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Article

Ecological Vulnerability Evaluation and Change Analysis of the Tianshan Area Along the Pipeline of the “West-to-East Gas Transmission” Project Based on the SRP Model

by
Chao Wang
1,
Yijie Zhu
1,
Zihao Wu
2 and
Xiong Xu
1,*
1
College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
2
School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(10), 4301; https://doi.org/10.3390/su17104301
Submission received: 1 March 2025 / Revised: 1 April 2025 / Accepted: 7 May 2025 / Published: 9 May 2025
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Abstract

:
The “West-to-East Gas Transmission” project has accelerated economic development in Xinjiang and the central-western regions along the pipeline. However, as the pipeline traverses multiple cities and counties in the Tianshan region, it has significantly impacted the local ecology, necessitating a comprehensive assessment. This study employs the Sensitivity-Resilience-Pressure (SRP) model to construct an ecological vulnerability assessment system for the Tianshan region, aiming to analyze changes in ecological vulnerability and evaluate the environmental impact of the “West-to-East Gas Transmission” project. The results indicate that, spatially, ecological vulnerability in the Tianshan region increases progressively from northwest to southeast. Temporally, from 2000 to 2010, the mean Ecological Vulnerability Index (EVI) exhibited a decreasing trend, with values of 0.476, 0.464, and 0.462, primarily shifting to lower vulnerability levels. From 2010 to 2020, the EVI showed an increasing trend, with values of 0.462, 0.466, and 0.468, predominantly transitioning from heavy to very heavy vulnerability. The key influencing factors of ecological vulnerability in the Tianshan region, ranked by importance, are NDVI, NPP, land use type, annual precipitation, and aridity. Furthermore, the “West-to-East Gas Transmission” project consists of three main pipelines (Lines 1, 2, and 3), for which buffer zone analyses were conducted at radii of 1 km, 3 km, and 5 km. The results indicate that ecological vulnerability patterns remained consistent across different buffer zone sizes, and larger buffer radii were associated with lower mean EVI values along the pipeline. After pipeline construction, the mean EVI along Line 1 decreased from 0.566 to 0.550, while the EVI along Line 2 remained nearly unchanged. In contrast, the mean EVI along Line 3 increased from 0.434 to 0.447. Regarding changes in ecological vulnerability levels, along Line 1, the area of improvement (18.83%) exceeded the area of deterioration (1.09%), primarily due to the high proportion of very heavy vulnerability zones (>80%), which are more likely to transition to lower vulnerability levels. Along Line 2, ecological vulnerability remained relatively stable, indicating minimal environmental impact. However, along Line 3, the improvement area (3.81%) was significantly smaller than the deterioration area (20.52%), suggesting that construction of Line 3 had a more pronounced ecological impact, leading to greater degradation of the ecological vulnerability along its route.

