Coupling Coordination Between Ecological Environment and Tourism Economy in Xinjiang

Abstract

This study examines the Xinjiang Uygur Autonomous Region as a critical case study, constructing comprehensive evaluation frameworks for both ecological environment and tourism economy. We calculate the integrated development levels of both systems from 2010 to 2024, employing entropy weighting to derive composite development indices, Coupling Coordination Degree modeling to quantify the intensity and quality of system interactions, Relative Development Degree modeling to characterize coordination typologies and developmental asymmetries, and Grey Relational Analysis to identify key driving factors. Our findings reveal that although the coupling coordination of Xinjiang’s tourism–ecological system has transitioned from “mild imbalance” to “marginal coordination”, the system exhibits pronounced vulnerability and persistent “tourism-lag” dynamics. To effectively leverage the current “strategic window” of ecological surplus, we propose a multi-dimensional transformation pathway: (1) enhancing digital resilience through intelligent monitoring systems to mitigate external mobility shocks; (2) optimizing spatial connectivity via a “fast transit, slow travel” infrastructural paradigm; (3) institutionalizing micro-scale ecological governance to position oasis cities as sustainable “ecological gateways”; and (4) catalyzing deep cultural-tourism integration, shifting from scale-driven sightseeing to value-driven Silk Road heritage experiences. These pathways furnish a clear blueprint for Xinjiang to achieve high-quality, sustainable regional tourism development while maintaining its strategic positioning as a northwestern ecological security barrier.

1. Introduction

In 2005, during his tenure in Zhejiang Province, President Xi Jinping first articulated the doctrine that “lucid waters and lush mountains are invaluable assets,” emphasizing that ecological conservation and economic development are not inherently contradictory but can achieve synergistic gains [1]. The Report to the 20th National Congress of the Communist Party of China further delineates that promoting green development and fostering harmonious coexistence between humanity and nature constitute essential requirements for Chinese modernization. In this context, tourism operates as an environmentally dependent industry and serves as a primary catalyst for economic growth [2]. Its evolution remains fundamentally contingent upon pristine ecological resources, whilst generated revenue streams can be reinvested into environmental protection, thereby establishing a virtuous cycle of “ecology-tourism-conservation.” Consequently, investigating the coupling relationship between ecological environment and tourism economy, elucidating patterns of coordinated evolution, and proposing optimization strategies hold profound significance for implementing national ecological civilization strategies and advancing high-quality regional development.

The Xinjiang, located on China’s northwestern frontier, functions both as a critical ecological security barrier for the nation and as the core region of the Silk Road Economic Belt. Its unique combination of glacial, oasis, and desert ecosystems provides an exceptional ecological and geographical foundation for tourism development. In recent years, following the comprehensive implementation of the “Tourism Revitalizes Xinjiang” strategy, the regional tourism industry has experienced sustained and rapid expansion, reflected in continuous growth in tourist arrivals and tourism revenue. Consequently, tourism has increasingly emerged as a strategic pillar of regional economic development, playing an important role in stabilizing economic growth, generating employment opportunities, and enhancing overall regional vitality. Nevertheless, Xinjiang’s continental arid climatic conditions render its ecological environment highly fragile and environmentally sensitive. Persistent challenges, including water resource scarcity, land desertification, and frequent dust storms, continue to intensify the tension between tourism expansion and ecological protection. Under these circumstances, achieving a sustainable and high-quality balance between tourism economic growth and ecological environmental conservation has become a central developmental priority for the region. Therefore, systematically clarifying the coupling coordination relationship between the ecological environment and tourism economy, together with its underlying evolutionary mechanisms, is of substantial theoretical and practical significance. Such analysis not only contributes to understanding the pathways of regional economic and social transformation in Xinjiang, but also carries broader strategic implications for strengthening ecological security and promoting sustainable development across western China.

Scholarly inquiry into the relationship between tourism economy and ecological environment has a well-established history. Regarding the impact of tourism economy upon ecological systems, early international research predominantly focused on unidirectional mechanisms, coalescing around two divergent paradigms. The first, the “negative impact thesis,” posits that tourism activities precipitate resource overexploitation, environmental pollution, ecosystem destruction [3], expanded regional ecological footprints, and potentially irreversible ecological degradation [4]. The second, the “positive effects thesis,” emphasizes that tourism can furnish financial resources and institutional incentives for environmental protection [5], fostering ecological improvement through revenue reinvestment into conservation projects, enhanced public environmental awareness, and community engagement in governance.

However, this binary analytical framework exhibits significant limitations. First, both perspectives derive from specific socioeconomic contexts, rendering their generalizability and transferability questionable. Second, these opposing views tend to magnify unidirectional effects while neglecting the dynamic feedback mechanisms between tourism and ecology. Specifically, the paradoxical relationship wherein tourism development may degrade ecological systems while ecological improvement may conversely constrain tourism expansion. Third, early research relied predominantly on macro-level qualitative analysis, lacking micro-level empirical substantiation. Consequently, this unidirectional causal framework has been progressively superseded in academic discourse.

Concurrently, scholars have universally acknowledged the foundational supportive role of ecological environments in tourism economies; superior environmental quality constitutes a core attractor for tourism destinations [6,7], necessitating sustainable tourism policies and investment in clean energy and green infrastructure to align with sustainable development objectives [8], with major tourist destinations adopting environmentally friendly technologies, infrastructure, and consumption patterns to foster sustainable economic growth [9]. Nevertheless, while this recognition holds significance, it remains largely at the stage of qualitative assertion, lacking rigorous quantitative identification of causal mechanisms. Critical questions remain inadequately addressed: To what extent must ecological quality improve to effectively stimulate tourism revenue? Do threshold effects exist regarding the intensity of impacts that varying degrees of ecological degradation exert upon tourism competitiveness? Such questions have received scant attention in extant literature.

In China, scholarly inquiry into the relationship between ecological environment and tourism economy originated during the 1980s, with early investigations predominantly characterized by qualitative analysis and theoretical discourse. Since the turn of the twenty-first century, alongside rapid tourism expansion and advancing ecological civilization construction, relevant research has proliferated substantially, with methodological approaches becoming progressively diversified and quantitatively oriented. This evolution is manifested across theoretical framework development, research scales and temporal scopes, analytical dimensions, and methodological refinement.

Regarding theoretical framework evolution, three generational shifts are discernible. First-generation research (2000–early 2010s) centered upon the antagonistic relationship between “tourism development” and “ecological destruction” [10], employing simplistic positive-negative effect balance models that lacked systemic conceptualization. The fundamental limitation lay in excessive simplification of internal structures and interaction pathways within these two complex systems. Second-generation research (2010–2020) introduced coupling coordination theory [11], transcending the binary opposition framework; however, most investigations remained confined to static coordination assessment, including calculating Coupling Coordination Degree values at specific temporal points, while exhibiting pronounced deficiencies in examining evolutionary mechanisms, regional differentiation, and driving factors. Third-generation research (post-2020) has gradually shifted toward integrated “tourism-economy-ecology” tripartite analysis, representing a significant theoretical advancement. Scholars have begun elucidating the empowering role of ecological resilience in enhancing industrial quality from a systemic coupling perspective [12], proposing three-dimensional culture–tourism–ecology coupling models [13], and developing analytical frameworks for non-coordinated coupling drivers [14]. Nevertheless, regarding indicator system construction, extant literature predominantly employs standardized generic metrics, such as conventional industrial “three wastes” emissions. Such “one-size-fits-all” measurement approaches lack adaptability when applied to specific geographical environments. Particularly in arid fragile zones, where rigid water resource constraints, natural vegetation coverage, and desertification threats constitute core determinants of systemic coupling ceilings, most existing indicator systems fail to sensitively capture these ecosystem-specific variables.

Regarding research scales and temporal scopes, extant scholarship has extensively encompassed national macro-level analyses alongside provincial, municipal, and county-level micro-scale investigations [15,16,17,18,19]. National-scale macro-analyses [12] typically benefit from data accessibility and yield conclusions of generalizable significance. Critically examined, however, existing literature targeting specific regions, particularly central and western China to exhibit pervasive temporal lag. Taking Xinjiang as exemplar, available studies predominantly employ pre-2021 data, thereby neglecting not only the severe ecological perturbations accompanying post-pandemic explosive tourism rebound but also failing to scientifically assess the substantive reshaping of regional “ecology-tourism” coupling evolutionary trajectories resulting from recent comprehensive deepening of the “Tourism Revitalizes Xinjiang” strategy. Consequently, research conclusions prove inadequate for guiding contemporary policy practice.

