Balancing Act on the Third Pole: Three Decades of Ecological-Economic Synergy and Emerging Disparities Along the Qinghai–Tibet Railway, China

Abstract

The Qinghai–Tibet Plateau (QTP), a critical ecological buffer for Asia, faces intensifying pressures from climate change and infrastructure expansion. The Qinghai–Tibet Railway (QTR), as the world’s highest-altitude railway, traverses this fragile yet economically vital region, where balancing ecosystem integrity and development remains a global sustainability challenge. While previous studies have documented localized environmental impacts of the QTR, systematic assessments of long-term ecological-economic interactions—particularly the synergies and trade-offs between ecosystem service value (ESV) and economic growth—are lacking. This gap hinders targeted policy design to reconcile conservation and development in extreme environments. The present research integrates an enhanced ecosystem service valuation framework with spatial econometric modeling to quantify environmental changes and ecological-economic coordination in the Qinghai–Tibet Railway Region (QTRR) during 1990–2020. The analysis reveals a cumulative ESV increase of USD 54.4 billion over the past 30 years, driven primarily by grassland restoration and regulated land use transitions. Notably, county-level ecological-economic coordination improved significantly, with harmonization indices rising by 32–68% across all jurisdictions. However, latent risks emerged: five counties exhibited severe ecosystem-health-to-economy mismatches by 2020. These findings demonstrate that infrastructure corridors in fragile ecosystems can achieve partial ecological-economic coordination through policy interventions, yet persistent local disparities demand spatially differentiated management. By linking ESV dynamics to governance pathways—including livestock–forage balance mechanisms and green urban zoning—the present study provides a transferable framework for assessing sustainability trade-offs in extreme environments. Broader implications highlight the necessity of embedding adaptive ecological thresholds into infrastructure planning, offering experiences for the Belt and Road Initiative and other high-altitude development frontiers.

1. Introduction

The Qinghai–Tibet Plateau, known as the “Roof of the World” and the “Third Pole of the Earth”, plays a significant role in global climate changes due to its unique natural environment and ecosystems [1]. The Qinghai–Tibet Railway, serving as a crucial transportation artery across this mysterious land, has brought considerable attention to the ecological environment changes in the towns along its route. The environment changes in the QTRR present a multifaceted and complex research challenge [2]. In recent years, the hybrid effects of warming and intense regional socioeconomic activities have driven substantial ecological changes in this area. Research indicates that glaciers on the QTP are melting at an accelerated pace [2], lake areas are expanding [3], and vegetation cover is changing [4], all of which have profound impacts on the QTRR’s ecological environment.

These changes in the ecological environment are not merely structural but represent dynamic adjustments in the value of ecosystem services. By quantifying these changes, researchers can gain a clearer understanding of the long-term ecological environment status of the region along the QTRR (the full list of abbreviations is given before the references), thereby providing scientific evidence for future ecological protection and sustainable development.

The ecological environment changes in the QTRR have complex effects on the value of ecosystem services. These changes are driven by both natural factors and human activities. The value of ecosystem services varies spatially across different regions [5], indicating the need to account for these differences in ecological conservation and resource management strategies. The Qinghai–Tibet Railway has introduced unprecedented socioeconomic opportunities to the towns along its route, while simultaneously exerting far-reaching impacts on the ecological environment. In this region, the development of the ecological environment and socioeconomic growth presents a complex coupling relationship, characterized by both opportunities and challenges. Rapid industrialization and urbanization lead to resource overexploitation and environmental pollution [6,7]. The Qinghai–Tibet Railway has significantly improved transportation conditions for the towns along its route, fostering rapid regional economic development [8]. However, this economic development has also intensified ecological environment impact on the QTRR. The influx of tourists and the expansion of infrastructure have placed considerable strain on local ecosystems, resulting in challenges such as grassland degradation and the overexploitation of water resources [9].

The ecological environment changes also impact socioeconomic development [10]. Environmental changes, such as deforestation or pollution, can disrupt socioeconomic systems by altering resource availability [11], affecting human health [12], and impacting agriculture [13], thereby influencing economic growth and social welfare. Conversely, environmental improvements, such as pollution reduction and habitat restoration, can stimulate economic growth by improving public health [14], attracting investment [15], and fostering sustainable industries [16], leading to prosperous and balanced development. The ecological environment and socioeconomic development of the region along the Qinghai–Tibet Railway form an interdependent and mutually influential system [1]. It is essential to prioritize ecological protection and restoration while simultaneously promoting economic growth.

Achieving coupled and coordination development between the ecoenvironment and socioeconomic growth in this unique region is a complex and challenging task. This requires a comprehensive scientific assessment of the coordination between the environment and the economy. Although there are trade-offs between economic output and environmental outcomes, their coordinated development presents an opportunity to enhance the sustainability of the regions along the Qinghai–Tibet Railway. Our research represents an important step towards the sustainable development of QTRR integrating economy and environment.

2. Literature Review

Ecosystems form the foundation of human life, offering a multitude of ecosystem services (ES) that include provisioning, regulating, and cultural functions [17,18,19]. Valuing ecosystem service value (ESV) underscores the indispensable role of ecosystems, strengthens decision-making processes, and enhances the effectiveness of ecosystem management [20]. Consequently, quantifying ecosystem services has become a critical global priority [21,22,23]. In response to growing demands for resources, habitats, and industrial expansion, natural ecosystems have been increasingly converted into agricultural and urban landscapes, potentially significantly altering the provision of ecosystem services. Valuing these services is fundamental to decision-making at all scales, from local to global, shaping planning and management strategies [24,25,26]. However, the comprehensive identification of ecosystem services impacted by human activities remains a formidable challenge, and a profound understanding is imperative for their assessment. This necessitates the integration of ecological assessments with metrics of human well-being.