1. Introduction

The Tianshan region is a typical representative of large mountainous ecosystems in temperate arid zones worldwide and is also a sensitive area for global environmental change [1]. Its ecological environment is extremely fragile but relatively rich in natural resources and energy. The region plays a significant role in the ecological and geographical pattern of the Central Asian arid zone [2], while also serving as a central hub for trade, cultural exchange, and artistic interactions. It is a strategic barrier in China’s northwest, the core area of the Silk Road Economic Belt, and a key region in the National Western Development Initiative, thus it is an important gateway for China’s opening to the West.
Since the implementation of the “West-to-East Gas Transmission” strategy, a series of large-scale corridor-type infrastructure projects have been launched in Xinjiang in the new century [3]. These projects ensure the continuous transport of western energy resources to eastern China via highways, railways, and oil and gas pipelines, supporting the normal operation of the social and economic production system. Concurrently, Xinjiang has been developing the Tianshan North Slope Urban Cluster with Ürümqi as the core. This area has become one of the most developed regions in Xinjiang, with significant advancement in industries, such as modern manufacturing, agriculture, transportation, education, and technology, accounting for 83% of Xinjiang’s heavy industry and 62% of its light industry [4,5]. However, along with urbanization, land development, and pipeline construction, the Tianshan region faces significant challenges in maintaining its ecological quality. These factors have introduced threats to the ecosystem, such as land degradation, changes in vegetation cover, and soil salinization. The Tianshan ecosystem is highly vulnerable, being a typical mountain–oasis–desert system with strong ecological sensitivity and weak resilience to disturbances [6]. As the western development process accelerates, the intensification of tourism, urbanization, and oil and gas exploration, along with the construction and operation of gas pipelines, could alter surface vegetation cover and lead to land desertification, further worsening the already fragile ecosystem and increasing the vulnerability of the ecosystems along the pipeline routes.
The “West-to-East Gas Transmission” project is a key component of the Western Development Strategy. It was initiated to address the imbalance between the natural gas resources in the western regions and the consumption markets in the eastern parts of China. This project is expected to drive the country’s economic progress, especially playing a significant role in the development of central and western China [7], making it essential to assess the ecological vulnerability along its pipeline routes. Various models have been developed for constructing ecological vulnerability assessment index systems. Commonly used models include the PSR (Pressure-State-Response) model [8,9,10,11], the VSD (Vulnerability Scoping Diagram) model [12,13], the SRP (Sensitivity-Resilience-Pressure) model [14], the exposure-sensitivity-adaptation model [15,16], the natural causes–results model [17,18], etc. There have been numerous studies conducted by domestic scholars on the ecological vulnerability assessment of China’s central and western regions. For example, Chen et al. used the VSD model to develop an ecological vulnerability assessment index system for the Hunshandake Sandy Land and conducted an assessment and driving mechanism analysis of its ecological vulnerability from 2000 to 2019 [19]. Xu et al. employed the SRP model to establish an Ecological Vulnerability Index system for the Qaidam Basin and carried out a systematic evaluation of the region’s ecological vulnerability [20]. Xue et al. [10] developed an ecological vulnerability assessment system for the Tarim River Basin using the PSR model. The VSD model provides an in-depth analysis of ecological vulnerability but requires complex and high-volume data. The PSR model emphasizes ecological relationships but overlooks sensitivity and resilience. In comparison, the SRP model is more comprehensive and accurate, taking into account sensitivity, resilience, and pressure [21]. This model integrates these three core dimensions to assess the ecosystem’s response and recovery capabilities to both internal and external disturbances, while considering the impacts of the natural environment and human activities, thereby identifying and quantifying ecological vulnerability. The commonly used indicator weighting methods for models include principal component analysis [22,23], the analytic hierarchy process [24,25], fuzzy mathematical methods [26,27], the entropy weight method [28,29,30], etc.
In summary, this study will focus on the following three aspects:
1.
It will focus on establishing an ecological vulnerability evaluation system for the Tianshan region based on the SRP model. Eleven representative evaluation indicators will be selected across five key dimensions—topography, meteorology, surface conditions, vegetation, and socio-economic factors—with their respective weights assigned using the Analytic Hierarchy Process (AHP).
2.
It will focus on performing a driving force analysis using the Geodetector method to quantify the influence of different factors on ecological vulnerability. This study will apply both factor detection and interaction detection modules to assess the explanatory power of individual factors and uncover the relative importance of various drivers affecting ecological vulnerability.
3.
It will focus on conducting a buffer zone analysis of the “West-to-East Gas Transmission” pipeline. By comparing the spatial distribution characteristics and evolutionary patterns of ecological vulnerability before and after the construction of each main pipeline, this study aims to reveal the extent and impact of pipeline construction on the surrounding ecosystem.
The findings of this study provide valuable data support for the long-term ecological changes in the Tianshan region and serve as an important reference for ecological restoration efforts and the management of the West-to-East Gas Transmission pipeline.

2. Study Area

The Tianshan region, located in the heart of Central Asia, serves as an important geographical landmark between China and Central Asia. Geographically, it spans approximately from 39.464° N to 46.212° N in latitude, and from 75.515° E to 91.916° E in longitude, stretching from the Turpan Basin in Xinjiang, China, in the east to Kazakhstan in the west, and extending along an east–west axis, as shown in Figure 1. The Tianshan Mountain range is approximately 2500 km long, with an average width ranging from 250 to 350 km, covering most of the central Xinjiang Uyghur Autonomous Region. Due to its vast geographic span, the Tianshan region lies at the intersection of the ancient Asian Ocean closure zone and the Tethys Ocean closure zone, having undergone a complex geological evolutionary history. This has resulted in the formation of rich mineral resources and unique geological landscapes. Furthermore, the region is rich in a diverse array of natural resources, including snow-capped peaks, ice glaciers, forest meadows, rivers and lakes, red-layered canyons, and a wealth of rare, endangered, and endemic species [31]. It is one of China’s most important ecological zones. Additionally, the “West-to-East Gas Transmission” pipeline passes through several cities and counties in the Tianshan region, significantly impacting the local ecological environment and socio-economic development.

3. Research Methods

3.1. Research Framework

This study first constructs the SRP model based on ecological environmental factors in the Tianshan region, identifying 11 model indicators [32,33,34], including elevation, slope, annual average temperature, annual average precipitation, aridity index (AI), land use type, net primary productivity (NPP), NDVI, population density, per capita GDP, and nighttime light index. Data collection, image stitching, image clipping, and resampling are then performed, followed by data fusion and indicator standardization. The weights of the various indicators are determined using the APH. The EVI for the Tianshan region is calculated using a weighted approach. Ecological vulnerability characteristics in the region from 2000 to 2020, in five-year intervals, are analyzed. Subsequently, a buffer zone analysis based on the “West-to-East Gas Transmission” pipeline data is conducted to assess the distribution of ecological vulnerability along the pipeline. This allows for the analysis of the impact of pipeline construction on the surrounding ecology. The technical approach used in this study is illustrated in Figure 2.