Concerning analytical dimensions, although coverage remains broad, a pronounced “measurement-heavy, mechanism-light” bias prevails. While existing research addresses individual dimensions including coordination degree quantification, spatial differentiation, and factor identification [20,21,22,23], longitudinal integration across these dimensions remains deficient. Specifically, analyses of evolutionary processes have largely remained confined to descriptive characterizations of temporal trends, with limited attention devoted to the underlying factors driving such transformations. Although existing studies have identified patterns of spatial heterogeneity, the deeper causal mechanisms shaping these variations, including investment regimes and industrial structures, have not been sufficiently examined. In addition, the identification of constraints and driving forces has predominantly relied on single factor analyses or straightforward combinations of multiple variables, while the complex interactions, dynamic feedback relationships, and synergistic effects among these factors remain substantially underexplored.

Regarding methodological approaches and refinement directions, despite extensive employment of Coupling Coordination Degree models and their variants for measurement, supplemented by Grey Relational Analysis or geographical detectors for spatial differentiation [24,25], most investigations remain at stages of “characteristic description” and “superficial correlation analysis.” Examination of deep-seated driving mechanisms underlying coupling coordination evolution appears relatively underdeveloped. Most existing studies conceptualize ecology and tourism as relatively closed systems, without adequately incorporating critical external variables, such as transportation infrastructure network development, the intensity of environmental protection investment, and the scale effects of the tourism industry, into an integrated causal analytical framework. As a result, the explanatory capacity of current research remains limited in revealing the complex nonlinear mechanisms and dynamic processes underlying coordinated development between ecological and tourism systems.

Synthesizing the above analysis, while extant scholarship has established robust theoretical and methodological foundations for the present study, significant deficiencies persist: First, indicator systems lack regional adaptability. Existing research has predominantly relied on generalized analytical frameworks, demonstrating substantial limitations in addressing the distinctive ecological constraints characteristic of arid regions, particularly water resource stress, desertification, and the scarcity of natural vegetation. Such limitations weaken the scientific rigor and contextual specificity of ecological environment assessments. As a representative continental arid region, Xinjiang urgently requires differentiated indicator systems capable of incorporating region specific ecological characteristics. In addition, the temporal scope of current studies does not adequately correspond with contemporary realities. Most Xinjiang focused research concludes around 2021 and therefore fails to capture the effects of major developments, including the disruptions associated with the COVID 19 pandemic, the rapid rebound of tourism during the post pandemic period, and the comprehensive implementation of the “Tourism Revitalizes Xinjiang” strategy. Consequently, many existing conclusions may no longer accurately reflect the current dynamics of tourism ecology coupling and coordination in Xinjiang, thereby limiting their practical policy relevance. Furthermore, the identification of driving forces remains fragmented, while mechanism-based analyses continue to be relatively superficial. Existing studies largely concentrate on determining the level of coupling coordination itself, yet provide insufficient explanation regarding the underlying causes and operational pathways shaping such outcomes. Although factors such as transportation infrastructure, environmental protection investment, and tourism industrial scale have been widely discussed, most analyses remain confined to single factor examinations or simple correlation assessments, lacking systematic investigation of inter factor interactions, compound effects, and dynamic mechanisms. Finally, both theoretical contextualization and the precision of policy recommendations remain insufficient. Current interpretations of coupling coordination evolution are still largely framed at a universal level, with limited theoretically grounded discussion of the unique characteristics, developmental stages, and structural constraints shaping the tourism ecology relationship in Xinjiang. Correspondingly, policy recommendations tend toward excessive macro-level abstraction, with inadequate elaboration of specific operational pathways, implementation conditions, and feasibility, proving difficult to furnish operationally actionable reference for local decision-makers.

Based on the identified research gaps, this study seeks to systematically examine the dynamic evolutionary logic underlying the relationship between the ecological environment and tourism economy in Xinjiang, with particular attention to the following research questions:

RQ1: What spatiotemporal evolutionary characteristics and developmental patterns characterize the Coupling Coordination Degree (CCD) between Xinjiang’s ecological environment and tourism economy during the period from 2010 to 2024?

RQ2: Which factors constitute the principal driving forces shaping the evolutionary dynamics of the coupling coordination relationship between the ecological environment and tourism economy in Xinjiang, and do these factors exhibit significant stage specific differences in terms of influence intensity and operational mechanisms across different developmental phases?

RQ3: Does a persistent tourism lag phenomenon or broader systemic imbalance exist within Xinjiang’s tourism ecology system, and if so, what are the principal constraining factors underlying such imbalances? Furthermore, how can different regions within Xinjiang adopt differentiated optimization pathways to simultaneously strengthen ecological environmental protection and tourism economic development, thereby promoting higher quality and more sustainable regional development?

To address these inquiries, this study takes Xinjiang as its analytical subject, conducting systematic measurement of coupling coordination levels between ecological environment and tourism economy while probing potential influencing factors throughout their developmental trajectories. Compared with extant research, the marginal contributions of this study are manifested in four dimensions: First, regional specificity of the indicator system. This study transcends the predominant emphasis upon industrial pollution metrics in available literature, constructing a comprehensive indicator framework incorporating arid-zone-specific ecological elements including water resource stress and natural vegetation coverage, thereby enhancing regional adaptability and scientific validity of ecological environment assessment in alignment with Xinjiang’s continental arid-zone characteristics. Second, extension of research timeliness. This study extends the sample period through 2024, representing one of the most temporally current investigations in this domain, capable of systematically capturing the phasic impacts of major events—including COVID-19 shocks, post-pandemic explosive tourism recovery, and comprehensive advancement of the “Tourism Revitalizes Xinjiang” strategy—upon coupling coordination evolutionary trajectories, effectively remedying temporal deficiencies in extant research. Third, deepening of driver mechanism analysis. Building upon coupling coordination measurement, this study further introduces the Relative Development Degree model to dissect the directionalities and intensities of key variables including transportation infrastructure, environmental protection investment, and tourism scale and benefits, clarifying the internal logic and causal relationships influencing synergistic ecological-tourism development. Fourth, policy orientation of research conclusions. Grounded in Xinjiang’s “Tourism Revitalizes Xinjiang” strategic context, this study tightly integrates research findings with regional developmental practice, furnishing local governments with more operationally actionable decision-making reference for promoting coordinated ecological protection and tourism economic development.

2. Materials and Methods

2.1. Study Area

The Xinjiang Uyghur Autonomous Region is situated in northwestern China, deep within the Eurasian hinterland, serving as a pivotal corridor linking China to Central Asia. This vast jurisdiction encompasses 14 prefecture-level administrative divisions. Geomorphologically, the region exhibits the distinctive “Three Mountain Ranges and Two Basins” configuration, wherein alpine, oasis, and desert ecosystems form an intricate spatial mosaic. Despite substantial ecological heterogeneity, the overall environmental system demonstrates pronounced fragility.

Northern Xinjiang is dominated by steppe, forest, and lacustrine wetland landscapes. The Ili River Valley and adjacent areas benefit from comparatively favorable hydrothermal regimes, while the northern Tianshan piedmont oases support dense agricultural and urban settlements, functioning as principal zones for demographic concentration and industrial activity. By contrast, Southern Xinjiang is typified by the Taklimakan Desert and its peripheral oases, characterized by extreme aridity, severe precipitation deficits, and acute water resource constraints; nevertheless, the desert landscapes, Populus euphratica forests, and oasis ecosystems possess distinctive visual identities.

Xinjiang hosts a diverse array of natural and cultural tourism assets, including alpine glacial and forest-lake systems (e.g., the Tianshan Mountains and Kanas Lake) and historic-cultural resources with multi-ethnic characteristics (e.g., Turpan and Kashgar), forming a hierarchically structured tourism resource system of substantial appeal. However, the region faces acute ecological challenges, including land desertification, aeolian sandstorms, limited carrying capacities in oasis-desert transition zones, and degradation of grassland and wetland ecosystems. Consequently, examining the coupling coordination between Xinjiang’s ecological environment and tourism economy, alongside its driving mechanisms, holds significant practical implications for transitioning toward green, high-quality tourism development, enhancing regional ecological governance efficacy, and supporting national ecological security objectives in western China.

2.2. Indicator Framework Development and Data Acquisition

The necessity of constructing a multi-dimensional evaluation framework stem from the inherent complexity and non-linear synergistic characteristics governing tourism–ecology relationships in arid frontier regions. Univariate assessment approaches prove insufficient for capturing the dynamic trade-offs between economic expansion and environmental preservation. Consequently, this study establishes a comprehensive indicator system comprising 32 metrics, providing a robust empirical foundation for subsequent Coupling Coordination Degree (CCD) quantification.

