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<p>The study focuses on exploring the impact mechanism of the Digital Economy (DE) level on Tourism Economic Growth (TEG), aiming to reveal how DE promotes TEG through technological innovation, information dissemination, and optimization of resource allocation. Meanwhile, the study also pays close attention to the differences in DE infrastructure and application capabilities across regions and their heterogeneous impacts on TEG. The paper selects China’s panel data from 198 cities (2011 to 2020). It uses PCA to construct an evaluation index system for the DE development level, and constructs a fixed effects model to accurately evaluate its influences on TEG. In addition, an ISSA is introduced to perfect the model. The results indicated that the RMSE and MAE of the ISSA tended to stabilize after 20 iterations, both converging to 0.09. The goodness of fit was as high as 0.95, the standard deviation was only 0.0012, and the average calculation time was only 25.3&#xa0;s. It had high efficiency and reliability in processing complex data. In empirical analysis, the DE development level and the TEG were positively correlated. For every unit increase in the former, the average growth rate of the latter increased by 0.35%. The coefficient of the mobile payment rate was as high as 0.30, indicating that its promotion effect on TEG was particularly prominent. In the analysis of regional heterogeneity, the central and western areas benefited more from the DE development, with coefficients of 0.40 and 0.50, significantly higher than other regions. This study enriches the theoretical research on the relationship between DE and TEG, promotes their deep integration and coordinated development, and has important practical significance.</p>

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Impact of digital economy development level on tourism economic growth based on fixed effects model

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  • 出版模式：

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  • 发表日期：

    2025年12月27日

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  • Yonghe Yang

摘要

The study focuses on exploring the impact mechanism of the Digital Economy (DE) level on Tourism Economic Growth (TEG), aiming to reveal how DE promotes TEG through technological innovation, information dissemination, and optimization of resource allocation. Meanwhile, the study also pays close attention to the differences in DE infrastructure and application capabilities across regions and their heterogeneous impacts on TEG. The paper selects China’s panel data from 198 cities (2011 to 2020). It uses PCA to construct an evaluation index system for the DE development level, and constructs a fixed effects model to accurately evaluate its influences on TEG. In addition, an ISSA is introduced to perfect the model. The results indicated that the RMSE and MAE of the ISSA tended to stabilize after 20 iterations, both converging to 0.09. The goodness of fit was as high as 0.95, the standard deviation was only 0.0012, and the average calculation time was only 25.3 s. It had high efficiency and reliability in processing complex data. In empirical analysis, the DE development level and the TEG were positively correlated. For every unit increase in the former, the average growth rate of the latter increased by 0.35%. The coefficient of the mobile payment rate was as high as 0.30, indicating that its promotion effect on TEG was particularly prominent. In the analysis of regional heterogeneity, the central and western areas benefited more from the DE development, with coefficients of 0.40 and 0.50, significantly higher than other regions. This study enriches the theoretical research on the relationship between DE and TEG, promotes their deep integration and coordinated development, and has important practical significance.