Xiaoli Lu

数字经济赋能旅游智慧化转型，成为中国旅游经济稳定可持续发展的新着力点。论文在厘清数字经济对旅游经济韧性的时空影响机理基础上，基于中国211个优秀旅游城市2011—2020年面板数据，采用中介效应模型、空间面板杜宾模型等方法检验了数字经济对旅游经济韧性的影响机制及空间溢出效应，研究发现：① 空间分布上，研究期内数字经济发展始终呈现出东高西低的分布态势，并在区域内呈“组团式”片状分布形态；旅游经济韧性在研究期内分布相对分散，东、中、西以及东北地区空间差距呈现收敛趋势；② 数字经济对旅游经济韧性产生显著的正向影响，数字经济发展水平每提高1个单位，旅游经济韧性提升0.2748，该结果通过了稳健性检验；③ 在数字经济对旅游经济韧性的影响机制方面，数字经济通过在抵抗力维度提高旅游经济增长水平、在恢复力维度提高技术载体和在重组能力维度提高人力资本从而促进旅游经济韧性的提升，即旅游经济增长、技术载体和人力资本是数字经济对旅游经济韧性产生显著正向影响的主要路径；④ 数字经济发展赋能了本地旅游经济韧性提升，但对相邻城市的旅游经济韧性产生“虹吸效应”。研究结果不仅丰富了数字经济作为旅游经济韧性的前置影响因素的研究，而且为数字经济赋能旅游经济高质量发展提供参考。

The manufacturing industry has created a rapid evolution of the economy, but it has also negatively impacted the ecosystem. A better understanding of the manufacturing industry in green growth is crucial to achieving the sustainability goals in China’s high-quality development stage and is better for identifying the impact of scale effect or technological effect in EKC. In this research, a super-efficiency slacks-based measure model is proposed to evaluate the green growth efficiency of 27 manufacturing industries, and a Luenberger index method is adopted to interpret the driving forces of efficiency. The results demonstrate that green growth efficiency in the manufacturing industry shows a fluctuating upward trend, and more than 60% of the industries are in a gray growth state. The growth of green growth efficiency mainly depends on the pulling effect of technological dividends brought by technological progress, rather than the improvement of technical efficiency. As the industry heterogeneity is analyzed, technology-intensive industries still dominate in the process of manufacturing industry and have shown a significant upward trend. Finally, some suggestions are proposed from the perspective of the government and enterprises.

Green growth, a new growth mode to tackle resource and environment crises, is imperative in light of current environmental crises and resource depletion. Most evaluation strategies of green growth, while emphasizing the time dimension, ignore the spatial association and diffusion. This study provides a measurement framework of green growth with which to select a set of 18 indicators and evaluates the efficiency of green growth in different regions. The coefficient of variation and the regional Gini coefficient were applied to analyze the spatial variation of green growth. The Exploratory Spatial Data Analysis (ESDA) was used to identify the evolution of the geographical agglomeration in 30 administrative regions in mainland China. The results show that China’s green growth capacity is constantly improving, and the gap between regions in this respect is shrinking. The spatial evolution trend of green growth is expanding horizontally from the East to the West and vertically from the central to the southwest and the northwest regions. Green growth in the eastern and central regions is active but poor in the northeast region. Compared with the continuous stability of the 22 provinces, eight provinces exhibited spatial activity and growth spillover, which affected the adjacent regions. Promoting the outflow of capital and technology is key to increasing green growth in the eastern and central region, while increasing investment and introducing technology through policy advantages to promote industrial transformation is an urgent task for the northeast region.