For the ecological environment subsystem, the Pressure–State–Response (PSR) analytical paradigm [26] was adapted to address Xinjiang’s distinct environmental vulnerabilities, including land desertification processes, aeolian sandstorm activity, and grassland/wetland degradation. The resulting framework encompasses three functional components (

Table 1. Evaluation index and weight of ecological environment development level in Xinjiang. Source: Calculated by the authors based on Xinjiang Statistical Yearbooks.

Dimensional Layer	Subdimensions	Specific Indictors	Direction	Weight
Ecological environment	A. Ecological environment pressure	A1 Industrial SO2 emissions (104 t)	Negative (−)	0.0654
		A2 Industrial particulate matter emissions (104 t)	Negative (−)	0.0582
		A3 Industrial wastewater discharge (104 t)	Negative (−)	0.0521
		A4 Industrial solid waste generation (104 t)	Negative (−)	0.0498
		A5 Municipal wastewater discharge (104 t)	Negative (−)	0.0412
		A6 Forest pest and disease affected area (104 hm2)	Negative (−)	0.0387
		A7 Forest fire affected area (hm2)	Negative (−)	0.0215
		A8 Direct economic loss from natural disasters (108 RMB)	Negative (−)	0.0346
	B. Ecological environment state	B1 Forest Coverage (%)	Positive (+)	0.0612
		B2 Air quality excellence rate in major cities (%)	Positive (+)	0.0443
		B3 Nature reserve area (104 hm2)	Positive (+)	0.0425
		B4 National nature reserves (number)	Positive (+)	0.0398
		B5 Per capita water resources (m3 capita−1)	Positive (+)	0.0287
		B6 Urban per capita green space (m2)	Positive (+)	0.0556
		B7 Green coverage ratio in built-up areas (%)	Positive (+)	0.0689
	C. Ecological environment response	C1 Annual afforestation area (104 hm2)	Positive (+)	0.0543
		C2 Forest pest and disease control rate (%)	Positive (+)	0.0578
		C3 Environmental governance investment as share of regional GDP (%)	Positive (+)	0.0321
		C4 Total environmental governance investment (108 RMB)	Positive (+)	0.0416
		C5 Urban sewage treatment investment (104 RMB)	Positive (+)	0.0604
		C6 Harmless disposal rate of municipal solid waste (%)	Positive (+)	0.0287
		C7 Greening and ecological protection investment (104 RMB)	Positive (+)	0.0226

). The Pressure dimension captures anthropogenic and natural stressors imposed upon ecosystems, encompassing industrial activities, domestic emissions, and catastrophic disturbances [27]. Eight metrics quantify these perturbations: industrial sulfur dioxide (SO₂) emissions, particulate matter discharge, industrial wastewater effluent, forest pest and disease incidence, and areas affected by forest and grassland conflagrations. The State dimension reflects the existing stock and quality of environmental resources under prevailing stress conditions, operationalized through seven indicators including nature reserve quantity and spatial extent, and per capita water resource availability. The Response dimension represents proactive governance interventions and societal investments, captured through seven metrics: annual afforestation area, forest pest and disease control efficacy, aggregate environmental pollution treatment investments, and municipal wastewater treatment expenditures.

For the tourism economy subsystem, a tripartite evaluation framework was established (

Table 2. Xinjiang tourism economic development level evaluation index and weight. Source: Calculated by the authors based on Xinjiang Statistical Yearbooks.

Dimensional Layer	Subdimensions	Specific Indictors	Direction	Weight
tourist economy	D. Tourism scale and benefit	D1 Domestic tourism receipts (108 RMB)	Positive (+)	0.1498
		D2 International tourist arrivals (persons)	Positive (+)	0.1459
		D3 Domestic tourist arrivals (104 persons)	Positive (+)	0.1027
		D4 International tourism receipts (104 USD)	Positive (+)	0.1200
	E. Tourism facilities and services	E1 Number of travel agencies (number)	Positive (+)	0.0916
		E2 Number of star-rated hotels (number)	Positive (+)	0.0835
		E3 Railway network density (km/104 km2)	Positive (+)	0.0762
		E4 Road network density (km/104 km2)	Positive (+)	0.0788
	F. Tourism resources and social support	F1 Number of A-level and above scenic spots (number)	Positive (+)	0.0819
		F2 Xinjiang’s regional GDP (108 RMB)	Positive (+)	0.0694

), acknowledging the sector’s characteristic high multiplier effects and extensive intersectoral linkages [28]. The Tourism Scale and Economic Performance dimension quantifies direct economic contributions through four metrics, including domestic tourism revenue and visitor arrivals. The Infrastructure and Service Capacity dimension captures the material foundations and hospitality supply dynamics underpinning destination reception capabilities, operationalized through four indicators: travel agency density, star-rated hotel establishments, and railway and highway network densities. The Resource Endowment and Socioeconomic Support dimension reflects core tourism asset development levels and the macroeconomic context enabling sectoral expansion, measured via two indicators: designated scenic areas (Grade A and above) and regional gross domestic product.

This investigation utilizes secondary data sources, with the analytical timeframe spanning 2010–2024. Data were compiled from the Xinjiang Statistical Yearbook, China Statistical Yearbook, China Tourism Statistical Yearbook, and China Environmental Statistical Yearbook (2011–2025 editions). The apparent temporal discrepancy between publication years and data coverage reflects inherent publication lags characteristic of official statistical compendia; the 2011–2025 editions comprehensively encompass historical statistics for the 2010–2024 period. Missing values for individual years were imputed via linear interpolation to ensure temporal continuity and series completeness.

2.3. Method Selection and Model Construction

To scientifically measure the interactive relationship between the ecological environment and tourism economy and its driving mechanisms, this study constructs a comprehensive analytical framework integrating “entropy weighting—Coupling Coordination Degree measurement—Relative Development Diagnosis—Grey Relational Analysis.” This framework respectively corresponds to the research tasks of indicator weighting, coordination level measurement, development imbalance identification, and driving factor analysis, thereby providing a more complete response to the research questions.

2.3.1. Rationale for Methodological Selection

• Method Selection Based on Research Questions

This study primarily addresses three levels of inquiry: first, how to scientifically measure the overall coordination level of the two subsystems—the ecological environment and tourism economy; second, whether development imbalances exist during the coordinated evolution of the two subsystems and their specific manifestations; and third, which factors exert critical influences on the coordinated evolution of the systems. These questions respectively correspond to research tasks involving multi-indicator comprehensive evaluation, system coordination relationship measurement, and complex factor association identification. Consequently, this study requires a methodological combination capable of achieving objective weighting, characterizing system interactions, and further identifying driving mechanisms, rather than relying on a single analytical tool.

2.

Methodological Adaptability Analysis Based on Data Characteristics

Data related to Xinjiang’s ecological environment and tourism economy exhibit pronounced heterogeneity and stage-specific characteristics, primarily manifested in three aspects. First, the degree of dispersion varies substantially across different indicators: certain tourism economic indicators fluctuate significantly, whereas ecological environmental indicators remain relatively stable. Equal weighting or subjective weighting approaches would compromise the objectivity of comprehensive evaluation results; therefore, this study employs the entropy method for objective weighting [1]. Second, the interactive relationship between the ecological environment and tourism economy is nonlinear, complex, and multi-sourced, with relevant influencing factors often exhibiting simultaneous synergistic and competitive characteristics, rendering grey system theory appropriate for association analysis. Third, the system evolution during the study period demonstrates distinct stage-specific features, and coordination mechanisms may differ across temporal segments, requiring selected methods to simultaneously reflect dynamic changes and relative development disparities. Accordingly, this study adopts a methodological system capable of accommodating both holistic measurement and dynamic diagnosis.

3.

Comparative Analysis Based on Alternative Methods

Regarding the measurement of system coordination relationships, this study also examined alternative approaches including Principal Component Analysis (PCA), Structural Equation Modeling (SEM), and Multiple Linear Regression (MLR). Principal Component Analysis is primarily used for indicator dimensionality reduction and cannot directly reflect the interactive coordination degree between systems. Although Structural Equation Modeling can identify complex causal pathways, it imposes relatively stringent requirements on sample size and theoretical assumptions, which does not fully align with this study’s core objective of coordination measurement. Multiple Linear Regression facilitates interpretation but relies heavily on assumptions of linear relationships and variable independence, making it insufficiently adaptable to the nonlinear characteristics of complex systems. In comparison, the Coupling Coordination Degree (CCD) model, Relative Development Model (RDM), and Grey Relational Analysis (GRA) better satisfy the research requirements of this study [21,29].

