Spatiotemporal Evolution and Multi-scale Obstacle Factor Analysis of Tourism Ecological Resilience in the Yellow River Basin

School of Management, Yantai College of Science and Technology, Yantai, Shandong
School of Business, Beijing Technology and Business University, Beijing
School of Management, Tianjin University of Technology, Tianjin

Abstract

Tourism ecological resilience serves as a critical metric for evaluating the sustainable development capacity of tourism destinations. Enhancing this resilience is essential for promoting the long-term sustainability of the tourism industry. Based on panel data from provinces in the Yellow River Basin from 2011 to 2021, this study utilizes the entropy-weighted TOPSIS method, Markov chains, and spatial autocorrelation models to analyze the spatio-temporal evolution characteristics of tourism ecological resilience in the region. Furthermore, an obstacle degree model is employed to identify the key factors hindering the improvement of tourism ecological resilience.

The results indicate that from 2011 to 2021, the overall tourism ecological resilience in the Yellow River Basin remained at a relatively low level, exhibiting a fluctuating downward trend. A hierarchical spatial structure has emerged, characterized by a gradual decrease in resilience from the downstream to the upstream reaches. There is a significant positive spatial correlation in the development levels of tourism ecological resilience across the various provinces, with the spatial agglomeration effect intensifying over time, primarily manifested through "High-High" and "Low-Low" clustering characteristics. Regarding obstacle factors, the primary constraints at the criterion level are restorative adaptation capacity and innovative evolutionary capacity. At the indicator level, the main obstacles include the concentration level of tourism factors, total water resources, tourism R&D funding, and the number of authorized tourism-related invention patents.

Keywords: Tourism Ecosystem; Tourism Ecological Resilience; Spatiotemporal Evolution; Obstacle Factors; Yellow River Basin.

1. Introduction

The Yellow River Basin serves as a vital ecological shield and a core region for economic development in China. As the tourism industry increasingly becomes a strategic pillar for regional growth, the ecological pressure exerted by tourism activities has become a critical concern. Tourism ecological resilience refers to the ability of a regional tourism-ecological system to absorb, recover from, and adapt to external disturbances while maintaining its essential functions and structures. Understanding the spatiotemporal evolution of this resilience and identifying the key factors hindering its improvement is essential for achieving high-quality development and ecological protection in the Yellow River Basin.

The term "resilience" can be traced back to mechanical engineering and was introduced into ecology by Holling in the 1970s to evaluate the self-defense, recovery, and evolutionary capabilities of ecosystems subjected to disturbances \cite{1}. Research has since expanded into dimensions such as wetlands \cite{2}, oasis villages \cite{3}, and urban ecosystems \cite{4}. In the field of tourism, scholars view ecological resilience as a key indicator for measuring sustainable development capacity \cite{5, 6}. While models like DPSIR (Driver-Pressure-State-Impact-Response) are commonly used, they often fail to capture the dynamic adaptation and continuous evolutionary capabilities of ecosystems facing external shocks. Grounded in evolutionary resilience theory, this study constructs an evaluation framework based on defense-resistance capacity, recovery-adaptation capacity, and innovation-evolution capacity.

2. Methodology and Data Sources

2.1 Theoretical Framework and Indicator System

Evolutionary resilience posits that ecosystems respond flexibly to external shocks through structural self-organization. This study defines tourism ecological resilience as a triple-capacity system:
1. Defensive Resistance Capacity: The ability to withstand and mitigate shocks using economic and industrial support.
2. Restorative Adaptation Capacity: The ability to adjust states to regain stability after damage, based on internal driving mechanisms and resource-environmental carrying capacity.
3. Innovative Evolutionary Capacity: The ability to drive the ecosystem toward a higher-level sustainable state through R&D, innovation, and structural optimization.

