Changyao Song

1. The world is currently facing unprecedented changes, with multiple intertwined uncertainties pushing it into an era of volatility, uncertainty, complexity, and ambiguity. The Global Economic Pol...

低空旅游企业是低空旅游经济发展的核心。为揭示中国低空旅游企业空间格局的演化规律与驱动机制，论文运用空间探索性分析、回归分析和模糊集定性比较分析方法(fuzzy set qualitative comparative analysis，fsQCA)，系统探究中国低空旅游企业的时空特征及其影响因素。结果表明：(1) 根据低空旅游企业数量，可将低空旅游企业发展划分为萌芽探索、加速发展和爆发增长三个阶段，低空旅游企业空间格局呈现从单极引领向多极协同的演化特征；(2) 低空产业基础是影响企业区位选择的核心驱动力，体现出供给主导型的发展逻辑，其作用显著超越旅游市场需求因素；(3) 低空旅游产业发展存在综合驱动、技术补偿、资源依托和结构优化4条路径，不同地区通过差异化条件组合实现低空旅游企业集聚发展。中国低空旅游企业受产业基础主导影响，呈现出供给引领、多元路径并存的发展特征，未来应构建分区引导—基础支撑—产业协同的推动体系，形成以产业基础为核心、区域特色为支撑的低空旅游高质量发展新格局。

The relationship between tourism development and the urban–rural income gap has long been a focus of academic research; however, there is still a lack of consensus. This study introduces the innovative perspective of new structural economics to examine the causality between tourism comparative advantage and the urban–rural income gap using panel data from 31 provincial administrative units in China from 2000 to 2020. The findings indicate that the comparative advantage in the tourism industry helps narrow the urban–rural income gap, a conclusion that remains robust after the instrumental variable method and various robustness tests. This advantage operates through the effects of non–agricultural employment and rural infrastructure development. Both proactive government and an effective market play a positive moderating role in this process, and their synergistic effect is more pronounced. Furthermore, the comparative advantage in tourism in one region can benefit neighboring areas by reducing the urban–rural income gap through spatial spillover effects.

Tourism as a nontradable sector affects regional price indexes. However, the effect of tourism on urban housing prices has not been sufficiently studied. This study uses panel data for prefecture-level cities in China to empirically examine the effect of tourism development on urban housing prices. The findings indicate that tourism development can increase urban housing prices, and the effect mainly comes from domestic tourism. This conclusion is not only derived from the instrumental variable method considering endogenous problems but also verified by multiple robust methods including quantile regression. Meanwhile, the study innovatively identifies the mediating role of investment effect, income effect, and public service effect in the impact of tourism on urban housing prices. It further shows the heterogeneity of cities, with more significant effect of tourism on urban housing price in small- and medium-sized cities, noncentral cities, central and western cities, and nontourist cities. This study reveals the commonality and particularity of the impact of tourism development on urban housing price. It not only enriches the research on tourism economics from the perspectives of the relationship between tourism and real estate but also further enriches the relevant conclusions and theoretical mechanism of the impact of tourism on housing prices.

Uncertainties and risks have posed significant challenges to cross‐border mergers and acquisitions (M&As) in tourism. By using the cross‐border M&A data from 2000 to 2020, this study examines the relationship between the national risks of the host country and equity strategies for cross‐border M&As in tourism. The results show that the greater the host country's comprehensive risk and economic risk, the less inclined the tourism industry is to adopt high‐equity M&A strategies, and the fewer the shares and the lower the amount of cross‐border M&A transactions. Industry correlation negatively moderates the effect of national risks while institutional distance has positive moderating effect. The moderating role of cultural distance on the equity selection effect of national risks is limited.

Ice-and-snow tourism (IST) is a booming industry, and the development of its industrial clusters reflects its regional development quality. Taking 1985–2021 data for China's IST enterprises, this study used industrial cluster identification and industrial correlation analysis to explore the development of IST industrial clusters. The following results were obtained: (1) China's IST initially formed hotspot industrial clusters in Beijing-Tianjin-Hebei, the Northeast, the Yangtze River Delta, the Pearl River Delta, Chengdu-Chongqing, and Xinjiang regions. (2) Multiple industry forms failed to become deeply integrated into development, indicating a need to optimize the structure of the IST industrial chain. (3) The development environment of IST industrial clusters in each province showed differentiated characteristics. (4) IST industrial clustering was affected by both internal and external factors. External factors were grouped into climate and ice-and-snow resources, government policies and sports events, and economic fundamentals and market conditions. Internal factors included industrial association and industrial integration in the IST industrial cluster. Based on this study's identification of the characteristics of China's IST industrial clusters, countermeasures are proposed for their optimal development.