Based on the above considerations, this study ultimately adopts the Coupling Coordination Degree model as the core measurement tool, combined with the Relative Development Degree model to identify system imbalance states, and further employs Grey Relational Analysis to reveal key driving factors, thereby forming a progressive analytical framework of “measurement—diagnosis—explanation.”

2.3.2. Core Research Methods and Model Construction

Based on the aforementioned methodological rationale, this study first employs the entropy method for objective indicator weighting to calculate the comprehensive ecological environment index and comprehensive tourism economy index. Second, based on these comprehensive indices, the Coupling Coordination Degree model is applied to measure the coordination level of the two subsystems, with the Relative Development Degree model introduced to identify their development imbalance characteristics. Finally, Grey Relational Analysis is adopted to identify the key factors influencing the coordinated evolution of the systems. The specific models are presented below.

• Entropy Weighting and Composite Index Calculation

To circumvent the arbitrariness and subjective bias potentially associated with heuristic weighting approaches, and to ensure the objectivity and empirical validity of evaluation outcomes, entropy weighting was adopted for indicator specification across both ecological and tourism economic dimensions. Within this information-theoretic framework, greater dispersion in indicator data corresponds to lower information entropy, thereby signaling higher information content and consequently greater weighting coefficients in the composite evaluation; conversely, reduced data variability yields elevated entropy and attenuated weights. Assuming the system comprises m evaluation indicators and n observational periods, which constitute the original data matrix. After standardizing the data, the calculation formula of the information entropy ${\mathrm{e}}_{\mathrm{j}}$ of the jth index is as follows:

$${\mathrm{e}}_{\mathrm{j}} = -\mathrm{k}{\displaystyle {\sum }_{\mathrm{i}=1}^{\mathrm{n}}}{\mathrm{p}}_{\mathrm{i}\mathrm{j}}\ln {\mathrm{p}}_{\mathrm{i}\mathrm{j}}$$

(1)

where ${\mathrm{p}}_{\mathrm{i}\mathrm{j}}$ denotes the proportion of the i-th sample value under the j-th indicator, and k = 1/ln(n) represents the normalization constant.

From the derived information entropy values, the coefficient of diversity ${\mathrm{d}}_{\mathrm{j}}$ is computed as ${\mathrm{d}}_{\mathrm{j}} = 1$ − ${\mathrm{e}}_{\mathrm{j}}$, quantifying the discriminative power of each indicator. The weight ${\mathrm{w}}_{\mathrm{j}}$ for the j-th indicator is subsequently determined through normalization of these diversity coefficients.

Employing these objective weights, the composite development indices for the ecological environment (U₁) and tourism economy (U₂) are calculated as the weighted summation of standardized indicators. The formulations are expressed as:

$${\mathrm{U}}_{\mathrm{i}=}{\displaystyle {\sum }_{\mathrm{j}=1}^{\mathrm{m}}}{\mathrm{w}}_{\mathrm{j}} \times {{\mathrm{X}}^{\prime }}_{\mathrm{i}\mathrm{j}}$$

(2)

where ${{\mathrm{X}}^{\prime }}_{\mathrm{i}\mathrm{j}}$ denotes the standardized indicator values. Higher values of ${\mathrm{U}}_{\mathrm{i}}$ indicate superior comprehensive development levels of the respective system.

2.

Coupling Coordination Degree Model

The Coupling Coordination Degree model serves to quantify the intensity of intersystem interactions and the degree of positive feedback between subsystems. We employ this analytical framework to assess the synergistic development trajectories of Xinjiang’s ecological environment and tourism economy. The mathematical specifications are presented as follows:

$$\mathrm{C}=\sqrt{{\displaystyle \frac{{\mathrm{U}}_1\times {\mathrm{U}}_2}{{\left(\frac{{\mathrm{U}}_1+{\mathrm{U}}_2}{2}\right)}^2}}}$$

(3)

$$\mathrm{T}=\mathrm{\alpha }{\mathrm{U}}_1+\mathrm{\beta }{\mathrm{U}}_2$$

(4)

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

(5)

Herein, C represents the coupling degree between the ecological environment and tourism economy, with values bounded within the interval [0, 1]; U₁ and U₂ denote the composite development indices of the ecological and tourism economic systems, respectively; T signifies the comprehensive coordination index; and α and β are weighting coefficients subject to the constraint α + β = 1. Acknowledging that ecological preservation [30] and tourism-driven economic expansion hold equivalent strategic weight in regional high-quality development objectives, this study adopts equal parameter values of α = β = 0.5. The variable D represents the Coupling Coordination Degree, wherein values approaching unity indicate progressively higher levels of systemic synergy.

Drawing upon established classification frameworks [29] and adapted to the empirical context of this investigation, the Coupling Coordination Degree is categorized into the ordinal grades delineated in

Table 3. Coupling Coordination Degree level division. Source: Compiled by the authors.

Coupling Coordination Degree	Rank of Harmony Degree	State of Development Description
[0, 0.1)	Extreme imbalance	The two subsystems exhibited elementary coupling dynamics, verging on mutually antagonistic interactions
[0.1, 0.2)	Severe imbalance	Severe constraints preclude effective inter-systemic interaction
[0.2, 0.3)	Moderate imbalance	Marked internal structural disorder accompanied by pronounced inhibitory effects
[0.3, 0.4)	Mild imbalance	Interactive dynamics are present yet remain at elementary levels
[0.4, 0.5)	Borderline imbalance	Situated at the critical threshold between coordination and imbalance, highly susceptible to regression upon minimal perturbation
[0.5, 0.6)	Marginal coordination	Having crossed the coordination threshold, yet exhibiting negligible synergistic effects, indicative of an incipient developmental stage
[0.6, 0.7)	Primary coordination	Emergent synergistic interactions between subsystems, albeit superficial in nature
[0.7, 0.8)	Intermediate coordination	Intermediate coordination with preliminary establishment of virtuous feedback mechanisms
[0.8, 0.9)	Sound coordination	Marked mutualistic enhancement between subsystems, indicative of high-order developmental trajectories
[0.9, 1.0]	Superior coordination	Achieving profound systemic integration with mutually reinforcing resonance

.

3.

Relative Development Degree Model

Although the Coupling Coordination Degree (CCD) captures overall systemic coordination, it cannot resolve the disparate development rates and relative positions of individual subsystems. To elucidate the constraints on coordinated development, we introduce the Relative Development Degree model, calculating coefficient R as follows:

$$\mathrm{R}={\displaystyle \frac{{\mathrm{U}}_2}{{\mathrm{U}}_1}}$$

(6)

Following Liu et al. [31], we classify relative development into three categories: R ∈ [0, 0.8) (U₂ < U₁) indicates tourism development lags the ecological environment (tourism-lagging type); R ∈ [0.8, 1.2) (U₁ ≈ U₂) indicates concurrent development of both systems; and R ∈ [1.2, +∞) (U₂ > U₁) indicates tourism development outpaces ecological capacity (ecology-lagging type).

4.

Grey Relational Analysis

We employ Grey Relational Analysis to identify key determinants governing the tourism–ecosystem Coupling Coordination Degree in Xinjiang, quantifying relational strength by the geometric similarity of sequence curves. The formulations are as follows:

$${\mathsf{\epsilon }}_{\mathrm{i}}\left(\mathrm{k}\right)={\displaystyle \frac{{\mathrm{m}\mathrm{i}\mathrm{n}}_{\mathrm{i}}{\mathrm{m}\mathrm{i}\mathrm{n}}_{\mathrm{k}}\left|\mathrm{y}\left(\mathrm{k}\right)-{\mathrm{x}}_{\mathrm{i}}\left(\mathrm{k}\right)\right|+\mathrm{\rho }{\mathrm{m}\mathrm{a}\mathrm{x}}_{\mathrm{i}}{\mathrm{m}\mathrm{a}\mathrm{x}}_{\mathrm{k}}\left|\mathrm{y}\left(\mathrm{k}\right)-{\mathrm{x}}_{\mathrm{i}}\left(\mathrm{k}\right)\right|}{\left|\mathrm{y}\left(\mathrm{k}\right)-{\mathrm{x}}_{\mathrm{i}}\left(\mathrm{k}\right)\right|+\mathrm{\rho }{\mathrm{m}\mathrm{a}\mathrm{x}}_{\mathrm{i}}{\mathrm{m}\mathrm{a}\mathrm{x}}_{\mathrm{k}}\left|\mathrm{y}\left(\mathrm{k}\right)-{\mathrm{x}}_{\mathrm{i}}\left(\mathrm{k}\right)\right|}}$$

(7)

Here, ε_i(k) denotes the grey relational coefficient, y(k) − x_i(k) the absolute deviation, and ρ the distinguishing coefficient. Higher relational grades signify stronger drivers of coupling coordination.