3.2. Data Sources

The data used in this study are divided into five parts. The terrain data, including DEM data, are obtained from the Geo-Spatial Data Cloud Platform, while the slope data are derived from the DEM data processed using ArcGIS 10.8 software. Meteorological data, including temperature, precipitation, and aridity index (AI), are sourced from the National Earth System Science Data Center. The land surface data are obtained from the China Land Cover Dataset (CLCD). NDVI and nighttime light data are provided by the Institute of Resources and Environment Science and Data Platform, while the net primary productivity of vegetation data comes from the Earth Resource Data Cloud. Social data, including population density, are taken from the WorldPop dataset, and GDP data are retrieved from the Earth Resource Data Cloud. Specific data details are shown in Table 1.

3.3. Approaches to Ecological Vulnerability Analysis

3.3.1. Selection of Evaluation Indicators

Based on the principle of selecting evaluation indicators and considering the ecological environment of the Tianshan region, ecological fragility is decomposed into three aspects: ecological sensitivity, ecological fragility, and ecological pressure. We adopted a top-down approach, referring to relevant studies [35,36,37,38] and combining existing data to select 11 representative evaluation indicators from 5 key levels, terrain, meteorology, surface, vegetation, and society, to construct an ecological vulnerability evaluation index system for the Tianshan region. These indicators include elevation, slope, annual average temperature, annual average precipitation, AI, land use type, NDVI, NPP, population density, per capita GDP, and the nighttime light index. To facilitate the subsequent standardization of the indicators, and based on the inherent meaning of each indicator and its positive or negative impact on the results of ecological vulnerability assessment, the indicators were classified into two categories: positive and negative indicators [39]. Positive indicators reflect an increase in ecological vulnerability as their values increase, while negative indicators indicate a decrease in ecological vulnerability as their values increase.

3.3.2. Weight Assignment for Indicators

This study uses the AHP to establish the relative weights of the various indicators [40]. First, a judgment matrix reflecting the hierarchical relationships between the different indicators is constructed. After calculating the weights, consistency testing is performed to ensure the rationality of the results [41]. The relative importance between indicators was based on Liu et al.’s [42] research.
The calculated consistency ratio (CR) value of the judgment matrix constructed in this study is 0.056, which is less than 0.1. Therefore, the matrix passes the consistency test, indicating that the weighting results are reasonable and reliable. Based on this, the weight values for each indicator are successfully derived through data normalization of the matrix results, as shown in Table 2.

3.3.3. Standardization of Indicators

To address the significant differences in the value ranges and measurement units of the multiple indicators within the evaluation system, standardization is necessary. The standardization methods used in this study include the extreme difference standardization and the qualitative grading assignment method [43].
1.
Extreme difference standardization
This method eliminates the influence of variable unit constraints by adjusting the scale of the data, allowing for the comparison and integration of indicators with different units or magnitudes. Different calculation methods are applied for positive and negative indicators [44]. The specific formulas for calculation are as follows:
Z + = X i − X m i n X m a x − X m i n
Z − = X m a x − X i X m a x − X m i n
where Z + represents the standardized value of the positive indicator; Z − represents the standardized value of the negative indicator; X i represents the original data value; X m i n represents the minimum value of the data; X m a x represents the maximum value of the data.
2.
Qualitative grading assignment method
Land use type is a qualitative indicator. Based on the land use conditions in the Tianshan region, and referring to the land type classification standards used by Lu Qing et al. [45], the land use types are assigned graded values as follows: forest land and water are classified as level 2; grassland is level 4; arable land is level 6; built-up land is level 8; and barren land is level 10, as shown in Table 3. In this classification, a higher grade indicates a greater impact of that land type on ecological vulnerability, while a lower grade indicates a lesser impact.

3.3.4. EVI Calculation and Classification

In the ecological vulnerability assessment, the EVI value is calculated using a weighted summation method. After determining the weights of the indicators, weight coupling calculations are performed to assess ecological vulnerability. The calculation of the EVI follows the formula below:
E V I = ∑ i = 1 n c i w i
where E V I is the Ecological Vulnerability Index, used to quantify the vulnerability of the ecosystem in a specific region. c i represents the standardized value of the i -th ecological assessment indicator, while w i represents the weight of the i-th indicator factor within the comprehensive evaluation system. The range of the EVI is from 0 to 1, and the larger the value, the more fragile the ecology.
To further enhance the intuitiveness and readability of the data and to visually analyze the spatiotemporal evolution characteristics of ecological vulnerability, it is necessary to classify the EVI. Since there is no unified standard for classifying ecological vulnerability levels, mainstream classification methods include, but are not limited to, the equal interval method [46], natural breaks classification [47], the quantile method [48], and the standard deviation method [49]. Among these, the natural breaks classification method is a statistical technique that classifies data based on the distribution patterns of values. This method groups similar values in the most appropriate way while maximizing the differences between categories when creating class intervals. Compared to other classification methods, the natural breaks method is less influenced by subjective factors, making it a more objective way to obtain vulnerability classification results [50]. Therefore, this study adopts the natural breaks classification method to divide the ecological vulnerability in the Tianshan region into five levels: a slight vulnerability zone, a light vulnerability zone, a medium vulnerability zone, a heavy vulnerability zone, and a very heavy vulnerability zone. The classification results show that the vast majority of bare land shows very heavy vulnerability, while vegetation-covered areas such as grasslands and forests show slight or light vulnerability.