2.2 Research Methods

• Entropy-weighted TOPSIS Method: Used to determine objective weights and calculate comprehensive resilience scores for various regions, providing a ranking of ecological health and stability \cite{Ref_1}.
• Markov Chains: Employed to analyze the state transition probabilities of tourism ecological resilience over time, revealing internal trends and "path dependency" \cite{Ref_2}.
• Spatial Autocorrelation Models: Applied to identify spatial clustering patterns (Global and Local Moran's $I$), determining geographical concentration and evolution \cite{Ref_3}.
• Obstacle Degree Model: Utilized to identify and quantify the primary factors hindering system development at multiple scales \cite{Ref_4}.

2.3 Data Sources

Data were sourced from the China Statistical Yearbook, China Tourism Statistical Yearbook, China Environmental Statistical Yearbook, and various provincial statistical bulletins (2011–2021). Indicators include tourism revenue, R&D funding, wastewater discharge, and authorized patents. Linear interpolation was used for missing data points.

3. Spatiotemporal Evolution Analysis

3.1 Temporal Evolution Characteristics

The overall tourism ecological resilience in the Yellow River Basin remained at a relatively low level during the study period, exhibiting a fluctuating downward trend. While national strategies for ecological protection provided support, the system showed high sensitivity to external shocks, such as the global COVID-19 pandemic, which caused significant declines in resilience indices.

3.2 Spatial Distribution Patterns

Spatial analysis reveals a significant gradient characterized by a "downstream > upstream > midstream" pattern. The downstream region, being more economically developed with mature tourism infrastructure and proactive ecological governance, maintains the highest resilience. At the provincial level, Shandong and Henan have emerged as "high-resilience" types, while provinces like Shanxi and Gansu remain in lower categories.

3.3 Dynamic Evolution and Markov Transitions

Markov chain analysis indicates that low-resilience areas in the upstream region exhibit high stability, making improvement difficult. The midstream region shows significant fluctuations and bidirectional transitions. In contrast, the downstream region demonstrates high stability in its high-resilience areas and a strong potential for middle-resilience areas to transition upward.

3.4 Spatial Agglomeration Characteristics

The Global Moran's $I$ index confirms a significant positive spatial correlation, with the spatial agglomeration effect intensifying over time. Local Moran scatter plots indicate a prevailing "Low-Low" clustering trend across the basin. Shandong consistently exhibits "High-High" clustering, acting as a regional leader, while provinces like Inner Mongolia and Qinghai are often characterized by "Low-Low" clustering, indicating a need for collaborative improvement.

4. Obstacle Factor Analysis

4.1 Criterion Level Obstacles

The obstacle degree model identifies innovative evolutionary capacity as the primary constraint hindering resilience improvement, followed by restorative adaptation capacity. The obstacle degree for defense and resistance capacity is relatively low, suggesting the basin's tourism ecosystems possess a baseline robustness against immediate shocks but lack long-term transformative power.

4.2 Indicator Level Obstacles

At the specific indicator level, the leading constraints across the basin include:
- Concentration level of tourism factors
- Total water resources
- Tourism R&D funding
- Number of authorized tourism-related invention patents
- Investment in environmental pollution control

In the upstream reaches, resource constraints like total water availability are dominant, while in the midstream and downstream reaches, the lack of innovative R&D output and high-intensity tourism factor concentration serve as the main barriers.

5. Conclusion and Discussion

This study concludes that tourism ecological resilience in the Yellow River Basin is currently characterized by low-level stability and significant regional disparities. A hierarchical structure has formed where the downstream region leads in resilience, while the midstream and upstream regions face persistent ecological and innovative hurdles.

To enhance resilience, policy interventions should focus on:
1. Strengthening Innovation: Increasing R&D investment and patent authorizations to drive the "innovative evolutionary capacity."
2. Regional Collaboration: Leveraging the spillover effects of "High-High" clusters like Shandong to support neighboring "Low-Low" regions.
3. Targeted Governance: Addressing specific obstacles such as water resource management in the upstream and industrial optimization in the midstream.

Future research should aim to refine the indicator system and expand the analysis to the prefecture-level city scale as data availability improves, allowing for more granular management strategies.