旅游地生命周期理论拓展及其可预测性实证研究

旅游地演化是旅游地理学的经典命题之一。Butler提出的旅游地生命周期（TALC）理论作为构筑旅游地演化理论体系的基石，颇具全球影响力与权威性，却未能充分解释旅游地兴衰变化的内在机理，且难以准确预判旅游地何时会进入下一个阶段。鉴于此，本文遵循理论研究和实证研究相结合的研究范式，依据产品生命周期理论和创新扩散理论，以美国大峡谷国家公园（1919—2023年）为主案例，采用多元回归分析方法，对经典TALC理论进行拓展和实证检验。结果表明：（1）以累积旅游者数量、年旅游者数量、年旅游增长率共同表征的三重曲线框架可以更加直观地呈现TALC演化过程，三重曲线间的数理逻辑也可为判定TALC阶段分界的起步点、关键点、转折点和饱和点提供量化依据。（2）以累积旅游者数量作为测度指标的TALC“S型”曲线变化的直观动因是年旅游者数量和年旅游增长率的周期性波动。TALC演化的心理动因是各阶段分别吸引了早期探险者、早期小众旅游者、早期大众旅游者、晚期大众旅游者和滞后旅游者等不同心理类型的旅游者群体。TALC演化的现实动因是旅游地（供给侧）因素和旅游者（需求侧）因素的交互作用以及外部因素（环境）的复合作用。（3）综合考虑旅游地演化内外部因素和承载力动态性的TALC动态预测模型，具有良好的预测精度和稳健性。本文丰富了TALC演化机理的学理阐释，为TALC阶段划分和趋势预测提供新的思路和数理方法，同时为新时代旅游目的地高质量发展提供参考。

Within the field of tourism geography, the evolution of tourist destinations is one of the conventional topics. The tourism area life cycle (TALC) model, introduced by Butler as a cornerstone theoretical framework for understanding the evolution of destinations, has gained significant international influence and authority. Despite its extensive application, the TALC model remains limited in its capacity to elucidate the underlying mechanisms driving the rise and decline of tourist destinations, nor does it provide a basis for predicting the next stage of the destination's life cycle. Consequently, this study addresses this gap by integrating theoretical and empirical paradigms. Drawing upon product life cycle theory and innovation diffusion theory, and utilizing the U.S. Grand Canyon National Park (1919-2023) as a primary case study, this research employs multiple regression analysis to extend and empirically validate the classic TALC theory. The principal findings are as follows: (1) A TALC framework characterized by a triple-curve structure, plotting cumulative tourist numbers, annual tourist arrivals, and annual tourism growth rate, provides a more intuitive representation of the evolutionary process. The mathematical relationships between these three curves establish a quantitative basis for demarcating stage boundaries (e. g., inception, key development points, turning points, and saturation points) within the TALC. (2) The proximate driver of change in the TALC's characteristic “S-shaped” curve (measured by cumulative tourists) is the cyclical fluctuation observed in annual tourist arrivals and annual growth rates. The psychological motivations behind the evolution of TALC are that each stage attracts different types of tourist groups, including early explorers, early minority tourists, early majority tourists, late majority tourists, and lagging tourists. The fundamental driver of TALC evolution lies in the dynamic interaction between destination-specific (supply-side) factors and tourist-related (demand-side) factors, compounded by the influence of external environmental factors. (3) The proposed TALC dynamic prediction model, which comprehensively incorporates internal and external drivers of destination evolution alongside the dynamic nature of carrying capacity, demonstrates predictive accuracy and robustness. The contribution of this study lies in enriching the theoretical understanding of TALC's evolutionary mechanisms. It offers novel conceptual approaches and quantitative methodologies for delineating TALC stages and forecasting their developmental trajectories, thereby providing actionable insights for fostering the high-quality development of tourism destinations in the new era.