3.3.5. Analysis of EVI Influencing Factors

Geodetector is a method designed to explore the spatial stratification characteristics and patterns of geographic phenomena [51]. It effectively addresses issues of spatial dependence and heterogeneity that vary with scale, which traditional statistical methods often fail to handle. In this approach, the explanatory power of a single driving factor on the response variable is measured, while the interaction between two factors is evaluated to determine whether they jointly influence the response variable. The influence of each factor is quantified using the q value [52], which is calculated as follows:
q = 1 − ∑ h = 1 L N h σ h 2 N σ 2
where h represents the stratum of the variable or factor under consideration. N is the total number of cells across the entire region; N h is the number of cells in the h -th layer. σ h 2 and σ 2 indicate the variance in ecological vulnerability intensity specific to stratum h and the overall variance across the study area, respectively. The parameter q , which ranges from 0 to 1, reflects the influence on the spatial distribution of ecological vulnerability, with higher values signifying a greater impact.

4. Results

4.1. Characteristics of Ecological Vulnerability Change in the Tianshan Region

The distribution of ecological vulnerability levels in the Tianshan region is presented in Figure 3. The ecological vulnerability of the Tianshan region exhibits a spatial distribution pattern that gradually increases from northwest to southeast. Areas with lower ecological vulnerability are primarily located in the northwestern mountainous regions, including the Ili Kazakh Autonomous Prefecture, the Bortala Mongol Autonomous Prefecture, and the Bayanbulak Nature Reserve. These areas are characterized by high elevations, abundant precipitation, and dense vegetation, contributing to a relatively stable ecological environment. In contrast, ecological vulnerability intensifies significantly in the southeastern regions, encompassing extremely arid areas such as the Taklamakan Desert and Lop Nur. These regions experience minimal precipitation, arid climatic conditions, and predominantly barren land types, with very sparse vegetation, making their ecosystems highly susceptible to external disturbances. Furthermore, in densely populated urban areas such as Urumqi and Ili, ecological vulnerability is generally classified as heavy or very heavy levels. However, as one moves away from urban centers, ecological vulnerability gradually decreases, indicating that human activities have a significant impact on the stability of the regional ecosystem.
To analyze the changes in ecological vulnerability in the Tianshan region from 2000 to 2020, an area change map and a transition map of ecological vulnerability zones were constructed, as shown in Figure 4 and Figure 5 [53]. From a temporal perspective, the ecological vulnerability of the Tianshan region can be divided into two distinct periods.
The first period, from 2000 to 2010, witnessed a decline in ecological vulnerability. During this time, the mean Enhanced Vegetation Index (EVI) values were 0.476, 0.465, and 0.462, respectively. Simultaneously, the area of the very heavy vulnerability zone exhibited a decreasing trend, whereas the area of slight vulnerability zones expanded. According to the area transition map, between 2000 and 2005, 24.56% of the very heavy vulnerability zone transitioned to a heavy vulnerability zone, and 16.02% of the medium vulnerability zones transitioned to light vulnerability zones. Between 2005 and 2010, 15.72% of the medium vulnerability zones transitioned to light vulnerability zones, while 19.63% of the light vulnerability zone transitioned to a slight vulnerability zone. Overall, the area transitioning to lower vulnerability levels exceeded the area transitioning to higher vulnerability levels during this period, further confirming the downward trend in ecological vulnerability.
The second period, from 2010 to 2020, exhibited an increasing trend in ecological vulnerability, with mean EVI values of 0.462, 0.467, and 0.468, respectively. During this period, the area of the very heavy vulnerability zone showed a rising trend. The area transition analysis indicates that between 2010 and 2015, 28.46% of the slight vulnerability zone transitioned to the light vulnerability zone, 20.31% of the light vulnerability zone transitioned to medium vulnerability zones, and 17.91% of the heavy vulnerability zone transitioned to the very heavy vulnerability zone. Between 2015 and 2020, 22.99% of the heavy vulnerability zone transitioned to the very heavy vulnerability zone. Overall, the area transitioning to higher vulnerability levels exceeded the area transitioning to lower vulnerability levels from 2010 to 2020, further demonstrating the increasing trend in ecological vulnerability during this period.

4.2. Analysis of Driving Factors of Ecological Vulnerability

This study employs changes in the EVI at five-year intervals from 2000 to 2020 as the dependent variable, while variations in selected indicators serve as independent variables to investigate the driving factors of ecological vulnerability in the Tianshan region. The selected influencing factors include land use type (X1), temperature (X2), population density (X3), NPP (X4), NDVI (X5), precipitation (X6), GDP (X7), nighttime light intensity (X8), and AI (X9).

