中国旅游业碳生产率的区域差异及格局演变
Regional differences and pattern evolution of carbon productivity in China's tourism industry
李华 1郑梦园 1熊力 1刘娟娟 1庞红丽1
作者信息
- 1. 上海海事大学经济管理学院,上海 201306
- 折叠
摘要
基于中国30个省份2008-2019年旅游业及相关行业的面板数据,借助旅游消费剥离系数、空间自相关模型以及空间滞后模型,分析中国旅游业碳生产率的时空差异及影响因素.研究表明:(1)2008-2019年,中国旅游业碳生产率总体呈增长趋势,但空间分布不均,大体呈东高西低的空间格局,并且有向中、西部扩张的趋势.(2)中国旅游业碳生产率存在较强空间正自相关性,且在局部空间上的集聚性强于异质性,"热点"区域(H-H类型)主要邻近东部沿海地区,"冷点"区域(L-L类型)则主要分布在中、西部地区.(3)根据回归结果,仅产业结构体现出了正向影响,而经济发展程度、科技创新以及市场规模均呈现了负面影响.最后,针对性地提出提升我国旅游业碳生产率的对策建议.
Abstract
Based on panel data on tourism and related industries for 30 Chinese provinces from 2008 to 2019,this paper uses tourism consumption stripping coefficients,spatial autocorrelation models and spatial lag models to analyze the differences in carbon productivity in China's tour-ism industry over time and space and the factors influencing them.The research shows:(l)From 2008 to 2019,the overall carbon productivity of China's tourism industry has shown an increas-ing trend.However,the spatial distribution is uneven,generally showing the spatial pattern of high in the east and low in the west,and there is a trend of expansion to the middle and west.(2)There is a strong positive spatial autocorrelation in the carbon productivity of China's tourism in-dustry,and the agglomeration in the local space is stronger than the heterogeneity.The"hot spot"areas(H-H type)are mainly adjacent to the eastern coastal areas,while the"cold spot"ar-eas(L-L type)are mainly located in the central and western regions.(3)The degree of economic development,technological innovation and market size have a significant negative effect on car-bon productivity in tourism,and government intervention has a non-significant negative effect and industry structure has a non-significant positive effect.Finally,we propose countermeasures to improve the carbon efficiency of China's tourism industry.
关键词
旅游业/碳生产率/区域差异/格局演变Key words
tourism/carbon productivity/regional differences/pattern evolution引用本文复制引用
基金项目
教育部人文社会科学研究规划基金(21YJA790029)
出版年
2024
NETL
NSTL
万方数据