At present, the ESV is dominated by two principal methodologies: first, non-monetary valuation, which includes biophysical accounting and often overlooks human desires and necessities; and second, monetary valuation, which is based on consumer preferences. Consequently, ESV assessments typically employ two distinct approaches: a bio-physical accounting-focused method and an economic-oriented method. The bio-physical accounting approach quantifies ESVs using comprehensive eco-environmental assessment techniques, modeling, and direct bio-physical observations [27], but it does not consider human needs. Conversely, the economic-based approach uses financial methods, such as market pricing and benefit transfer, to calculate ESVs [28,29], thereby bridging the gap between ecosystems and human welfare. This economic-based evaluation method has been extensively applied in recent studies across various regions, including Bangladesh [30], China [31,32,33], the USA [34,35], Latin America and the Caribbean [36], Pakistan [26], India [37], and globally [38]. This equivalent factor method involves establishing a unit value equivalent for services across different ecosystems and then assessing these services in relation to land area [39]. The method’s intuitive nature and simplicity, coupled with its minimal data requirements, enable a comprehensive assessment while adhering to a standardized accounting framework [40]. It is particularly suited for evaluating ecosystem service values at both regional and global scales.

Land use, climate change, overexploitation, and pollution emissions contribute to the degradation of ecosystems [41]. Various land use/cover (LUC) types, each with distinct features, are crucial for delivering ecosystem services, and they illustrate the degree of human impact on the natural environment. Rapid urbanization triggers profound and diverse shifts in land use, manifesting in distinct spatio-temporal patterns, which in turn lead to significant ecological and environmental consequences [42,43,44]. As industrialization and urbanization progress, human disruptions to the natural environment intensify, increasing ecosystem vulnerability [45], which significantly impacts ecosystem services, including material supply [46], gas regulation [47], waste purification [48,49], soil conservation [50], and cultural services [51]. Urbanization and industrial conversion extensively occupy ecological lands, including grasslands, forests, and wetlands, resulting in significant losses of waste purification, water conservation, biological habitats, etc. [52,53]. Ecosystem restoration and environmental management can significantly augment the delivery of ecosystem services [5,54]. Effective land use/cover management is essential for sustainable regional development, given its integral role in preserving ecosystem services [55].

The Qinghai–Tibet Plateau hosts a delicate ecosystem, marked by its high altitude, frigid temperatures, austere climate, sparse flora, and significant soil erosion. Additionally, the region has experienced increasing human activities, including overexploitation and excessive grazing. The Qinghai–Tibet Railway construction has spurred the rise in several cities and towns, bolstering the economic development of the QTP, driving rapid growth in sectors such as logistics, hospitality, and tourism. The corresponding large-scale exploitation of resources, along with the increase in solid waste and sewage discharge, contributes to the growing environmental pressure. Thus, the QTRR is a unique region characterized by intense human activities and a prominent contradiction between economic activities and natural ecosystems on the QTP. Numerous studies described the spatial and temporal patterns and distributions of various ecosystem services at different scales [39,56,57] including within the QTP [58,59]. However, previous studies seldom report the influence of alteration of land use/cover (LUC) changes on ES in this vulnerable region, the QTRR. Additionally, the traditional equivalent factor method cannot accurately measure the ecosystem service value of the QTRR, considering its unique natural conditions and high altitudes.

The coupling coordination development between the ecological environment and socioeconomic development is one of the major challenges facing the world in the 21st century. This coupling relationship signifies that economic development and environmental protection are no longer isolated domains but are instead interdependent and mutually influential within a complex system. Coupling coordination degree (CCD) model, a general approach to evaluate the coupling coordination between diverse subsystems, has been commonly employed in the study of urbanization and water environment [60], economic development and ecological environment [10], economic development and air quality [61], and local development systems [62], as well as other domains [63]. Yet, studies on the coupling coordination development between economic and environment were performed in the urban agglomeration or economically developed areas. It is urgent and significant to construct an integrated evaluation framework in this particular area to assess the coordination development between the economy and environment.

The present study aims to evaluate the comprehensive eco-environmental impact by examining fluctuations in ecosystem service values in the QTRR, providing a scientific basis to promote sustainable regional development. Initially, land use changes in the QTRR from 1990 to 2020 are identified, followed by an assessment of the evolution of ecosystem services using an improved ESV accounting method. The coordination between ESV and economic development during the past 30 years is then investigated. Also, the study measures the trajectory of sustainable development in the QTRR and contributes to the advancement of ecological civilization construction in this region and even Western China, considering the Qinghai–Tibet Railway’s pivotal role in catalyzing the plateau’s development. Ultimately, the policy implications derived from these findings are discussed.

3. Materials and Methods

3.1. Study Area

The Qinghai–Tibet Railway, spanning 1956 km, traverses the heart of the Qinghai–Tibet Plateau. Originating in the east from Xining, it parallels the Qinghai–Tibet Highway, passes through Geermu and Nagqu, and terminates in Lhasa. The railway has significantly contributed to alleviating poverty, overcoming backwardness, and fostering economic and social development across the Qinghai–Tibet Plateau, thereby enhancing the collective well-being of various ethnic groups. As the main artery for tourism and freight transportation in the region, the QTR is instrumental in driving economic growth, accelerating urban and industrial advancements, and facilitating ongoing industrial transformation.

