With the accelerated growth of global tourism and the increasing availability of transport, the coordination and interaction between the tourism economy and transport have become a topic of great interest. This study seeks to analyse the coordination effects and influencing factors of Shaanxi’s tourism economy and transportation, thereby contributing to the sustainable development of the region’s tourism economy. To achieve this, we develop a tourism economy–transportation evaluation index system and employ the system coupled with the coordination model and entropy method to conduct a thorough analysis. The research spans over the years 2003–2021. According to the findings of this study, the integration and coordination of tourism economy–transportation and tourism development in Shaanxi are exhibiting an upward development trend. Over the years, the degree of coupling coordination has progressed through the stages of uncoordinated development, transformational development, and coordinated development, with the recent epidemic upheaval resulting in a primary level of coordination. In addition, the transport system has a significant impact on the coupled and coordinated development of the tourism economy–, with the size of the transport base and transport efficiency serving as the driving factors and transport pressure and epidemic shocks serving as significant influencing factors. Due to the fluctuations of the global pandemic, the coordination of the tourism system declines sharply in 2020, but recovers in 2021 as the pandemic progressively stabilises. Through this study, we can gain a better understanding of the relationship between the tourism economy and transport in Shaanxi and devise strategies and policies to support the development of sustainable transport and tourism systems.

The tourist industry is consistent with the new development paradigm and plays a crucial role in regional economic growth. At the same time, several areas of China have developed tourism as a vital industrial pillar. Analysing and examining the elements that affect the tourist industry in this setting has significant theoretical and practical implications. The dependent variable for this study is the tourist income for Guizhou Province from 2006 to 2019. A total of Eight variables are chosen as independent variables, including the distance travelled by rail and by road, the number of civil flights, the number of travel agencies, the overall number of tourists, the disposable incomes of both urban and rural residents, the number of tertiary industry workers, and the volume of foreign direct investment. The eight independent variables are discovered to favourably impact tourist revenue through the use of factor and regression analysis. Based on the study’s findings, this article suggests that (1) crisis management should be strengthened, and tourist businesses should be advised to become more active rather than passive, (2) infrastructure development should be enhanced for supporting tourism and directing the growth of the rural tourist industry in the area, and (3) the use of digital technologies should be enhanced and the speed of building intelligent tourism should be increased.

Tourism is a key driver of regional economic development and a foundational sector for national economic growth. Understanding the factors influencing the tourism industry is crucial for policy and investment decisions. This study examines the determinants of tourism revenue in Hunan Province, China, from 2003 to 2019. Using factor and regression analysis, the study investigates the impact of nine independent variables: rail and road travel distances, civil air passenger traffic, total number of tourists, art performance groups, GDP, urban and rural disposable income, and food and beverage consumption. All nine factors positively correlate with Hunan’s tourism revenue, indicating that economic and infrastructural variables significantly influence tourism industry growth. Strengthening tourism infrastructure, enhancing investment channels, and supporting supply-side reforms are essential for sustained growth. Regional collaboration and innovation should be prioritized to drive long-term industry development. Policymakers should focus on expanding tourism consumption capacity, improving connectivity, and fostering an innovation-driven tourism economy to enhance regional and national economic benefits.

A stressful external environment may cause various mental diseases. It is become a hot subject to figure out how to handle the tense link between the environment and health. The mechanism of human-environment interaction may be studied, and a conceptual model of the connection between rural human settlements and health can be developed by referencing relevant ideas and approaches in neuroscience. For rural human settlement health research, a framework is given based on this model, which begins with a conceptual model. The theoretical support system, the substance of research, and the techniques used to convey it are sorted out in a normative way. It has the potential to fill in the gaps in rural-related research and provide a new research approach and theoretical framework for the study of the link between a rural environment's impact on health.

The aim of this study is to construct a value evaluation system for leisure sports and recreational resources based on tourists' psychological perspectives. Using the Analytic Hierarchy Process (AHP), the study analyzed the weights of four criteria factors, namely diversity of recreational resources, service quality, pricing strategy, and environmental quality, with a focus on tourists' intention to revisit. Among these factors, environmental quality has the highest weight, accounting for 46.811%. At the secondary indicator level, among the diversity of recreational resources, environmental landscape diversity has the highest weight, reaching 45.793%; among service quality factors, the weight of service quality level is the highest, accounting for 44.343%; among pricing strategy factors, the weight of pricing rationality is the highest, accounting for 54.995%. In the scheme level, the study selected three schemes, namely Xi'an Olympic Sports Center, Xi'an Sports Stadium, and Xi'an Urban Sports Park. After the single sorting of indicators and the overall sorting of scheme level, Xi'an Olympic Sports Center obtained the highest score of 4.517 points, indicating that it is the leisure sports and recreational resource with the highest intention for tourists to revisit. The study's CR value was verified by a one-time test, demonstrating its reliability. The AHP hierarchy analysis method used in this study is a commonly used multi-index decision-making method that can accurately evaluate the weight of different factors and assess leisure sports and recreational resources. The quantified analysis of the scores of different factors and schemes provides a reference for decision-makers, helping to optimize the management and service of leisure sports and recreational resources, and improve tourist satisfaction and intention to revisit.

Guangxi’s rapid urbanization and rising environmental pressures have made balanced water–ecology governance a core regional policy concern. Using annual data for prefecture-level cities in Guangxi, China, from 2004 to 2022, this study constructs an ecological indicator system encompassing “water resources–pollutants–clean governance” and conducts a comprehensive evaluation with a coupling-coordination diagnosis. Data sources included the Guangxi Statistical Yearbook, the China Environmental Statistical Yearbook, and relevant editions of the Guangxi Statistical Bulletin of National Economic and Social Development. Methodologically, indicators were first standardized via minimum–maximum scaling. Using the driver–pressure–state–impact–response framework, indicators were selected. Objective weights were determined through the criteria importance through the intercriteria correlation method. The technique for order preference by similarity to the ideal solution method was used to compute composite scores for the three subsystems. We then established models for the coupling degree (C) and coupling-coordination degree (D). Across 2004–2022, the D ranged from 0.458 to 0.798, indicating predominantly low-to-moderate coordination with discernible phase fluctuations rather than extreme imbalance. In 2022, the headline metrics were measured at 0.958 for C and 0.798 for D, representing the upper end of the observed coordination level over the sample period. Overall, urban ecological governance in Guangxi remained only partially coordinated during 2004–2022, although with episodic improvements. Policy pathways should prioritize high-weight, high-dispersion dimensions to enhance subsystem matching and raise coordination.