Fundamental Research Funds for Central Non-profit Scientific Institution, grant number 1610132020016, Project of Special Investigation on Basic Resources of Science and Technology, grant number 2019FY202501, Agricultural Science and Technology Innovation Program, grant number CAAS-ZDRW202201, a grant from State Key Laboratory of Resources and Environmental Information System, and the Strategic priority research program of the Chinese Academy of Sciences, grant number XDA2002040203.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The terrain of the QTP and its location.
Figure 1. The terrain of the QTP and its location.
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Figure 2. Technical flowchart.
Figure 2. Technical flowchart.
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Figure 3. Spatial distribution of ecological assets in QTP from 2000 to 2015: (a) 2000; (b) 2005; (c) 2010; (d) 2015.
Figure 3. Spatial distribution of ecological assets in QTP from 2000 to 2015: (a) 2000; (b) 2005; (c) 2010; (d) 2015.
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Figure 4. Dynamic changes in average ecological asset value in QTP from 2000 to 2015.
Figure 4. Dynamic changes in average ecological asset value in QTP from 2000 to 2015.
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Figure 5. Spatial and temporal changes in ecological assets in QTP from 2000 to 2015: (a) 2000–2005; (b) 2005–2010; (c) 2010–2015; (d) 2000–2015.
Figure 5. Spatial and temporal changes in ecological assets in QTP from 2000 to 2015: (a) 2000–2005; (b) 2005–2010; (c) 2010–2015; (d) 2000–2015.
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Figure 6. Area changes of ecological assets on the Qinghai-Tibet Plateau from 2000 to 2015.
Figure 6. Area changes of ecological assets on the Qinghai-Tibet Plateau from 2000 to 2015.
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Figure 7. Area changes of ecological assets in the ecological sub-regions of the QTP from 2000 to 2015.
Figure 7. Area changes of ecological assets in the ecological sub-regions of the QTP from 2000 to 2015.
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Figure 8. The dominant factors of QTP in 2000. (a) Interactive factors; (b) single factors. Note: A is the eastern alpine gorge temperate semi-humid hard broad-leaved forest-dark coniferous forest ecological area; B is the southern Tibetan Mountain wide valley temperate semi-arid shrub grassland ecological area; C is the upper reaches of the Yellow River and Qilian Mountains temperate broad-leaved forest-coniferous forest-shrub ecological area; D is the Ali Mountain arid desert ecological area; E is the Qiangtang Plateau sub-frigid semi-arid grassland ecological area; F is the Qinghai–Tibet plateau alpine meadow and alpine desert ecological area; G is the northeast Qinghai alpine desert ecological area; H is the southeastern Tibetan mountain tropical rain forest-monsoon forest ecological area; and I is the Kunlun Plateau arid desert grassland ecological area.
Figure 8. The dominant factors of QTP in 2000. (a) Interactive factors; (b) single factors. Note: A is the eastern alpine gorge temperate semi-humid hard broad-leaved forest-dark coniferous forest ecological area; B is the southern Tibetan Mountain wide valley temperate semi-arid shrub grassland ecological area; C is the upper reaches of the Yellow River and Qilian Mountains temperate broad-leaved forest-coniferous forest-shrub ecological area; D is the Ali Mountain arid desert ecological area; E is the Qiangtang Plateau sub-frigid semi-arid grassland ecological area; F is the Qinghai–Tibet plateau alpine meadow and alpine desert ecological area; G is the northeast Qinghai alpine desert ecological area; H is the southeastern Tibetan mountain tropical rain forest-monsoon forest ecological area; and I is the Kunlun Plateau arid desert grassland ecological area.
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Figure 9. The dominant factors of QTP in 2005: (a) interactive factors; (b) single factors.
Figure 9. The dominant factors of QTP in 2005: (a) interactive factors; (b) single factors.
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Figure 10. The dominant factors of QTP in 2010. (a) Interactive factors; (b) single factors.
Figure 10. The dominant factors of QTP in 2010. (a) Interactive factors; (b) single factors.
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Figure 11. The dominant factors of QTP in 2015. (a) Interactive factor; (b) single factor.
Figure 11. The dominant factors of QTP in 2015. (a) Interactive factor; (b) single factor.
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Table
Table 1. Dynamic changes in average ecological asset value in QTP from 2000 to 2015 (CNY 105/km2).
Table 1. Dynamic changes in average ecological asset value in QTP from 2000 to 2015 (CNY 105/km2).
2000200520102015
Min0.220.430.350.17
Max15.2715.8013.3711.98
Average5.345.734.163.60
Standard deviation4.124.243.493.19
Table
Table 2. Dominant single factors and interactive factors of different ecological sub-regions in 2000.
Table 2. Dominant single factors and interactive factors of different ecological sub-regions in 2000.
CodeEcological Sub-RegionsDominant Interactive FactorsqSingle Factorq