Spanning 22 county-level administrative units, the Qinghai–Tibet Railway exerts a profound impact on these regions. Hence, we designated these 22 administrative divisions as the study area, termed QTRR (Figure 1). QTRR serves as a key location for the comprehensive development of the plateau’s abundant resources. Our investigation focused on LUC, variations in ESV, and the coordinated development between ecology and economy. The study aims to guide prudent regional planning and inform data-driven policy decisions.

3.2. Improved Ecosystem Services Value Assessment

This study employs an advanced equivalent factor method to evaluate the per unit area ESV. We utilize the monetary value of the mean natural crop yield for each unit of arable land as the reference benchmark for ecosystem service assessment. The calculation primarily considers grain products such as rice, wheat, and corn. The economic value per unit of grain has varied due to scientific and technological advances, as well as inflationary effects. Consequently, we adopted the average natural yield value of grain per unit area of farmland in China in 2010 as the baseline standard equivalent for ecosystem services. However, the equivalent factor approach assumes, albeit implicitly, that the value of ecosystem services is spatially independent. While this assumption might hold on a global or similarly large scale, as discussed by Costanza et al. (2014) [39], at a more localized scale, this assumption’s validity diminishes since ecosystem service provision is closely linked to specific natural conditions. The Normalized Difference Vegetation Index (NDVI) accurately reflects surface vegetation coverage, indicating variations in the land’s ability to provide overall ecological resources and services [64]. Higher NDVI values correspond to more vigorous vegetation growth and coverage, thereby enhancing the ESV. Considering the temporal and spatial variability in ecosystem service provision, we incorporate NDVI to refine the estimation of Ecosystem Service Values in QTRR. The improved ecosystem services value (ESV) assessment method enhances accuracy by incorporating region-specific ecological parameters and dynamic adjustments based on land use changes and ecosystem function variations, rather than relying on static coefficients. Compared to traditional methods, this approach better captures spatiotemporal variations in ecosystem services, providing a more precise and policy-relevant evaluation of ecological value in the QTRR.

$$ESV={\sum }_u{\sum }_i{\sum }_{j}F·{EC}_{ij}·{\displaystyle \frac{{NDVI}_j}{\overline{{NDVI}_j}}}·A_j$$

(1)

$${ESV}_i={\sum }_u{\sum }_{j}F·{EC}_{ij}·{{\displaystyle \frac{{NDVI}_j}{\overline{{NDVI}_j}}}·A}_j$$

(2)

$${ESV}_j={\sum }_u{\sum }_{i}F·{EC}_{ij}·{{\displaystyle \frac{{NDVI}_j}{\overline{{NDVI}_j}}}·A}_j$$

(3)

where F represents the unit value of food production service of cropland (USD ha⁻¹) in China, ${EC}_{ij}$ indicates the equivalent value factor of ecosystem service i for ecosystem types j, ${NDVI}_j$ is the NDVI of ecosystem types j for the given unit area, and $\overline{{NDVI}_j}$ represents the average NDVI of ecosystem types j for the whole country. $A_j$ signifies the area of ecosystem type j in the study unit u, and $ESV$ indicates the total ecosystem service value (USD ha⁻¹ year⁻¹). ${ESV}_i$ and ${ESV}_j$ refer to the ecosystem service values of service i and land use type j, respectively.

3.3. Ecological-Economic Coordination Analysis

Resources and the environment are the basic conditions for human survival and development, as well as the material basis for economic development and social progress. It is essential to recognize that resource and environmental protection cannot be separated from economic development, nor should they be viewed as opposing forces. Instead, there is a need to focus on the relationship between economic development and the environment, promoting their coordinated development. The present study constructs an ecological-economic coordination index to explore the coordination level between the ecological environment and economic development in the QTRR, in order to offer empirical evidence and guidance for the balanced coexistence of people and nature and the eco-environment and economy coordinated development in the study area. The coupling coordination degree measures the harmony level between different systems or elements in the development process. Ecological-economic coordination degree (D) can inform whether the relationship between ecosystem service value and GDP is harmonious [65]. The index values for ecosystem service value (E) and GDP (G) are obtained through a process of standardization and weighting (provided in the Supplementary Materials).

$$C=2\times {\left[{\displaystyle \frac{E\cdot G}{{(E+G)}^2}}\right]}^{{\displaystyle \frac{1}{2}}}$$

(4)

$$T=\alpha E+\beta G$$

(5)

$$D=\sqrt{C\times T}$$

(6)

where C denotes the ecological-economic coupling degree, and D signifies the coupling coordination degree. D can be categorized into five stages: 0 < D ≤ 0.2 reflects a severely disordered relationship between economic growth and ecosystem services; 0.2 < D ≤ 0.4 means the system is in mild disorder; when D ∈ (0.4,0.6], regional economic development and ecological environment are primarily coordinated; when 0.6 < D ≤ 0.8, the region is in moderate coordination; D ∈ (0.8, 1] shows that it has achieved a high coordination between environment and economy and the eco-economic system develops towards a harmonious state of mutual promotion. Even with significant economic progress, regional economic development will be adversely affected if there is increasing dependence on environmental and resource consumption, coupled with inadequate attention to the ecosystem management.