3. Results

3.1. Temporal Dynamics of Ecological and Tourism Economic Systems in Xinjiang

Entropy weighting was first applied to objectively specify indicator weights for Xinjiang’s ecological environment and tourism economy over the period 2010–2024. Building upon these specifications, composite development indices for the ecological subsystem (U₁) and tourism economic subsystem (U₂) were constructed via linear aggregation (

Table 4. Measurement of comprehensive development level of ecological environment and tourism economy in Xinjiang. Source: Calculated by the authors based on Xinjiang Statistical Yearbooks.

Particular Year	A	B	C	Comprehensively Ecological U1	D	E	F	Tourism Economy Comprehensive U2
2010	0.1197	0.1132	0.0553	0.2882	0.0088	0.0329	0.0001	0.0417
2011	0.0768	0.0946	0.0942	0.2655	0.0141	0.0286	0.0052	0.0478
2012	0.0699	0.1055	0.1386	0.3140	0.0190	0.0383	0.0077	0.0649
2013	0.0481	0.0893	0.0996	0.2370	0.0214	0.0231	0.0104	0.0549
2014	0.0346	0.0784	0.1242	0.2372	0.0193	0.0283	0.0129	0.0605
2015	0.0486	0.1016	0.1367	0.2866	0.0277	0.0317	0.0138	0.0733
2016	0.1101	0.1345	0.1140	0.3586	0.0405	0.0296	0.0145	0.0846
2017	0.0910	0.1396	0.1159	0.3465	0.0533	0.0278	0.0205	0.1016
2018	0.1115	0.0895	0.0939	0.2949	0.0588	0.0633	0.0262	0.1482
2019	0.1031	0.1018	0.0745	0.2794	0.0848	0.0519	0.0318	0.1686
2020	0.1126	0.0984	0.0797	0.2906	0.0308	0.0628	0.0329	0.1265
2021	0.1129	0.1015	0.0626	0.2770	0.0532	0.0731	0.0453	0.1716
2022	0.1004	0.1314	0.0865	0.3183	0.0470	0.0852	0.0515	0.1837
2023	0.1292	0.1293	0.0973	0.3558	0.1058	0.1163	0.0562	0.2783
2024	0.1249	0.1471	0.0974	0.3694	0.0921	0.1378	0.0559	0.2857

), enabling characterization of their temporal evolutionary trajectories (Figure 1).

3.1.1. Development Trajectory of the Ecological Subsystem

The ecological environment index (U₁) exhibited an overall upward trajectory characterized by oscillatory yet progressive growth throughout the study period, ascending from 0.2882 in 2010 to 0.3694 in 2024. Despite intermittent year-to-year fluctuations, the temporal pattern reflects systematic amelioration in environmental quality. This trajectory does not represent stochastic variation but rather constitutes the concrete manifestation of China’s national ecological civilization construction strategy, particularly the operationalization of the “Two Mountains” doctrine (lucid waters and lush mountains are invaluable assets) within the Xinjiang context. As a critical ecological security barrier and resource-endowed region, Xinjiang has witnessed sustained augmentation of environmental protection investments in recent years, encompassing the implementation of stringent ecological red-line systems, large-scale conversion of cropland to forest and grassland, comprehensive desertification control initiatives, and protection of critical ecological functional zones. The synergistic effects of these governance interventions have incrementally enhanced regional ecological carrying capacity, thereby establishing a robust foundation for the subsequent green development of the tourism sector.

3.1.2. Development Trajectory of the Tourism Economic Subsystem

The tourism economic index (U₂) exhibited a substantially more pronounced upward trajectory accompanied by marked volatility, with its evolutionary pathway demonstrating strong congruence with China’s macro-level tourism development patterns and region-specific perturbations, temporally delineated into three distinct phases.

2010–2019: Phase of Rapid Expansion and Model Exploration. During this interval, the tourism economic index ascended steadily from 0.0418 to 0.1686, reflecting sustained sectoral expansion and enhanced market vitality driven by the national Western Development Strategy, Xinjiang assistance policies, and progressive improvements in regional transportation infrastructure. Throughout this phase, Xinjiang developed diversified tourism product systems through the cultivation of distinctive itineraries, border tourism, and rural tourism offerings, progressively amplifying destination attractiveness. However, this rapid growth was concurrently accompanied by exploratory extensive development models, laying the groundwork for subsequent transformation and upgrading imperatives.

2020–2022: Phase of External Perturbation and Structural Adjustment. Severely impacted by the global COVID-19 pandemic, the tourism economic index plummeted to a nadir of 0.1265 in 2020, subsequently undergoing protracted recovery while remaining in a state of structural adjustment. During this period, Xinjiang’s tourism sector, analogous to other provincial jurisdictions, confronted severe operational constraints including inter-provincial travel restrictions and drastic reductions in international visitation, revealing heightened vulnerability to external shocks. Nevertheless, the pandemic simultaneously catalyzed internal structural adjustments, including accelerated digital transformation, development of local and peripheral tourism markets, and service quality enhancements, thereby accumulating adaptive capacity for post-pandemic recovery and demonstrating context-specific resilience and transformative potential.

2023–2024: Phase of Robust Recovery and Strategy-Driven Growth. Following the optimization of pandemic prevention policies and the comprehensive implementation of the “Tourism Revitalizes Xinjiang” strategy, the tourism economic index rapidly ascended to an unprecedented high of 0.2857. This rebound growth reflects not merely the release of suppressed market demand but also the tangible outcomes of proactive governmental promotion of tourism development. Through intensified marketing initiatives, optimization of business environments, and enhancement of tourism product quality, Xinjiang’s tourism sector demonstrated substantial endogenous resilience and expansive recovery prospects. This trajectory exemplifies the characteristics of “policy-driven development,” wherein the tourism economy achieves rapid yet high-quality recovery through the synergistic effects of improved external conditions and robust internal policy support.

3.2. Coupling Coordination and Relative Development Characteristics

Building upon the composite index calculations, we subsequently employed the Coupling Coordination Degree (CCD) and Relative Development Degree (RDD) models to quantify the evolutionary coordination patterns and intrinsic developmental dynamics between Xinjiang’s ecological environment and tourism economy systems over the period 2010–2024 (

Table 5. Coordinated development of ecological environment and tourism economy in Xinjiang. Source: Compiled by the authors.

Particular Year	Coupling Coordination Degree	Coupling Coordination Grade	Relative Development Degree	Relative Development Type
2010	0.3312	Mild discordance	0.1447	Tourism-lagging type
2011	0.3354	Mild discordance	0.1802	Tourism-lagging type
2012	0.3776	Mild discordance	0.2067	Tourism-lagging type
2013	0.3366	Mild discordance	0.2317	Tourism-lagging type
2014	0.3462	Mild discordance	0.2552	Tourism-lagging type
2015	0.3807	Mild discordance	0.2556	Tourism-lagging type
2016	0.4172	Borderline imbalance	0.2360	Tourism-lagging type
2017	0.4325	Borderline imbalance	0.2932	Tourism-lagging type
2018	0.4570	Borderline imbalance	0.5025	Tourism-lagging type
2019	0.4658	Borderline imbalance	0.6034	Tourism-lagging type
2020	0.4382	Borderline imbalance	0.4351	Tourism-lagging type
2021	0.4669	Borderline imbalance	0.6196	Tourism-lagging type
2022	0.4918	Borderline imbalance	0.5772	Tourism-lagging type
2023	0.5610	Marginal coordination	0.7823	Tourism-lagging type
2024	0.5700	Marginal coordination	0.7734	Tourism-lagging type

) (Figure 2).

3.2.1. Temporal Evolution of Coupling Coordination Degree

Throughout the study period, the Coupling Coordination Degree (CCD) between Xinjiang’s ecological environment and tourism economy exhibited a fluctuating yet ascendant trajectory, increasing from 0.3312 in 2010 to 0.5700 in 2024—representing a cumulative growth of 72.1% (mean annual growth rate: 3.86%). This sustained enhancement in systemic synergy confirms the region’s progressive incorporation of ecological protection into core developmental considerations, translating the vision of ecological-tourism symbiosis into tangible practice.

Phase I (2010–2015): Integration Initiation amidst Mild Imbalance. The CCD increased moderately from 0.3312 to 0.3807, with the system remaining in a state of mild imbalance. During this interval, although Xinjiang’s economic development began emphasizing the tourism sector and initial ecological conservation achievements emerged, policy coordination and functional integration between the two systems remained nascent. The predominant pattern involved parallel yet independent development trajectories rather than the establishment of effective reciprocal enhancement mechanisms. This reflects the characteristic systemic adaptation phase of evolutionary trajectories, wherein the two subsystems progressively identified mutual constraints and functional requirements through preliminary interactions.

