Liao Zhenjie, Wang Lang
School of Management, Guangzhou Huashang College, Guangzhou, China.
School of Economics and Management, Xianyang Normal University, Xianyang, China.
Sci Rep. 2024 May 10;14(1):10761. doi: 10.1038/s41598-024-61021-6.
This study employs modified data envelopment analysis (DEA) models and spatial autocorrelation methods to analyze the characteristics of red tourism transformation efficiency and categorize them into efficiency zones. By utilizing geographic detector models, the interactive driving mechanisms behind spatial differentiation are revealed, providing valuable insights for the high-quality transformation and development of China's red tourism economy. The application of modified DEA models facilitates the evaluation of red tourism resource transformation efficiency by decomposing comprehensive efficiency into single-factor efficiency for individual input and output variables. The results indicate that: (1) Expansion of tourism factors is crucial for achieving red tourism resource transformation in China, with low efficiency in resource endowment investment acting as the primary constraint. (2) Local spatial correlation between production efficiency and resource transformation efficiency demonstrates a decreasing trend from east to west, leading to the classification of China's red tourism resources into five types of efficiency zones. (3) Endogenous ability factors predominantly affect red tourism resource transformation efficiency, with interaction between internal and external factors driving spatial differentiation.
本研究采用改进的数据包络分析（DEA）模型和空间自相关方法，分析红色旅游转型效率特征并将其划分为效率区。利用地理探测器模型揭示空间分异背后的交互驱动机制，为中国红色旅游经济的高质量转型发展提供有价值的见解。改进的DEA模型的应用通过将综合效率分解为单个投入和产出变量的单因素效率，便于评估红色旅游资源转型效率。结果表明：（1）旅游要素扩张对中国实现红色旅游资源转型至关重要，资源禀赋投入效率低下是主要制约因素。（2）生产效率与资源转型效率的局部空间相关性呈现出从东向西递减的趋势，导致中国红色旅游资源分为五种效率区类型。（3）内生能力因素对红色旅游资源转型效率影响较大，内外部因素的相互作用驱动着空间分异。