3.4. Data

To comprehensively assess all ecosystem services, it is crucial to account for the total extent of ecosystems within the study area. The data used in this study includes multi-temporal land use data, Normalized Difference Vegetation Index (NDVI) data, and economic statistical data. The land use data spans four periods, 1990, 2000, 2010, and 2020, and is sourced from the 1:100,000-scale multi-period remote sensing monitoring data set of land use status from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences [66,67]. This data set is generated through manual visual interpretation of Landsat TM/ETM remote sensing images, serving as the primary data source. The accuracy for the primary land use types was validated by field surveys, with an accuracy rate of over 93% [66]. In this study, land use types are divided into 13 categories (

Table A1. LUC and equivalent coefficient per ha of land for various ecosystem services.

LUC	Provision Services			Regulating Services				Support Services			Cultural Services
	Food Supply	Materials Supply	Water Supply	Gas Regulation	Climate Regulation	Waste Treatment	Hydrological Regulation	Erosion Prevention	Nutrient Cycling	Habitat Services	Cultural and Amenity Services
Cropland	0.85	0.40	0.02	0.67	0.36	0.10	0.27	1.03	0.12	0.13	0.06
Have woodland	0.29	0.66	0.34	2.17	6.50	1.93	4.74	2.65	0.20	2.41	1.06
Bush forest	0.31	0.71	0.37	2.35	7.03	1.99	3.51	2.86	0.22	2.60	1.14
Sparse woodland	0.22	0.52	0.27	1.70	5.07	1.49	3.34	2.06	0.16	1.88	0.82
Other woodland	0.19	0.43	0.22	1.41	4.23	1.28	3.35	1.72	0.13	1.57	0.69
High coverage grassland	0.38	0.56	0.31	1.97	5.21	1.72	3.82	2.40	0.18	2.18	0.96
Medium coverage grassland	0.22	0.33	0.18	1.14	3.02	1.00	2.21	1.39	0.11	1.27	0.56
Low coverage grassland	0.10	0.14	0.08	0.51	1.34	0.44	0.98	0.62	0.05	0.56	0.25
Rivers and lakes	0.80	0.23	8.29	0.77	2.29	5.55	102.24	0.93	0.07	2.55	1.89
Ice and snow	0.00	0.00	2.16	0.18	0.54	0.16	7.13	0.00	0.00	0.01	0.09
Wetland	0.51	0.50	2.59	1.90	3.60	3.60	24.23	2.31	0.18	7.87	4.73
Sand	0.01	0.03	0.02	0.11	0.10	0.31	0.21	0.13	0.01	0.12	0.05
Bare land	0	0	0	0.02	0.00	0.10	0.03	0.02	0.00	0.02	0.01

), with rivers and lakes combined into one class. Wetlands include reservoirs, ponds, beaches, and swamps; saline–alkali land, Gobi, bare land are collectively categorized as bare land. Sand is categorized separately due to the severity of soil desertification. The NDVI data used in the present paper were obtained from the Data Center for Resources and Environmental Sciences of the Chinese Academy of Sciences (https://www.resdc.cn). GDP data were collected from local statistical yearbooks and local statistical bulletins of national economic and social development. F being $503.2 per hectare was employed in our study according to the market price of food production in natural cropland in 2010 for comparison across different years. The equivalent coefficients for ESV per unit of area associated with various land types were obtained from previous studies [39,40,68], which summarize the opinions of hundreds of relevant scholars and reflet national conditions (Table A1). The ESVs are allocated to 4 types and 11 subtypes (Table A1). Construction land area was not considered in assessing ESV in this study due to the specific characteristics of urban construction on the Qinghai–Tibet Plateau.

4. Results

4.1. LUC Change

We performed a detailed reclassification of LUC (

Table 1. Land use of the region along the Qinghai–Tibet Railway during the period 1990–2020.

Year	1990		2000		2010		2020
Land Type	Area (ha)	Percent	Area (ha)	Percent	Area (ha)	Percent	Area (ha)	Percent
Cropland	224,070	0.47%	236,619	0.50%	234,761	0.50%	260,018	0.55%
Forestland	14,383	0.03%	14,364	0.03%	15,293	0.03%	30,841	0.07%
Shrubland	348,503	0.74%	348,125	0.74%	346,943	0.73%	455,752	0.96%
Sparse woodland	162,119	0.34%	160,189	0.34%	160,189	0.34%	248,167	0.53%
Other woodland	584	0%	1735	0%	1687	0%	2,922	0.01%
High coverage grassland	3,242,632	6.86%	3,238,567	6.85%	3,236,788	6.85%	2,014,397	4.26%
Medium coverage grassland	7,952,007	16.83%	7,945,336	16.82%	7,917,914	16.76%	7,444,746	15.76%
Low coverage grassland	13,201,754	27.94%	13,188,245	27.91%	13,131,880	27.79%	14,519,004	30.73%
Rivers and lakes	1,161,537	2.46%	1,148,711	2.43%	1,185,821	2.51%	1,368,343	2.90%
Ice and snow	430,237	0.91%	430,299	0.91%	429,607	0.91%	484,073	1.02%
Wetlands	2,045,804	4.33%	2,044,210	4.33%	2,032,360	4.30%	2,620,882	5.55%
Sand	2,092,898	4.43%	2,110,497	4.47%	2,181,978	4.62%	2,312,367	4.89%
Bare land	16,317,267	34.54%	16,320,450	34.54%	16,281,559	34.46%	15,384,794	32.56%
Construction land	54,156	0.11%	60,604	0.13%	91,171	0.19%	101,644	0.22%
Total	47,247,951	100%	47,247,951	100.00%	47,247,951	100%	47,247,951	100%

