China Tourism Academy

Current research extensively examines the relationship between geographical environment, spatio-temporal activities, and subjective well-being. However, how well-being accumulates across different temporal scales remains unclear. Based on activity diary data collected from intercity commuters in the city of Zhongshan in China, this study analyzes the accumulation process of subjective well-being from the activity episode level to the day level. The findings reveal that: (1) activity episode well-being (ESW) is influenced by both activity types and spatial contexts, while day-level well-being (DSW) is mainly affected by the type of activities conducted in the day; notably, the impact of travel on activity episode well-being is opposite to that on day-level well-being; (2) in general, the accumulation from ESW to DSW follows the average rule; and (3) considering different mobility patterns among residents, the accumulation from ESW to DSW for the fixed mobility group adheres to a “begin–end” rule, whereas for the flexible mobility group it follows a “peak (low)–end” rule. These findings establish a dialogue between geography and psychology in the field of residents’ well-being, contributing to a deeper understanding of the factors influencing and processes shaping different temporal scales of subjective well-being among urban residents. They offer valuable insights for more refined urban health planning and governance in the future.

Accompanied by the impulsive interpretation of investment-driven growth in the process of industrialization, the trajectory of the rising contribution rate of consumption to economic growth has received more and more attention. Based on the VAR model, the article selects the relevant data of a region from 2005 to 2024 to make an empirical research on the relationship between tourism flow and local economy, and analyzes the current situation of tourism industry and economic development in the region, and finally proposes the relevant strategies according to the empirical results. The empirical results show that there is a mutual positive influence between tourism flow and local economy, and the positive effect of tourism flow on local economic growth is more significant, and its contribution can be up to 45% in the 10th period, while the local economy's contribution to tourism flow is stable at about 15%~20%, compared with the more limited role of economic development on tourism flow. The causal relationship provides a theoretical basis and foundation for the development of regional tourism policy.

This study aims to develop an Inclusiveness Educational Hospitality Framework that supports sustainable tourism in Pulau Langkawi through a stakeholder-centred approach. Inclusive education plays a crucial role in ensuring equitable access to hospitality training for students with diverse needs, particularly those enrolled in Special Education Program Integration (PPKI). The proposed framework connects the Langkawi Tourism Academy (LTA), District Education Office (PPD), and The Westin Langkawi Resort & Spa, establishing a comprehensive structure for technical learning, on-the-job training (OJT), and industry-aligned competency development. A cross-sectional quantitative design was used to examine the influence of OJT, training effectiveness, and training content on work group inclusion. Data were collected through a validated questionnaire incorporating established measurement items and analysed using SmartPLS to assess the measurement and structural models. Findings demonstrate adequate reliability and convergent validity, providing evidence that inclusiveness-driven training contributes to students’ belongingness and workplace adaptability. This research offers practical implications for hospitality education providers by promoting structured collaboration between institutions and industry to enhance accessibility, skill readiness, and social inclusion. The framework supports national efforts to strengthen the hospitality talent pipeline while advancing Malaysia’s sustainable tourism and inclusive development agenda.

This study introduces AHDB-LDP (Adaptive Hash and Dynamic Budget for Efficient Local Differential Privacy), an optimized Local Differential Privacy (LDP) framework that significantly enhances LDP's performance by focusing on adaptive hashing and dynamic budget allocation. This research presents several key innovations: Firstly, it employs a multihash adaptive bucket partitioning method, which utilizes multiple hash functions to effectively reduce bucket conflicts in high-dimensional data, thereby significantly improving the accuracy of frequency estimation and optimizing load balancing. Secondly, we designed a nonlinear dynamic privacy budget allocation model capable of adaptively adjusting privacy parameters (such as ϵ and α) based on data frequency or context; this strategy achieves an optimal trade-off between privacy and data utility in heterogeneous data environments. Finally, AHDB-LDP incorporates a two-stage adaptive perturbation mechanism that dynamically adjusts perturbation probabilities according to data frequency, enhancing estimation robustness and effectively reducing overall error while strictly satisfying ϵ-LDP. These combined improvements enhance the algorithm's efficiency, accuracy, and robustness in dynamic, high-dimensional, and heterogeneous data scenarios, making it more suitable for privacy-preserving data collection in distributed systems. Experimental results confirm that AHDB-LDP consistently outperforms other state-of-the-art algorithms in terms of both frequency estimation quality and computational efficiency, providing an efficient and practical solution for robust privacy-preserving data analysis.

Over the past several decades, old industrialized regions worldwide have faced immense pressure to adapt to global economic shifts. Using one of China’s major industrial provinces, Hebei, as a representative case study, this study examines how the evolution of one of China’s old industrial provinces, Hebei’s industrial structure has influenced its economic growth from 1990 to 2023. Drawing on theories of structural transformation and endogenous growth, we argue that the reallocation of resources from lower-productivity sectors (e.g., agriculture) to higher-productivity sectors (manufacturing and services) can act as an engine of growth. We employ a shift-share analysis (SSA) to decompose Hebei’s economic growth into components attributable to national trends, industrial structure, and regional competitive performance. The results reveal a globally relevant pattern of stagnation: while Hebei’s growth largely benefited from nationwide economic expansion (national effect), its heavy industrial structure initially posed a drag on growth (negative structural effect) and its regional competitive advantage in steel and energy sectors has eroded over time (weakening competitive effect). Our regression analysis further shows that growth was overwhelmingly dependent on capital accumulation while the contribution of labor was statistically insignificant, pointing to a low-productivity trap common in such regions. By integrating these methods, this study provides a robust diagnostic framework for identifying the root causes of economic distress in legacy industrial regions both within and outside China. These findings underscore the importance of structural upgrading for sustainable growth and offer critical lessons for policymakers globally, highlighting the necessity of moving beyond extensive, capital-driven growth toward an intensive model focused on industrial diversification, innovation, and human capital to ensure the sustainable revitalization of post-industrial economies.