Phase II (2016–2022): Synergistic Acceleration at the Threshold of Coordination. The CCD ascended from 0.4172 to 0.4918, indicating improved yet insufficient synergistic interactions, with the system hovering at the critical juncture between imbalance and coordination. Throughout this period, as the “green development” paradigm gained traction, the interdependence between ecological preservation and tourism intensified. Ecological advantages gradually transformed into tourism attractiveness, while tourism development commenced reinvestment into ecological protection. Nevertheless, the rapid expansion of tourism periodically exerted pressure upon ecological systems, compounded by external perturbations (notably the pandemic), resulting in sluggish and unstable coordination advancement that precluded full transition from the imbalance domain. This manifests the growing pains of systemic transformation—the challenge of balancing multiple objectives while navigating external uncertainties during the transition from independent to coordinated development.

Phase III (2023–2024): Qualitative Leap into Marginal Coordination. The CCD surged rapidly to 0.5700, crossing the critical coordination threshold of 0.5 for the first time and entering the marginal coordination state. This substantial improvement stems from the deep integration of “high-quality development” and “comprehensive tourism” strategies within Xinjiang. During post-pandemic recovery, governmental priorities increasingly emphasized the greening and quality enhancement of tourism through upgraded eco-tourism products, strengthened environmental regulation, and industrial convergence between tourism and agriculture/animal husbandry, thereby achieving higher-order interactions between ecological protection and tourism development. This demonstrates that with clear policy orientation and sustained practical exploration, systemic synergistic effects can achieve accelerated enhancement.

Regarding the evolution of coordination grades, the system transitioned from mild imbalance to marginal coordination during the study period, indicating that Xinjiang has attained preliminary achievements in promoting sustainable ecological-tourism development, while substantial potential for further advancement remains.

3.2.2. Temporal Evolution of Relative Development Degree and the “Tourism-Lag” Bottleneck

Analysis of the Relative Development Degree (RDD) between Xinjiang’s ecological and tourism subsystems reveals persistent structural developmental asymmetry throughout the study period. The RDD remained consistently below unity, exhibiting a sustained upward trajectory, with all observational years classified as tourism-lag type. This indicates that despite continuous tourism economic expansion, its developmental velocity and absolute magnitude failed to synchronize with ecological environment advancement, with the ecological subsystem maintaining a perpetually relative position.

2010–2015: Severe Tourism Lag. The RDD ascended from 0.1447 to 0.2556, indicating substantial underdevelopment of the tourism sector during this phase. This pattern may be attributed to Xinjiang’s abundant natural and cultural tourism resources failing to translate fully into economic advantages due to peripheral geographical location, deficient infrastructure, limited marketization, inadequate promotional efforts, and restricted tourist accessibility. The precedence of ecological conservation policies during this period elevated the ecological index relative to tourism economic performance, thereby amplifying the inter-systemic disparity.

2016–2019: Accelerated Catch-up amid Persistent Lag. The RDD accelerated markedly from 0.2360 to 0.6034. Throughout this interval, the tourism economy accumulated developmental momentum with significantly enhanced growth rates, reflecting the robust catalytic effects of the Belt and Road Initiative and tourism-targeted poverty alleviation policies. Nevertheless, despite accelerated tourism expansion, substantial absolute developmental gaps relative to the ecological environment persisted, with growth velocity remaining the primary constraint upon systemic coordination enhancement. This suggests that growth rate improvements alone cannot fully resolve structural imbalances; attention must concurrently be directed toward growth quality and sustainability.

2020: Widening Divergence under Pandemic Perturbation. The RDD retrenched to 0.4351, highlighting the direct and severe impact of COVID-19 upon the tourism sector, exacerbating relative divergence between tourism and ecological development. During the pandemic, restricted human mobility precipitated near-cessation of tourism activities, whereas reduced anthropogenic disturbance potentially facilitated ecosystem recovery, further amplifying relative disparities and confirming the sector’s pronounced vulnerability.

2023–2024: The tourism lag phenomenon has been alleviated to some extent, although structural constraints continue to persist. The relative development degree exceeded 0.77, indicating substantial progress in tourism development and a narrowing gap between the tourism economy and ecological environmental performance. This improvement can largely be attributed to the strong recovery of the tourism sector in the post pandemic period and the continued implementation of the “Tourism Revitalizes Xinjiang” strategy. Nevertheless, the fundamental pattern of tourism lag has not been fundamentally reversed, and considerable disparities between tourism economic development and ecological environmental conditions remain evident. Structural bottlenecks, including product homogenization, deficiencies in service quality, and limitations in transportation accessibility, continue to constrain Xinjiang’s transition toward a higher level of coupling coordination. Accordingly, future development efforts should prioritize the optimization of tourism industrial structures, the enhancement of human capital, the strengthening of regional tourism branding, and the expansion of market depth in order to promote more sustainable and coordinated development in Xinjiang.

3.3. Analysis of Factors Influencing Coupling Coordination

Employing Grey Relational Analysis (GRA), this study investigates the critical determinants governing the coupling coordination between Xinjiang’s ecological environment and tourism economy, quantifying the relative influence magnitudes of 32 indicators across six dimensions (

Table 6. Influencing factors and correlation results of coupling coordination between ecological environment and tourism economy in Xinjiang.

Driver Determinant	Indicator Metric	Dimension-Wise Mean Relational Grade	Grey Relational Grade	Ranking Hierarchy
Economic and industrial foundations	Xinjiang’s regional GDP (108 RMB)	0.7160	0.8675	2
	Number of star-rated hotels (number)		0.5325	30
	Number of travel agencies (number)		0.7480	9
Tourism resource endowment and attractiveness	Domestic tourism receipts (108 RMB)	0.7855	0.7968	8
	Domestic tourist arrivals (104 persons)		0.8586	4
	Number of A-level and above scenic spots (number)		0.8489	6
	International tourist arrivals (persons)		0.7254	14
	International tourism receipts (104 USD)		0.6980	18
Environmental pressure and Quality conditions	Industrial wastewater discharge (104 t)	0.6502	0.7466	10
	Industrial SO2 emissions (104 t)		0.7341	11
	Industrial particulate matter emissions (104 t)		0.6760	19
	Municipal wastewater discharge (104 t)		0.5603	28
	Industrial solid waste generation (104 t)		0.6221	24
	Forest pest and disease affected area (104 hm2)		0.6660	20
	Forest fire affected area (hm2)		0.6400	23
	Direct economic loss from natural disasters (108 RMB)		0.5562	29
Ecological governance investment	Urban sewage treatment investment (104 RMB)	0.6183	0.6580	21
	Greening and ecological protection investment (104 RMB)		0.6536	22
	Total environmental governance investment (108 RMB)		0.5863	25
	Annual afforestation area (104 hm2)		0.5833	26
	Forest pest and disease control rate (%)		0.7099	16
	Environmental governance investment as share of regional GDP (%)		0.5187	31
Infrastructure and reception capacity	Road network density (km/104 km2)	0.8287	0.8612	3
	Railway network density (km/104 km2)		0.8560	5
	Urban per capita green space (m2)		0.9062	1
	Green coverage ratio in built-up areas (%)		0.7970	7
	Harmless disposal rate of municipal solid waste (%)		0.7233	15
Ecological resource endowment	Per capita water resources (m3 capita−1)	0.6357	0.5754	27
	Air quality excellence rate in major cities (%)		0.7254	13
	Forest Coverage (%)		0.7262	12
	National nature reserves (number)		0.7064	17
	Nature reserve area (104 hm2)		0.4449	32

).The mean grey relational grade of 0.7057 substantially exceeds the conventional threshold of 0.5 for strong correlation, indicating robust and pervasive associations between the selected driving factors and coordinated developmental dynamics.

3.3.1. Comprehensive Dimensional-Level Influence Effects

At the aggregate dimensional level, the mean grey relational grades exhibit the following hierarchical ordering: infrastructure and hospitality capacity, tourism resource endowment and attractiveness, economic and industrial foundations, environmental pressure and quality status, and ecological resource endowment coupled with governance investment.