) and the estimation of ecosystem service values. To enhance simplicity and comparability, all forest types were grouped into a single land use category—forest, and all grass types were consolidated into grassland. In 1990 and 2020, QTRR was predominantly covered by grassland, including high coverage, medium coverage, and low coverage, accounting for 51.63% and 50.77%, with a slight decrease. Bare land constituted the second-largest area in QTRR, covering 34.54% and 32.56% in 1990 and 2020, respectively. Construction land constituted the smallest proportion of land use, ranging from 0.11% to 0.22% during the study period, yet it experienced significant growth of 87.69%.

Over the past 30 years, the Qinghai–Tibet Railway Region (QTRR) has experienced notable land use changes. The most significant alterations include a substantial increase in wetland areas by 575,078 hectares and a considerable reduction in bare land by 932,473 hectares (Figure 2). Grassland also underwent notable changes, with a decrease of 418,246 hectares from 1990 to 2020, significantly impacting overall land use and cover trends. Specifically, low-coverage grassland saw a substantial increase, while high-coverage and medium-coverage grasslands experienced significant declines. Additionally, woodland, sand, and rivers and lakes exhibited significant growth, with increases exceeding 200,000 hectares. In contrast, the changes involving cropland, ice and snow, and construction land were relatively minor.

4.2. Spatial-Temporal Evolution of ESV

QTRR’s ESV increased from 1.44 × 10¹¹ to 1.98 × 10¹¹ USD by 37.67% during 1990–2020. In 2020, grasslands and rivers/lakes exhibited the highest ecosystem service values at 8.43 × 10¹⁰ and 7.02 × 10¹⁰ USD, comprising 42.45% and 35.37% of the total value. Wetlands (16.58%) and forests (2.64%) followed, while the remaining ecosystem types collectively contributed a relatively low 2.07% to the total value. Between 1990 and 2020, the overall trend in ecosystem service values demonstrated slower growth in the earlier period followed by accelerated growth in the later period.

The ecosystem service value (ESV) of rivers and lakes, along with wetlands, experienced a remarkable surge, reaching 4.89 × 10¹⁰ USD and 1.09 × 10¹⁰ USD, respectively, and accounting for 90.02% and 20.08% of the total ESV increase. These values far exceeded the ESV growth pertaining to other land use classes. Forests, sand, and ice and snow followed, with ESV increases of 1.56 × 10⁹ USD, 4.36 × 10⁸ USD, and 1.98 × 10⁸ USD, yet these increments represented only 5.48%, 0.80%, and 0.36% of the total ESV increase. Meanwhile, the Grassland ESV exhibited a certain degree of decline, amounting to 7.84 × 10⁹ USD. In comparison, the ESV of forests increased by 7.73 times from 1990 to 2020, while the ESV of rivers and lakes grew by 2.30 times. Additionally, the ESV of ice and snow and bare land increased by 12.61% and 5.15%, respectively.

The ESV classification aligns with the Millennium Ecosystem Assessment [19] framework, which categorizes ecosystem services into four types: Provisioning Services: Direct material benefits from ecosystems, such as food, water, and raw materials (e.g., cropland’s grain production, rivers’ water supply). Regulating Services: Ecosystem-mediated processes that stabilize environmental conditions, including climate regulation, flood mitigation, and waste purification (e.g., forests’ carbon sequestration, wetlands’ water purification). Supporting Services: Fundamental processes enabling other services, such as soil formation, nutrient cycling, and habitat provision (e.g., grasslands’ erosion prevention, forests’ biodiversity maintenance). Cultural Services: Non-material benefits, including recreation, aesthetic value, and spiritual enrichment (e.g., scenic landscapes near lakes, ecotourism opportunities).

Among ecosystem service categories, regulating services were the most significant, followed by supporting, cultural, and provisioning services (see Figure 3). In terms of subcategories, the value of hydrology regulation and climate regulation services is 9.15 × 10¹⁰ and 2.77 × 10¹⁰ USD, accounting for 46.10% and 13.95% of the total ESV, respectively. The following were habitat provision, waste treatment, erosion prevention, and gas regulation, which accounted for 8.35%, 7.62%, 7.30%, and 6.03% of the total service value, respectively. The combined value of other ecosystem services tends to be lower, accounting for only 11.57%. From 1990 to 2020, the comprehensive ecological condition of the study area steadily improved, resulting in corresponding increases in ESV across various services. During the first decade, ESV for all categories showed growth. However, during 2000–2010 and 2010–2020, except for raw material provision, gas regulation, climate regulation, erosion prevention, and nutrient cycling, the value of other services showed a growth trend.

During 1990–2020, hydrological regulation services saw a significant increase of 4.39 × 10¹⁰ USD, constituting the largest share (80.82%) of the total increment. The values of water supply, hydrological regulation, and waste treatment underwent substantial changes, experiencing increases of 85.44%, 92.22%, and 20.45%, respectively. This indicates a substantial improvement in the water ecological environment of the study area over the past 30 years, accompanied by enhanced environmental purification capacity. The values for erosion prevention and nutrient cycling initially increased and then decreased, suggesting that soil conservation capacity in the study area followed a trend of early increase then a drop. This could be linked to the substantial loss of grasslands, particularly those with high coverage. The growth of cultural service value initially decreased and then rebounded, reflecting increased attention to and promotion of ecological culture.

