School of Business Administration, Nanjing University of Finance and Economics, Nanjing, 210046, China.
School of Community Resources and Development, Arizona State University, Phoenix, AZ, 85004, USA; Hainan University-Arizona State University Joint International Tourism College, Hainan University, Haikou, 570228, China.
J Environ Manage. 2022 Oct 15;320:115812. doi: 10.1016/j.jenvman.2022.115812. Epub 2022 Aug 7.
Constructed on the total-factor analysis framework, this paper develops a comprehensive evaluation system and adopts the Super-SBM model to both analyze and enunciate the characteristics of tourism eco-efficiency in China during 2000-2017. This paper also identifies the determinants associated with spatial differentiation of tourism eco-efficiency by employing a novel geographical technique, namely the Geographical Detector Model. The results indicate that the tourism eco-efficiency exhibits great potential for growth. Besides, pure technical efficiency drives the optimized development of eco-efficiency. Also, there is significant spatial variations in eco-efficiency across different provinces and regions in China. Urbanization contributes to tourism eco-efficiency remarkably, followed by openness, technical level, economic scale, industrial structure, capital effect, environmental regulation, and tourism growth. The relational interrelations of tourism eco-efficiency determinants are the bi-enhancement and the nonlinear-enhancement interactions. The implications of research findings are discussed and may be applied to a multitude of corporate environmental-economic management scenarios.
本文构建在全要素分析框架的基础上，开发了一个综合评价体系，并采用 Super-SBM 模型，对 2000-2017 年中国旅游生态效率的特征进行了分析和阐述。本文还通过一种新颖的地理技术——地理探测器模型，确定了与旅游生态效率空间分异相关的决定因素。结果表明，旅游生态效率具有巨大的增长潜力。此外，纯技术效率推动了生态效率的优化发展。同时，中国不同省份和地区的生态效率存在显著的空间差异。城市化对旅游生态效率有显著的促进作用，其次是开放度、技术水平、经济规模、产业结构、资本效应、环境规制和旅游增长。旅游生态效率决定因素的关系相互作用是双增强和非线性增强的相互作用。研究结果的意义进行了讨论，并可应用于多种企业环境经济管理情景。