Infrastructure and hospitality capacity emerge as the predominant driving force, occupying the foremost position with the highest relational grade. This finding exhibits strong logical consistency within Xinjiang’s distinctive geographical and developmental context. The region’s vast territorial expanse and low population density render tourism development critically constrained by accessibility and amenity quality. Comprehensive transportation networks, high-quality accommodation and catering facilities, convenient telecommunications infrastructure, and robust tourism service systems constitute fundamental prerequisites for attracting long-haul visitors, enhancing tourism experiences, and extending industrial value chains. Without adequate infrastructural foundations, even premier tourism resources fail to translate into tangible economic benefits. This aligns with accessibility theory and carrying capacity theory in tourism economics, underscoring infrastructure as a core determinant of destination competitiveness.

Tourism resource endowment and attractiveness follow closely, indicating that innate resource advantages constitute the core appeal for sectoral development. Xinjiang possesses distinctive natural landscapes (e.g., the Tianshan Mountains, Kanas Lake) and multicultural heritage resources representative of Silk Road civilizations, whose scarcity and uniqueness form the fundamental basis for tourism economic development. However, the subordinate ranking relative to infrastructure corroborates the proposition that superior resources require robust infrastructural support for effective value realization. This finding resonates with applications of resource-based theory in tourism studies, emphasizing the value-creation potential of unique resources.

Economic and industrial foundations constitute a crucial driving force in promoting the coupling and coordinated development between ecology and tourism, primarily through the provision of strong economic support and diversified industrial bases. Higher levels of regional GDP enhance governmental fiscal capacity, thereby enabling greater investment in environmental protection initiatives and tourism infrastructure construction. At the same time, well developed industrial structures generate employment opportunities and stimulate income growth, which in turn strengthens local tourism consumption capacity and supports the provision of diversified complementary services, including agro processing and handicraft production, that reinforce the broader tourism industry chain. These dynamics reflect the core propositions of regional economic support theory, which emphasizes that a strong regional economic foundation constitutes a necessary condition for achieving long term sustainable development.

The relational grade for environmental pressure and quality status demonstrates that maintaining superior environmental quality while effectively managing anthropogenic pressures proves critical for coupling coordination. High environmental quality constitutes the foundation for attracting eco-tourism and high-end tourism segments, whereas excessive environmental pressure rapidly erodes tourism resource attractiveness, precipitating ecological-tourism system dysfunction. This directly embodies the core tenet of sustainable development theory—economic advancement must not compromise environmental integrity.

Ecological resource endowment, while constituting an important component of the ecological system, exhibits a relational grade marginally below that of “environmental pressure and quality.” This suggests that although Xinjiang possesses superior innate ecological capital, the effective management and protection of these endowments to prevent degradation and foster benign interactions with tourism development may prove more determinative than their mere existence. In other words, the importance of “governance” and “stewardship” potentially supersedes that of pure “possession.”

Ecological governance investment ranks as a relatively secondary driving force, occupying the final position among the six dimensions. This does not imply insignificance; rather, it indicates that at the current developmental stage, investment alone exhibits limited direct catalytic effects upon coupling coordination. Potential explanatory mechanisms include: First, temporal lag effects. Ecological governance benefits typically manifest with pronounced long-term latency, potentially failing to register immediately in coupling coordination metrics. Second, efficiency considerations. The effectiveness and precision of investment may supersede absolute magnitude in importance; investments failing to address critical ecological issues with precision diminish in impact. Third, foundational and reactive characteristics. Ecological governance investment predominantly assumes remedial or maintenance functions regarding existing environmental challenges, rather than exerting direct promotional or steering effects upon tourism development comparable to infrastructure investments. Given existing ecological foundations, the marginal contribution to coordinated development may fall below factors exhibiting stronger “pull effects.” Fourth, synergistic requirements. Ecological governance investment requires integration with legal frameworks, technological innovation, and public participation to achieve maximal efficacy.

3.3.2. Indicator-Level Analysis of Critical Driving Factors

At the disaggregated indicator level, the three highest-ranking variables by grey relational grade are: per capita urban green space (0.9062), regional gross domestic product (0.8675), and road network density (0.8612). These findings substantiate the validity of the dimensional-level analysis presented above.

As the strongest individual driver, per capita urban green space captures not only urban ecological livability and aesthetic value but also constitutes a critical component of destination attractiveness. For visitors, urban green spaces provide essential venues for recreation, sightseeing, and wellness activities, thereby enhancing the holistic tourism experience. This aligns with destination quality theory, which emphasizes visitor sensitivity to environmental amenities. Concurrently, this indicator reflects Xinjiang’s commitment to embedding ecological civilization principles within urbanization processes—specifically, the integration of green infrastructure into urban development to reconcile ecological and economic benefits.

The pronounced relational grade of regional GDP reaffirms the foundational enabling role of economic infrastructure in coupling coordination. Elevated GDP signifies enhanced resource allocation capacity, investment capabilities (encompassing both tourism and environmental protection), employment generation potential, and resident consumption power, thereby furnishing the tangible material basis necessary for interactive development between ecological preservation and tourism expansion.

Road network density, as a key indicator of infrastructural endowment, demonstrates a strong degree of relational influence, thereby directly confirming the dominant role of infrastructure and hospitality capacity in shaping tourism ecology coupling coordination. Within the geographical context of Xinjiang, transportation accessibility functions as a decisive factor linking spatially dispersed tourism resources, reducing travel time and transportation costs, and expanding regional market accessibility. In the absence of efficient and accessible transportation networks, even high-quality tourism resources cannot be effectively utilized, thereby constraining the formation of effective coupling between ecological and tourism systems. This finding at the micro level further reinforces the broader macro level conclusion regarding the primacy of infrastructure development.

Taken together, the coupling coordination between Xinjiang’s ecological environment and tourism economy should be understood as a complex adaptive system shaped through the interaction of infrastructural provision, distinctive tourism resource attractiveness, regional economic foundations, and the maintenance of environmental quality. Strengthening infrastructure development and optimizing tourism economic development models therefore represent critical leverage points for overcoming the persistent tourism lag bottleneck and promoting higher order coordinated development. By contrast, ecological governance investment alone cannot fully achieve sustainable coordination unless accompanied by more refined management practices and stronger synergistic integration with multiple interacting factors.

4. Discussion

This study conducts an in-depth analysis of the dynamic evolution and driving factors of the Coupling Coordination Degree (CCD) between Xinjiang’s ecological environment and tourism economy. The findings reveal distinctive patterns of regional sustainable development. This section integrates the theoretical framework and empirical discoveries with comparative analysis against domestic and international studies, aiming to deepen the academic contributions of this research and provide more refined, evidence-based references for regional policy formulation through dialogue with existing knowledge systems.

4.1. System Resilience, External Shocks, and Digital Strategic Response

This study finds that although the CCD of Xinjiang’s ecological and tourism systems exhibit a nonlinear upward trend, it was significantly impacted during 2020–2022 by public health events, highlighting the system’s vulnerability when confronting major external shocks. This finding is highly consistent with numerous domestic and international studies [13,32], demonstrating the universal sensitivity of the tourism industry to uncertainties such as pandemics and economic recessions. Particularly for frontier regions like Xinjiang, where tourism relies excessively on physical cross-regional mobility, this vulnerability is further amplified—a phenomenon also documented in studies of other northwestern Chinese provinces such as Gansu and Qinghai [33].

This vulnerability is not unique to Xinjiang but represents a global predicament for the tourism industry in the face of “black swan” events, challenging traditional assumptions in tourism planning regarding linear growth and stable environments [34]. However, this study emphasizes that for Xinjiang—ecologically sensitive and sparsely populated—digital technology intervention offers distinctive solutions for addressing such vulnerability. Our analysis supports the importance of constructing “digital resilience,” proposing the integration of a digital intelligence framework encompassing emergency response platforms and virtual experiences such as “Cloud Tour Xinjiang.” This aligns closely with current smart tourism and resilient tourism theories [35]. Compared with developed regions, Xinjiang still has considerable room for improvement in digital tourism infrastructure construction and application, yet its latecomer advantage lies in the capacity to directly draw upon advanced models without duplicating construction efforts.

For instance, through big data analytics for tourist flow monitoring, artificial intelligence for pandemic impact prediction, and VR/AR technologies for immersive virtual experiences, digital technologies can effectively resolve the “human-land contradiction,” achieving visitor diversion, precise early warning, and optimal resource allocation, thereby promoting dynamic equilibrium between ecological protection and tourism development. This strategy not only provides prospective approaches for responding to diverse future external shocks but also addresses the United Nations Sustainable Development Goals (SDGs) regarding innovation and infrastructure development, holding significant reference value for underdeveloped or ecologically fragile regions analogous to Xinjiang.