Figure 4 illustrates the spatial pattern of ESV in the QTRR. In 2020, the region with moderate to high ecosystem service (ESV > 6000 USD/ha) covered 7,108,905 hectares, constituting 15.08% of the total area. Within this, the high-value area (ESV > 15,000 USD/ha) occupied 1,699,846 hectares, accounting for 3.61% of the total area. The low-value area, defined as the region with ESV below USD 1500/ha, covered 23,025,557 hectares, representing 48.84% of the total QTRR area. Overall, the ecosystem service value exhibits a pattern of “low in the middle and high on both sides”. Areas of low value were predominantly centered in the bare lands within the territories of Geermu and Dulan, whereas the areas with high value were mainly located in the rivers and lakes in Gangcha, Haiyan, and Damxung counties. This can be attributed primarily to the gradual decrease in precipitation and the conversion of LUC from grassland to bare land and sand, moving from the southwest and northeast toward the central region of the study area. Additionally, counties under the jurisdiction of Xining and Lhasa, located in river valleys in the northeast and southwest of the QTRR, demonstrated strong soil and water conservation capabilities, exhibiting high ecosystem service functions per unit of area.

Overall, the ecosystem service value (ESV) transitioned from the lower value range to the medium and higher ranges, while still retaining some areas with low values. Between 1990 and 2020, the area with high ESV (ESV > 15,000 USD/ha) expanded by 783,138 hectares, with noticeable increases in ecosystem service value observed in Gangcha, Haiyan, Huangyuan, and Damxung. Nevertheless, the decrease in ESV was mainly concentrated in Nagqu County, attributed primarily to the conversion of certain rivers and lakes into bare land during the study period. The areas with low ESV (ESV < 1500 USD/ha), medium ESV (6000 < ESV < 10,500 USD/ha), and higher ESV (10,500 < ESV < 15,000 USD/ha) decreased by 439,172, 768,677, and 269,951 hectares, respectively. Meanwhile, the area with lower value (1500 < ESV < 6000 USD/ha) expanded by 6,696,545 hectares. A pattern of “strong in the middle and weak at both ends” was evident in the total ESV, influenced by the extensive area covered by the intermediate counties (see Figure 5). Over the study period, Geermu, Qumarlêb, Zhidoi, and Amdo consistently exhibited the highest ESV levels, while Gangcha, Tianjun, Nagqu, and Damxung reached this level by 2020.

During the first phase (1990–2000), despite a total ESV increase of 8.97% across the entire region, seven counties experienced a decline, with all but Dachaidan located under Xining’s jurisdiction (see Figure 6). Between 2000 and 2010, the ESV of the study area exhibited minimal change, increasing by less than 1%. As a result, half of the counties showed a declining ESV, with Nagqu experiencing the largest absolute decrease. The value of ecosystem services in all counties under Lhasa’s jurisdiction declined, indicating that the human activities in Lhasa lagged behind that in Xining. During the final phase (2010–2020), the total ESV increased significantly by 25.13%, yet ten counties still saw declines in ESV, with Chengdong experiencing the largest decrease at 27.90%. Overall, during 1990–2020, the ESV of QTRR increased by 37.67%, but nine counties still experienced a decline. Chengdong and Chengbei had the steepest declines, followed by Chengguan and Doilungdêgên, while Huangyuan showed a modest decrease of 3.71%. ESV in Gangcha and Damxung more than doubled, and significant increases were also observed in Haiyan and Chengxi, with both surpassing 50%.

4.3. Coordination Development Between Eco-Environment and Economy

Coordination development between the eco-environment andeconomic growth is essential for sustainable development. Overall, the environment and economic development in the QTRR have shown increasing levels of coordination. In detail, the coupling coordination degree between economy and environment presents peak in the center and low at the extremes in the QTRR (Figure 7). The number of counties in severe disorder has steadily decreased, with many transitioning to mild disorder, and some regions even achieving moderate coordination. In 1990, except Geermu, Amdo, and Zhiduo, the rest of the study area showed serious disorder between economic development and ecological environment. In 2000, Qumarlêb county had entered the level of mild disorder; by 2010, Geermu had risen to primary coordination, while the number of districts experiencing serious disorder had dropped to 10; by 2020, there were only five severely disordered regions—Ulan, Chengzhong, Dachaidan, Huangyuan, and Dagzê. The relationship between economic development and ecological environment in Geermu has risen to the intermediate coordination stage, indicating that the regional economic development is accompanied by the improvement of the ecological environment, and the two aspects are in a sound state of coordination. In addition to Geermu, Nagqu County ranks the highest in the environment-economic coordination degree (0.3800), followed by Gangcha, Delingha, and Amdo. Dagzê has the lowest ecological-economic coordination degree of 0.1499. The ecological quality of Dagzê County has been deteriorating, evidenced by a 4.28% decrease in ESV between 1990 and 2020, which has had a significant impact on its economic development.

The conflict between economic growth and eco-environment is more notable in the counties under the jurisdiction of Xining and Lhasa. Compared to other areas in the QTRR, these regions have larger populations and economies but are constrained by limited land resources due to their location in river valleys. Although considerable progress has been made in ecological conservation and a series of ecological restoration projects have been carried out, the ecological-economic coordination development is still not optimistic. Most counties have an ecological-economic coordination degree below 0.4. Even in 2020, there are five counties experiencing serious disorder between ecological environment and economic growth. Economic growth is a risk to the ecological environment. We should pay more attention to ecosystem protection and encourage the construction of ecological civilization in the process of the QTRR development.