4.2.1. Analysis of Single-Factor Detection Results

From 2000 to 2005, the explanatory power of each influencing factor was ranked as follows: NDVI (0.274), aridity index (0.216), and precipitation (0.146), with the q-values of other factors all below 0.1. During 2005–2010, land use (0.523) became the most significant influencing factor, followed by the NDVI (0.268) and NPP (0.205). From 2010 to 2015, NPP (0.244), NDVI (0.227), and land use (0.132) had the most notable impacts on ecological vulnerability. In the period of 2015–2020, NPP (0.501), land use (0.289), and precipitation (0.172) were the dominant factors.
Overall, NDVI, NPP, land use type, annual precipitation and AI were identified as the key determinants of ecological vulnerability in the Tianshan region. These findings indicate that the regional ecosystem is highly sensitive to climatic factors. The prominence of the first three factors highlights the critical role of vegetation dynamics in shaping ecological vulnerability. Additionally, changes in precipitation indirectly affect vegetation growth and desertification trends, further intensifying ecological stress.

4.2.2. Analysis of Interaction Detection Results

This study further evaluates the interaction between different factors to explore the combined effects of two variables on EVI variations. The test results are shown in Figure 6; across all five study periods, the interaction between each factor and the EVI remained relatively stable. Compared to single-factor analysis, the interaction effects between any two factors were significantly stronger than the impact of individual factors alone. Overall, the interactions among the driving factors of ecological vulnerability in the Tianshan region were primarily classified into two types: bivariate enhancement and nonlinear enhancement.
Specifically, during 2000–2005, strong synergistic effects were observed between the aridity index and temperature (0.441), as well as between the aridity index and NDVI (0.426). In 2005–2010, the most significant interactions occurred between land use type and NPP (0.720) and between land use type and NDVI (0.766). From 2010 to 2015, the strongest interactions were observed between NPP and NDVI (0.375), as well as between land use type and NPP (0.373). In the period of 2015–2020, land use type and NPP (0.784), NPP and precipitation (0.585), and NPP and the aridity index (0.535) exhibited the most significant interactions.
In summary, the interactions between land use and NPP, land use and the NDVI, NPP and the NDVI, NPP and the aridity index, and NPP and precipitation were the most prominent. These results further demonstrate that the synergistic effects of land use changes, vegetation dynamics, and climatic factors are the key mechanisms driving the evolution of ecological vulnerability in the Tianshan region.

4.3. Ecological Vulnerability Evolution Along the “West-to-East Gas Transmission” Pipeline

The “West-to-East Gas Transmission” pipeline system primarily includes Lines 1, 2, and 3, which were constructed sequentially. The construction of Line 1 took place from July 2002 to October 2004, the western section of Line 2 was constructed between February 2008 and December 2009, and the western section of Line 3 was built from October 2012 to August 2014. Therefore, 2000 and 2005 were selected as the pre- and post-construction comparison years for analyzing the environmental impact of Line 1. Similarly, 2005 and 2010 were chosen as the comparison years for Line 2, and 2010 and 2015 were chosen as the comparison years for Line 3. The buffer zone analysis was conducted at 1 km, 3 km, and 5 km radii along the pipeline corridor to avoid single-scale analytical bias [54]. This analysis is only used to determine the potential impact of pipeline construction on the local area, and not to explain the ecological evolution of the entire Tianshan region.
Since the completion of Line 1, the average EVI within the three buffer zones along the line has decreased from 0.561 to 0.548, as shown in Table 4. In addition, it can be seen that as the radius increases, the average EVI slowly decreases, which fully indicates that the influence of the pipeline gradually weakens with distance. Within a range of 1–5 km, the land area of very the heavy vulnerability zone along the pipeline shows a decreasing trend, as shown in Table 5: for example, within a range of 1 km, the area of the very heavy vulnerability zone decreases from 1189.372 km2 to 966.714 km2. This situation is due to the fact that Line 1 is located on the edge of the southeastern desert, with the very heavy vulnerability zone occupying the majority of the area, making it more likely to transition to lower levels of vulnerability.
Since the completion of Line 2, the average EVI values within the three buffer zones along the line have remained almost unchanged, which is reflected in each buffer zone, as shown in Table 6. In addition, similar to the situation of Line 1, as the radius increases, the average EVI also slowly decreases. Within all buffer zones, the area of the light vulnerability zone along the pipeline shows a decreasing trend, while the area of the heavy vulnerability zone shows an increasing trend, as shown in Table 7. For example, within a 1 km radius, the area of the light vulnerability zone increases by 48.976 km2, while the area of the heavy vulnerability zone decreases by 47.291 km2. The degree of change in the area of other ecologically fragile areas is very small. Indicating that the construction of Line 2 has a relatively small impact on the surrounding ecology.
After the completion of Line 3, the average EVI within the three buffer zones along the line increased from 0.441 to 0.444, as shown in Table 8. In addition, similar to the situation of Line 1 and Line 2, as the radius increases, the average EVI also slowly decreases. Within all buffer zones, the area of the very heavy vulnerability zone along the pipeline has shown an increasing trend. The area of other vulnerable areas has almost shown a slight decrease, as shown in Table 9; for example, within a 1 km range, the area of the very heavy vulnerability zone has increased from 468.731 km2 to 562.380 km2. It can be seen that the ecological vulnerability level along Line 3 is trending towards very heavy vulnerability. This indicates that the construction of Line 3 has a significant impact on the surrounding ecology.
In summary, it can be seen that the results presented by a buffer zone radius of 1 km are basically consistent with the radii of 3 km and 5 km. Indicating that using a radius of 1 km can reflect the impact of the pipeline. To further investigate the spatiotemporal evolution characteristics of ecological vulnerability around each pipeline, differential calculations were applied to analyze the ecological vulnerability level data before and after the construction of Lines 1, 2, and 3 [55,56], with the results shown in Figure 7. When the calculation result is negative, it indicates a reduction in ecological vulnerability, meaning an improvement in environmental quality. Conversely, positive values indicate an increase in ecological vulnerability, suggesting environmental deterioration.
After the completion of Line 1, 18.83% of the area along the corridor experienced an improvement in ecological vulnerability, while 1.09% of the area showed signs of deterioration. The remaining 80.07% of the area remained stable. This trend can be attributed to the fact that the majority of Line 1 passes through a very heavy vulnerability zone, where the transition toward lower vulnerability levels is more pronounced.
Following the completion of Line 2, 9.32% of the area showed improvement in ecological vulnerability, while 10.91% experienced deterioration. However, the majority of the area (79.77%) remained stable, indicating that the construction of Line 2 had a relatively weak impact on the surrounding environment and exerted minimal ecological pressure.
After the completion of Line 3, only 3.81% of the area exhibited an improvement in ecological vulnerability, whereas 20.52% of the area experienced deterioration. The remaining 75.67% of the area remained unchanged. These results suggest that the construction of Line 3 had a more significant impact on the surrounding ecological environment, exerting greater pressure on the ecosystem.