4.2. Infrastructure-First Strategy and Refined Ecological Governance: The Dual Drivers of the “Fast Access, Slow Experience” Paradigm

The driving factor analysis in this study clearly identifies macro-level infrastructure and reception capacity as the most critical factors influencing the CCD of Xinjiang’s ecological and tourism systems, with their importance even surpassing baseline environmental conditions. This finding is highly consistent with numerous studies on tourism development in underdeveloped regions [1], which uniformly emphasize transportation accessibility as the primary prerequisite and bottleneck for tourism development. Xinjiang’s vast spatial extent and dispersed resource distribution render “road network density” and high-quality reception facilities crucial for connecting fragmented tourism resources and enhancing visitor experience and attractiveness. The “fast access, slow experience” paradigm proposed in this study represents an innovative strategy precisely responsive to Xinjiang’s geographical characteristics and overcoming geographical barriers. Drawing upon international research on transportation network optimization and deep integration with destination experiences [36], it aims to achieve rapid arrival through efficient transportation connectivity, followed by slow, in-depth experiences that realize the cultural and ecological value of tourism.

Notably, our findings diverge from conclusions in some developed-country studies where “environmental regulation constitutes the primary driving force” [37], reflecting that dominant contradictions in regional development vary across different developmental stages. For Xinjiang, prior to achieving high-level coordination, infrastructure construction remains a prerequisite for removing development bottlenecks.

Simultaneously, per capita urban green space area, as an important micro-level indicator, demonstrates that localized, refined ecological provision, particularly urban ecological environmental quality to constitute the foundation supporting macro-level systemic coordination. This aligns with “urban tourism ecosystem” theory [38], which emphasizes that the ecological environmental quality of cities as tourism transit points and destinations not only affects resident well-being but also directly relates to visitor experience and perception. By fostering the development of “ecological gateway cities” and implementing micro level ecological governance measures, including the expansion of small urban parks and the upgrading of solid waste treatment systems, Xinjiang can simultaneously improve residents’ quality of life and strengthen the attractiveness and carrying capacity of cities functioning as tourism distribution centers, transportation hubs, and destination nodes. This strategy highlights the importance of coordinated “point line surface” development, in which urban centers operate as strategic points, transportation networks serve as connecting lines, and regional ecological landscapes form the broader spatial surface. Through this integrated framework, phased and multi-level policy recommendations can be formulated for infrastructure development and ecological governance in economically underdeveloped regions. Moreover, such an approach provides a practical pathway for addressing the persistent urban rural dual structure and uneven development patterns that characterize western China, thereby facilitating the organic integration of macro level strategic planning and micro level governance practices.

4.3. The “Tourism-Lag” Window Period and Culture–Ecology Integration Pathways: The “Xinjiang Paradigm” for High-Quality Development

This study reveals the long-standing “tourism-lag” characteristic in Xinjiang, indicating that ecological carrying capacity has not yet been excessively consumed. Consequently, Xinjiang currently occupies a uniquely favorable “development window period”—a stage of latecomer advantage enabling high-quality, sustainable development. This signifies that Xinjiang can avoid the development pitfalls of pioneer regions by drawing upon their experiences, thereby achieving leapfrog development. This condition contrasts sharply with challenges faced by regions such as the Yangtze River Economic Belt, where excessive ecological environmental pressure necessitates “debt-repayment” governance [39].

To fully capitalize on this window period, this study proposes shifting the focus of tourism development from quantity-driven sightseeing models toward value-driven immersive culture–ecology integration pathways. Xinjiang’s distinctive “desert-oasis-glacier” geographical continuum provides unparalleled advantages for developing participatory ecological tourism, which not only conforms to the core concepts of “ecotourism theory” but also responds to international pursuits of responsible tourism and the experience economy [40].

Meanwhile, activating the latent value of Silk Road cultural heritage and constructing unified cultural supply chains can effectively mitigate traditional tourism seasonal fluctuations, enhancing the cultural connotation and added value of tourism products to form a “culture + ecology” composite attractiveness. Against the backdrop of global tourism markets increasingly pursuing uniqueness and in-depth experiences, Xinjiang’s “culture + ecology” strategy possesses formidable competitiveness. Through digital reconstruction of archaeological sites (e.g., VR experiences at Jiaohe Ancient City), refinement of ethnic song-and-dance performances (e.g., large-scale outdoor production Kunlun Covenant), and protection and revitalization of primitive villages (e.g., in-depth tours of Kashgar Ancient City), Xinjiang can construct a more resilient, sustainable, and higher-quality tourism ecosystem. This strategy not only provides a Xinjiang paradigm for realizing the value transformation of “lucid waters and lush mountains are invaluable assets,” but also offers replicable experience for underdeveloped regions along China’s “Belt and Road” in exploring development models combining cultural resources and ecological advantages.

4.4. Empirical Validation and Methodological Reflection

The findings of this study demonstrate substantial congruence with the evolutionary trajectories and phase-specific characteristics documented in ecological-tourism coupling coordination research across other northwestern Chinese provinces [1], indicating robust regional generalizability and replicability of the indicator framework and quantitative outcomes. Methodologically, this investigation transcends the unidimensional industrial pollution perspective prevalent in extant literature, expanding toward comprehensive natural environmental stewardship and explicitly incorporating accessibility metrics such as railway and highway network densities. These refinements address critical deficiencies inherent in conventional indicator systems, thereby enhancing both validity and reliability. This study underscores that within modernization processes, Xinjiang’s strategic positioning as a national ecological security barrier necessitates dynamic equilibrium between stringent ecological red-line adherence and profound culture–tourism integration—a imperative bearing significant theoretical and practical implications for regional high-quality development.

4.5. Limitations and Future Research Directions

Several limitations merit acknowledgment. First, high-resolution, high-frequency ecological remote sensing data were not fully incorporated due to acquisition constraints; future investigations could leverage remote sensing technologies and big data analytics to obtain finer-grained, real-time ecological indicators. Second, constrained by data availability at the prefectural level, this analysis was primarily conducted at the provincial scale, potentially obscuring intra-regional heterogeneity in ecological pressures and tourism development intensities across Xinjiang’s prefectural jurisdictions. Future efforts should promote sub-provincial data sharing to enable more refined spatial differentiation analyses, elucidating complex internal dynamics to inform precision policy interventions. Finally, this study focused predominantly on current-state assessment of coupling coordination and driver analysis, with limited examination of specific policy intervention effects, micro-level mechanisms of market agent behavior, and climate change impacts. Subsequent research could draw upon policy evaluation theory, employ quasi-experimental methods, and integrate qualitative approaches such as surveys and in-depth interviews to construct more comprehensive, multi-scalar, multi-level analytical frameworks for tourism ecosystem coupling coordination.

5. Conclusions

Through a quantitative assessment of the coupling coordination between the ecological environment and tourism economy in Xinjiang during the period from 2010 to 2024, this study reveals the distinctive interaction patterns and sustainable development trajectories characterizing the region’s tourism ecology relationship. The findings demonstrate that the ecological subsystem index exhibited an overall fluctuating upward trend, increasing from 0.2882 to 0.3694, thereby indicating significant progress in environmental governance effectiveness. At the same time, the tourism economic subsystem experienced rapid yet highly volatile growth, evolving through three major stages: steady expansion between 2010 and 2019, contraction during the COVID 19 pandemic from 2020 to 2022, and accelerated recovery during 2023 and 2024.

Regarding coordinated development, the Coupling Coordination Degree (CCD) displayed a sustained upward trajectory, rising from 0.3312 to 0.5700, representing a cumulative increase of 72.1 percent and an average annual growth rate of 3.86 percent. This trend reflects a qualitative transition from a state of mild imbalance to marginal coordination. Importantly, despite the rapid expansion of the tourism economy, the relative relationship between tourism development and ecological environmental conditions consistently maintained a tourism lag pattern. Nevertheless, with the tourism development scale reaching 0.7734 in 2024 and approaching the threshold of synchronous development, Xinjiang has entered a strategically favorable stage in which its comparatively strong ecological foundation remains capable of supporting further tourism expansion. Under such conditions, development priorities should shift from the pursuit of quantitative growth toward the promotion of high-quality coordinated development.

The driver analysis further identifies infrastructure and hospitality capacity as the dominant determinants shaping coupling coordination outcomes. In particular, per capita urban green space, regional GDP, and road network density emerge as the most influential indicators contributing to systemic synergy between tourism and ecological development. Overall, to ensure the long-term sustainability of Xinjiang’s distinctive tourism ecology nexus, future policy formulation should prioritize the advancement of digital and intelligent transformation to strengthen systemic resilience, the continuous optimization of transportation infrastructure to improve regional accessibility, and the deepening of refined urban ecological governance practices to consolidate the foundational conditions necessary for sustainable development.