5. Discussion and Policy Implications

5.1. Discussion

The 37.67% increase in ESV (1990–2020) underscores the Qinghai–Tibet Railway Region’s (QTRR) resilience under coupled climatic and policy drivers. On the whole, grassland and bare land decreased, while wetland and sand greatly expanded in the QTRR. Wetland and river expansions—responsible for 90% of ESV growth—mirror trends observed in other high-altitude regions undergoing warming [69,70], yet contrast sharply with ESV declines in lowland urban corridors. Notably, the coupling coordination degree (D) improved by 32–68% in 17 counties, reflecting the efficacy of China’s grassland subsidies and grazing bans. However, the melting of permafrost and the rapid reduction in solid water (ice and snow) have further increased the disaster risk and caused an increase in surface instability, posing a threat to the ecological barrier and engineering construction in the QTP.

The analysis results enable us to locate ecological problems in geographic space accurately. By 2020, most counties have come out of serious disorder between economy and ecosystem, while the economic development and ecosystem services of a few counties are still in serious disorder. Persistent issues remain in Ulan, Chengzhong, Dachaidan, Huangyuan, and Dagzê. To avert irreversible degradation, adaptive policies must prioritize climate-resilient zoning and livestock-forage equilibrium, as demonstrated in Geermu’s success case. Geermu and Qumarlêb are taken as examples to conduct a discussion and elucidate policy implications below.

Geermu’s coordination degree surged from 0.2295 (2000) to 0.6626 (2020) marking the highest improvement in QTRR. This success stems from three synergistic interventions: (1) Strategic Land Use Transitions: Between 1990 and 2020, grassland coverage expanded by 12% (Table 1), while wetland and river areas grew by 28%, directly boosting ESV by USD 4.74 billion. These changes correlate with a 45% reduction in soil erosion rates, as NDVI-adjusted vegetation productivity increased by 19%. (2) Policy Integration: Geermu pioneered a “payment for ecosystem services” (PES) system to subsidize degraded grassland restoration (2015–2020). This policy reduced livestock density while increasing herder incomes through ecotourism and carbon credit programs. (3) Community Engagement: public welfare positions were created for ecological monitoring, ensuring local participation in conservation. Training programs shifted pastoralists to non-grazing livelihoods, decoupling economic growth from land degradation. This model demonstrates that coupling strict ecological thresholds (e.g., livestock–forage balance) with inclusive economic incentives can reconcile conservation and development—a lesson critical for lagging counties like Dagzê, where ESV declined by 4.28% despite GDP growth

Qumarlêb demonstrated moderate ecological-economic coordination growth, with its index rising from 0.1958 to 0.2641 during 1990–2020. In 1990, grassland, covering 55.92% of the area, was the dominant type of land use in Qumarlêb, followed by bare land. From 1990 to 2020, grassland expanded and bare land reduced significantly, accounting for 15.70% and 17.99% of the administrative area, respectively, resulting in an ESV increase of USD 1709.3 million. The most significant land use change was the conversion of a majority of bare land into grassland. Located in the southwest of Qinghai Province, Qumarlêb’s strategic geographical position earns it the title “the first county at the sources of rivers”. However, from 1990 to 2010, extensive overgrazing in many pastoral areas led to significant reductions in quality forage, grassland degradation, desertification, and decreased grassland productivity. Long-term trampling and soil hardening resulted in decreased organic matter and nitrogen content, with a significant increase in sand content. During 2010–2020, Qumarlêb county implemented the concept of ecological and environmental protection, thoroughly carried out ecological restoration projects, took comprehensive control of grazing ban on grassland, and made great achievements. Consequently, establishing a wildlife database in Qumarlêb County is essential to highlight its ecological status, enhance herdsmen’s understanding of ecological protection, and further promote grassland ecosystem restoration.

Experience gained from Geermu and Qumarlêb shows that the improvement of eco-economic coordination depends on precise policy design (such as zoning and classification compensation), community endogenous motivation (such as cooperative mode), and technology enabling management (such as NDVI dynamic monitoring). The five regions with low coordination (Ulan, Chengzhong, Dachaidan, Huangyuan, and Dagzê) should abandon the “one-size-fits-all” policy and instead adopt differentiated, data-driven intervention strategies to achieve sustainable development in the special ecological barrier area of the Tibetan Plateau. The “high in the center, low on edges” coordination pattern (Figure 7) mirrors precipitation gradients and infrastructure density. Counties in arid central zones (e.g., Dachaidan) require decentralized water management, while humid peripheries (e.g., Damxung) need tourism regulation to curb habitat fragmentation. Tier fiscal transfers based on coordination indices—high-performing counties (e.g., Geermu) receive bonuses for innovation, while lagging counties receive conditional grants tied to grassland NDVI targets. Scale up Geermu’s PES model through carbon markets, ensuring transparent benefit-sharing with herders. Future policies must embed dynamic ecological thresholds to address nonlinear climate impacts.

This study primarily relies on land use/cover and NDVI data, which may oversimplify complex ecological processes such as biodiversity dynamics and soil microbial interactions. While the equivalent factor method captures broad trends in ecosystem service value, it does not explicitly model species-level contributions or nonlinear ecological feedback. These limitations highlight the need for future integration of high-resolution biodiversity data and process-based models to refine assessments in fragile alpine ecosystems.