AEastern alpine gorge temperate semi-humid hard broad-leaved forest-dark coniferous forest ecological areaFVC ∩ DEM0.609DEM0.453
BSouthern Tibetan Mountain wide valley temperate semi-arid shrub grassland ecological areaFVC ∩ DEM0.764FVC0.701
CUpper reaches of the Yellow River and Qilian Mountains temperate broad-leaved forest-coniferous forest-shrub ecological areaFVC ∩ DEM0.912FVC0.870
DAli Mountain arid desert ecological areaFVC ∩ Aspect0.545FVC0.459
EQiangtang Plateau sub-frigid semi-arid grassland ecological areaAccumulated temperature ∩ FVC0.841FVC0.805
FQinghai–Tibet plateau alpine meadow and alpine desert ecological areaFVC ∩ DEM0.803FVC0.757
GNortheast Qinghai alpine desert ecological areaFVC ∩ DEM0.866FVC0.723
HSoutheastern Tibetan Mountain tropical rain forest-monsoon forest ecological areaDEM ∩ Slope0.954DEM0.810
IKunlun Plateau arid desert grassland ecological areaFVC ∩ Aspect0.847FVC0.693
Table
Table 3. Dominant single factors and interactive factors in 2005.
Table 3. Dominant single factors and interactive factors in 2005.
CodeEcological Sub-RegionsDominant Interactive FactorsqSingle Factorq
AEastern alpine gorge temperate semi-humid hard broad-leaved forest-dark coniferous forest ecological areaSlope
∩ Altitude		0.569Altitude0.394
BSouthern Tibetan Mountain wide valley temperate semi-arid shrub grassland ecological areaFVC ∩
Aspect		0.775FVC0.644
CUpper reaches of the Yellow River and Qilian Mountains temperate broad-leaved forest-coniferous forest-shrub ecological areaSoil type
∩ FVC		0.878FVC0.842
DAli Mountain arid desert ecological areaSoil type
∩ FVC		0.608FVC0.473
EQiangtang Plateau sub-frigid semi-arid grassland ecological areaTemperature
∩ FVC		0.834FVC0.787
FQinghai–Tibet plateau alpine meadow and alpine desert ecological areaFVC ∩ Altitude0.808FVC0.768
GNortheast Qinghai alpine desert ecological areaFVC ∩ Altitude0.917FVC0.801
HSoutheastern Tibetan Mountain tropical rain forest-monsoon forest ecological areaSoil type
∩ Altitude		0.976FVC0.785
IKunlun Plateau arid desert grassland ecological areaSlope
∩ FVC		0.835FVC0.670
Table
Table 4. Dominant single factors and interactive factors in 2010.
Table 4. Dominant single factors and interactive factors in 2010.
CodeEcological Sub-RegionsDominant Interactive FactorsqSingle Factorq
AEastern alpine gorge temperate semi-humid hard broad-leaved forest-dark coniferous forest ecological areaSoil type ∩ Altitude0.520Altitude0.360
BSouthern Tibetan Mountain wide valley temperate semi-arid shrub grassland ecological areaFVC ∩ Altitude0.739FVC0.620
CUpper reaches of the Yellow River and Qilian Mountains temperate broad-leaved forest-coniferous forest-shrub ecological areaPrecipitation ∩ FVC0.893FVC0.814
DAli Mountain arid desert ecological areaFVC ∩ Aspect0.549FVC0.433
EQiangtang plateau sub-frigid semi-arid grassland ecological areaAccumulated temperature
∩ FVC		0.804FVC0.714
FQinghai–Tibet plateau alpine meadow and alpine desert ecological areaFVC ∩ Aspect0.918FVC0.783
GNortheast Qinghai alpine desert ecological areaFVC ∩ Aspect0.901FVC0.588
HSoutheastern Tibetan Mountain tropical rain forest-monsoon forest ecological areaFVC ∩ Altitude0.984Altitude0.859
IKunlun Plateau arid desert grassland ecological areaSlope ∩ FVC0.878FVC0.682
Table
Table 5. Dominant single factors and interactive factors in 2015.
Table 5. Dominant single factors and interactive factors in 2015.
CodeEcological Sub-RegionsDominant Interactive FactorsqSingle Factorq
AEastern alpine gorge temperate semi-humid hard broad-leaved forest-dark coniferous forest ecological areaSoil type ∩ FVC0.490FVC0.332
BSouthern Tibetan Mountain wide valley temperate semi-arid shrub grassland ecological areaPrecipitation
∩ FVC		0.718FVC0.564
CUpper reaches of the Yellow River and Qilian Mountains temperate broad-leaved forest-coniferous forest-shrub ecological areaHours of sunshine ∩ FVC0.867FVC0.795
DAli Mountain arid desert ecological areaFVC ∩ DEM0.575FVC0.435
EQiangtang Plateau sub-frigid semi-arid grassland ecological areaPrecipitation ∩ FVC0.781FVC0.638
FQinghai–Tibet plateau alpine meadow and alpine desert ecological areaFVC ∩ Aspect0.836FVC0.804
GNortheast Qinghai alpine desert ecological areaFVC ∩ DEM0.901FVC0.768
HSoutheastern Tibetan Mountain tropical rain forest-monsoon forest ecological areaFVC ∩ DEM0.988DEM0.862
IKunlun Plateau arid desert grassland ecological areaFVC ∩ DEM0.837FVC0.701
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Yuan, X.; Guo, B.; Lu, M.; Zang, W.; Liu, C.; Wang, B.; Huang, X. Eco-Asset Variations and Their Driving Factors in the Qinghai–Tibet Plateau, China, under the Context of Global Change. Sustainability 2023, 15, 7466. https://doi.org/10.3390/su15097466

AMA Style

Yuan X, Guo B, Lu M, Zang W, Liu C, Wang B, Huang X. Eco-Asset Variations and Their Driving Factors in the Qinghai–Tibet Plateau, China, under the Context of Global Change. Sustainability. 2023; 15(9):7466. https://doi.org/10.3390/su15097466

Chicago/Turabian Style

Yuan, Xingming, Bing Guo, Miao Lu, Wenqian Zang, Chuan Liu, Baoyu Wang, and Xiangzhi Huang. 2023. "Eco-Asset Variations and Their Driving Factors in the Qinghai–Tibet Plateau, China, under the Context of Global Change" Sustainability 15, no. 9: 7466. https://doi.org/10.3390/su15097466

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