5. Discussion

5.1. Limitations

This study was constrained by data availability limitations when constructing the ecological vulnerability assessment index system for 2000–2020 (at 5-year intervals). Several critical parameters, including soil erosion rates, keystone species distributions, and soil organic matter content, could not be incorporated. The absence of these data may have affected the accuracy of the assessment results, particularly in quantifying the specific impacts of human activities on ecosystems. In subsequent research, we will prioritize the collection and integration of these missing datasets to further refine the evaluation model. In addition, this study mainly focuses on the comparative analysis of ecological vulnerability before and after pipeline construction, and has not yet systematically evaluated the cumulative ecological effects generated by long-term pipeline operation. We hope to continue researching this issue in the future.

5.2. The Impact of Ecological Restoration on EVI

The EVI reflects the growth status and coverage of vegetation, and in the process of ecological restoration, the EVI usually increases with vegetation restoration. For example, in 2017, the Bayanbrak local government announced the phased results of grassland ecological restoration. The average comprehensive coverage of grassland vegetation has increased by 15%, and the height of the average comprehensive vegetation has increased by 17%. Thus, from 2015 to 2020, the ecological vulnerability around Bayanbrak National Nature Reserve showed a decline, while the ecological vulnerability had remained stable before and no increase in ecological vulnerability was observed. Therefore, the focus of reducing ecological vulnerability should be on vegetation protection.

5.3. Ecological Protection Suggestions

Geodetector-based factor analysis revealed that the NDVI (Normalized Difference Vegetation Index), NPP (Net Primary Productivity), land use type, annual mean precipitation, and the aridity index were the key drivers of ecological vulnerability changes in the Tianshan region, all of which are directly or indirectly related to vegetation conditions. During 2000–2010, the mean values of the NDVI, NPP, and precipitation showed increasing trends while the aridity index decreased, collectively leading to a reduction in the mean EVI during this period, whereas from 2010 to 2020, NDVI growth stagnated accompanied by an increase in the aridity index, resulting in a slight rise in mean EVI. Therefore, for the ecological protection in the Tianshan region, it is recommended to strengthen vegetation conservation by establishing nature reserves in densely vegetated areas, implement vegetation planting and afforestation measures in desert areas to minimize the expansion of desert and bare land, and promptly restore vegetation loss caused by pipeline construction activities. At the same time, when selecting the site for a new pipeline, avoid areas with high vegetation coverage and try to choose bare soil areas for construction.