5.2. Policy Implications

Considering the current ecological and economic conditions of the Qinghai–Tibet Railway Region (QTRR), a series of targeted measures are necessary to advance sustainable regional development. Strengthening ecological monitoring capabilities is essential for providing a solid foundation for precise environmental protection efforts. Establishing a comprehensive monitoring network that integrates remote sensing, on-the-ground ecological surveys, and real-time data analytics can significantly improve the ability to detect ecosystem changes and assess environmental risks. A regional ecological database should be developed to facilitate data-driven policymaking and early warning mechanisms, ensuring that ecological degradation is promptly addressed. Ecological compensation mechanisms should be refined to ensure stable financial support for conservation initiatives. Diversifying funding sources is crucial, with a particular emphasis on implementing an “Ecological Benefit Tax” (EBT) that adheres to the “beneficiary pays” principle. Direct beneficiaries of ecosystem services, such as urban water utilities, hydroelectric plants, and tourism enterprises, should contribute to compensation funds for ecological protection. Additionally, performance-based compensation should be introduced to encourage sustainable land management. Linking financial incentives to measurable ecological improvements, such as increased vegetation cover or reduced soil erosion, can enhance the effectiveness of these mechanisms. Exploring carbon credit trading opportunities for grassland restoration projects can provide alternative funding streams while promoting climate mitigation efforts.

Land use management must be tailored to the region’s specific geographical and ecological characteristics to maintain ecosystem integrity and improve resilience against environmental pressures. Ensuring continuity and integrity in land use patches is crucial for maintaining ecosystem services [70]. In surrounding areas, multiple, fragmented patches of ecological land can offer enhanced ecosystem service delivery capabilities [71,72]. In urban and rural development zones, compact and resource-efficient land use should be prioritized, while urban sprawl and excessive rural settlement expansion must be strictly controlled. Efforts should focus on optimizing the use of existing construction land, improving land use intensity, and ensuring a balanced distribution of ecological land, such as forests, grasslands, and wetlands, around human settlements. In regions where grassland degradation is severe, implementing rotational grazing systems, expanding artificial forage cultivation, and enhancing grazing ban subsidies can alleviate ecological pressure while supporting sustainable livestock production. Establishing public welfare employment opportunities in grassland management and conservation can also provide economic alternatives for local communities. Given that increasing ecosystem service values in the QTRR are largely driven by expanding surface water, climate resilience strategies must be integrated into regional planning to mitigate risks associated with glacial melt and permafrost degradation. Establishing protected buffer zones around critical water resources, adopting advanced permafrost stabilization techniques in infrastructure projects, and promoting low-carbon development strategies are crucial for maintaining long-term environmental stability. Renewable energy investments, particularly in solar and wind power, should be expanded to reduce reliance on fossil fuels and limit the region’s carbon footprint.

Furthermore, greater attention should be given to protecting ecosystem service capacity in gently sloping areas, as they are particularly vulnerable to human exploitation. Implementing strict land use zoning policies that limit construction in ecologically sensitive zones can help mitigate environmental degradation. Restoration projects focusing on afforestation, erosion control, and soil stabilization should be prioritized in these areas to maintain ecosystem functions and prevent land degradation. Given that gentle slopes are often hotspots for urban expansion and agricultural intensification, sustainable land use practices, such as contour farming and agroforestry should be promoted to balance economic development with ecological conservation.

To ensure the long-term success of these strategies, it is essential to strengthen regional cooperation and knowledge exchange. Drawing lessons from ecological civilization initiatives in other provinces and integrating best practices into policy frameworks can help refine governance approaches. Public engagement and education should also be emphasized, fostering a stronger societal commitment to sustainable development. By implementing these targeted policy measures, the QTRR can achieve a more sustainable balance between ecological conservation and economic growth, ensuring that the region remains an environmental stronghold while supporting high-quality development.

6. Conclusions

• The ecosystem service value (ESV) in the Qinghai–Tibet Railway Region (QTRR) increased significantly from 1.44 × 10¹¹ to 1.98 × 10¹¹ USD between 1990 and 2020, with grasslands contributing the largest share (42.45%) and regulating services accounting for 73.03% of the total ESV in 2020.
• The 11 categories of ESV exhibited distinct trends during this period: hydrological regulation and water provision showed the highest growth, while nutrient cycling and material provision experienced lower increases.
• Spatially, ESV per unit area was generally lower in the middle and higher areas along the edges, with the highest values concentrated in the southwest and northeast regions of the QTRR.
• Ecological-economic coordination has improved across all counties, with Geermu advancing to the intermediate coordination stage. However, the overall coordination level remains low, and economic growth still exerts pressure on the ecological system, posing sustainability challenges.
• While the overall ESV has increased, this growth is primarily driven by the expansion of water bodies rather than broad improvements in ecosystem health. Grassland degradation and land desertification remain major ecological concerns.
• Effective ecological restoration and grassland protection policies must be reinforced to enhance degraded grassland ecosystems and sustain ecosystem service capacity.
• Comprehensive management strategies are required to mitigate the impact of grassland degradation, including limiting overgrazing, enhancing natural grassland protection, and adopting sustainable animal husbandry practices.
• A multi-faceted approach integrating desertification control, grazing regulation, and grassland conservation is essential to halt degradation and ensure long-term ecological stability in the QTRR.