6. Conclusions

In this study, we integrated multi-source data and applied the SRP model to select 11 indicators, comprehensively analyzing the spatiotemporal evolution characteristics of ecological vulnerability in the Tianshan region from 2000 to 2020. We also conducted a specialized ecological vulnerability assessment of the pipeline corridor along the “West-to-East Gas Transmission” project and analyzed the impact of the three major pipelines on the surrounding ecological environment’s vulnerability. The main conclusions of the study are summarized as follows:
1.
Spatial and temporal distribution of ecological vulnerability in the Tianshan region
From a spatial perspective, the ecological vulnerability of the Tianshan region exhibits a pattern of gradual increase from northwest to southeast. The northwestern areas are primarily composed of slight and light vulnerability zones, whereas the southeastern areas are dominated by large, contiguous regions of very heavy vulnerability. From a temporal perspective, the average EVI values in the Tianshan region from 2000 to 2020 were 0.476, 0.464, 0.462, 0.466, and 0.468, respectively. This indicates a trend of initial decline followed by an increase, although the 2020 value remains lower than that of 2000. In terms of transitions, between 2000 and 2010, very heavy vulnerability zones primarily transitioned to heavy vulnerability zones, while medium vulnerability zones transitioned to light vulnerability zones. Conversely, from 2010 to 2020, the predominant trend was the transition from heavy to very heavy vulnerability zones.
2.
Key factors influencing ecological vulnerability
The primary influencing factors of ecological vulnerability in the Tianshan region, ranked by importance, are the NDVI, NPP, land use type, annual precipitation, and the AI. Moreover, the interactions between land use and NPP, land use and the NDVI, NPP and the NDVI, NPP and aridity, as well as NPP and precipitation, are the most significant in driving ecological vulnerability changes.
3.
Impact of the “West-to-East Gas Transmission” pipeline
Buffer zone analyses at 1 km, 3 km, and 5 km scales along the West–East Gas Pipe-line revealed generally consistent trends across all three scales, while demonstrating a gradual decrease in mean EVI values with increasing buffer radius, indicating that the pipeline’s impact on ecological vulnerability diminishes with distance. Comparative analysis showed that for Line 1, the mean EVI decreased from 0.561 to 0.548 after pipeline construction, primarily reflecting a shift from extreme to lower vulnerability levels; Line 2 exhibited minimal change with mean EVI values of 0.444 and 0.445, respectively, along with a relatively stable vulnerable area distribution, suggesting overall ecological stability; Line 3 displayed an increase in mean EVI from 0.441 to 0.444, mainly due to the expansion of the very heavy vulnerability zone. Regarding vulnerability class transitions, Line 1 predominantly changed from very heavy to heavy vulnerability, attributable to its initial composition of over 80% very heavy vulnerability zones that were more prone to downgrading; Line 2 maintained a stable vulnerable area distribution, indicating a limited ecological impact; Line 3 showed greater deteriorated than improved areas, suggesting more pronounced ecological impacts from its construction activities leading to significant vulnerability aggravation along its route.

Author Contributions

Investigation, formal analysis, writing—original draft preparation, writing—reviewing and editing, funding acquisition, C.W.; methodology, data curation, visualization, investigation, writing—original draft preparation, Y.Z.; supervision, funding acquisition., X.X. and Z.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (42222106, 42171363, and 42471450), the Third Scientific Research Project in Xinjiang (2022xjkk1004), and Fundamental Research Funds for the Central Universities of China.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 17 04301 g001
Figure 1. Overview of the study area.
Figure 1. Overview of the study area.
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Figure 2. Flow chart of this study.
Figure 2. Flow chart of this study.
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Figure 3. Distribution map of ecological vulnerability levels in Tianshan region.
Figure 3. Distribution map of ecological vulnerability levels in Tianshan region.
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Figure 4. Map of area changes in ecologically fragile areas.
Figure 4. Map of area changes in ecologically fragile areas.
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Figure 5. Map of area transfer in ecologically fragile areas.
Figure 5. Map of area transfer in ecologically fragile areas.
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Figure 6. Dynamic degree change map of single land use in Tianshan region.
Figure 6. Dynamic degree change map of single land use in Tianshan region.
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Figure 7. Ecological vulnerability level change map of each pipeline.
Figure 7. Ecological vulnerability level change map of each pipeline.
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Table 1. Research data sources.
Table 1. Research data sources.
Data TypeNameSources
TopographyChina’s 90 m Resolution Digital Elevation Model (DEM) Data ProductGeo-Spatial Data Cloud (https://www.gscloud.cn/)
MeteorologyChina’s 1 km Resolution Annual Average Temperature DatasetNational Earth System Science Data Center (https://www.geodata.cn/)
China’s 1 km Resolution Annual Average Precipitation Dataset
China’s 1 km Resolution Annual Aridity Index Dataset
Land SurfaceChina’s Land Cover DataCLCD (China Land Cover Dataset)
VegetationChina’s Annual NDVI Spatial Distribution DatasetResources and Environment Science and Data Platform (https://www.resdc.cn/)
China’s 1 km Resolution Annual NPP DatasetEarth Resource Data Cloud (http://www.gis5g.com/)
SocietyChina’s Population Density DatasetWorldPop
(https://www.worldpop.org/)
China’s Per Capita GDP Spatial Distribution DatasetEarth Resource Data Cloud
China’s Annual Nighttime Lights DatasetResources and Environment Science and Data
Table 2. Indicator weight allocation table.
Table 2. Indicator weight allocation table.
Goal LayerCriterion LayerIndicator LayerAttributeWeight
SensitivityTopographyElevationPositive0.044
SlopePositive0.061
MeteorologyTemperatureNegative0.068
PrecipitationNegative0.082
AIPositive0.125
Land SurfaceLand use typeQualitative0.136
ResilienceVegetationNPPNegative0.142
NDVINegative0.124
PressureSocietyPopulation densityPositive0.091
GDPPositive0.077
Nighttime light indexPositive0.050
Table 3. Assignment table for different land use types in Tianshan region.
Table 3. Assignment table for different land use types in Tianshan region.
Land Use TypesForest, WaterGrasslandCroplandImperviousBarren
Scoring criteria0.20.40.60.81
Table 4. EVI change table along Line 1.
Table 4. EVI change table along Line 1.
YearBuffer Radius (km)MINMAXMeanStandard Deviation
200010.3020.6530.5630.042
30.2330.6530.5620.052
50.2190.6530.5590.057
Mean0.2510.6530.5610.050
200510.2470.6400.5500.045
30.2400.6400.5480.055
50.1950.6400.5460.060
Mean0.2270.6400.5480.054
Table 5. Map of changes in the area of ecologically fragile areas along Line 1 (km2).
Table 5. Map of changes in the area of ecologically fragile areas along Line 1 (km2).
YearBuffer Radius (km)SlightLightMediumHeavyVery Heavy
20001012.47362.75382.6681189.372
33.08396.802182.876252.1273509.096
518.521218.097323.132420.565755.938
200511.83115.423100.536262.761966.714
314.667123.333285.452722.7782897.753
547.193273.226445.8551189.5294780.446
Table 6. EVI change table along Line 2.
Table 6. EVI change table along Line 2.
YearBuffer Radius (km)MINMAXMeanStandard Deviation
200510.1500.610.4470.114
30.1500.6130.4450.114
50.1500.6230.4420.113
Mean0.1500.6150.4440.114
201010.1660.5950.4480.114
30.1610.6320.4450.115
50.1610.6340.4420.115
Mean0.1620.6200.4450.114
Table 7. Map of changes in the area of ecologically fragile areas along Line 2 (km2).
Table 7. Map of changes in the area of ecologically fragile areas along Line 2 (km2).
YearBuffer Radius (km)SlightLightMediumHeavyVery Heavy
20051258.463310.557395.786464.347519.634
3850.223938.3671235.7251370.671445.063
51498.831560.272187.6172196.8512277.842
20101262.076359.533384.439417.056525.684
3822.8261105.911137.2611192.2671581.962
51395.2722027.7231887.2561992.82418.156
Table 8. EVI change table along Line 3.
Table 8. EVI change table along Line 3.
YearBuffer Radius (km)MINMAXMeanStandard Deviation
201010.1360.6070.4440.121
30.1610.6340.4410.115
50.1260.6070.4380.121
Mean0.1410.6160.4410.119
201510.1660.5950.4470.114
30.1610.6330.4440.115
50.1260.6070.4410.122
Mean0.1510.6120.4440.117
Table 9. Map of changes in the area of ecologically fragile areas along Line 3 (km2).
Table 9. Map of changes in the area of ecologically fragile areas along Line 3 (km2).
YearBuffer Radius (km)SlightLightMediumHeavyVery Heavy
20101291.764357.947408.348434.772468.731
3868.3881102.6671271.0021301.3021326.036
51508.0041829.4661908.9232095.3212419.322
20151267.367365.121386.2416.427526.38
3842.5571110.5381139.4531194.3771582.366
51525.2441741.041842.5122002.542648.394
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Wang, C.; Zhu, Y.; Wu, Z.; Xu, X. Ecological Vulnerability Evaluation and Change Analysis of the Tianshan Area Along the Pipeline of the “West-to-East Gas Transmission” Project Based on the SRP Model. Sustainability 2025, 17, 4301. https://doi.org/10.3390/su17104301

AMA Style

Wang C, Zhu Y, Wu Z, Xu X. Ecological Vulnerability Evaluation and Change Analysis of the Tianshan Area Along the Pipeline of the “West-to-East Gas Transmission” Project Based on the SRP Model. Sustainability. 2025; 17(10):4301. https://doi.org/10.3390/su17104301

Chicago/Turabian Style

Wang, Chao, Yijie Zhu, Zihao Wu, and Xiong Xu. 2025. "Ecological Vulnerability Evaluation and Change Analysis of the Tianshan Area Along the Pipeline of the “West-to-East Gas Transmission” Project Based on the SRP Model" Sustainability 17, no. 10: 4301. https://doi.org/10.3390/su17104301

APA Style

Wang, C., Zhu, Y., Wu, Z., & Xu, X. (2025). Ecological Vulnerability Evaluation and Change Analysis of the Tianshan Area Along the Pipeline of the “West-to-East Gas Transmission” Project Based on the SRP Model. Sustainability, 17(10), 4301. https://doi.org/10.3390/su17104301

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