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中国旅游供需耦合协调发展的空间分异及驱动机制研究
引用本文:于洪雁,王群勇,张博,刘继生.中国旅游供需耦合协调发展的空间分异及驱动机制研究[J].地理科学,2020,40(11):1889-1898.
作者姓名:于洪雁  王群勇  张博  刘继生
作者单位:1.牡丹江师范学院经济与管理学院,黑龙江 牡丹江 157012
2.南开大学数量经济研究所&中国经济协同创新中心,天津 300071
3.东北师范大学地理科学学院,吉林 长春 130024
基金项目:国家自然科学基金项目(41471111)、黑龙江省哲学社会科学研究规划项目(19JYE256)、山东省社会科学规划项目(19BJCJ07)、牡丹江师范学院博士科研启动基金项目(MNUB201811)、牡丹江师范学院东北亚经济研究中心项目(1353PT006)资助
摘    要:基于耦合协调模型测算2004―2016年中国31个省(市、区)(不含港澳台)旅游供需耦合协调度,利用空间自相关法、面板数据模型、地理加权回归模型,从全局和局部空间视角分析旅游供需耦合协调发展的影响因素,并探讨其驱动机制。研究表明:① 旅游供需耦合协调发展的空间地带性差异显著;空间相关性愈发明显。② 固定效应、动态面板模型、空间误差(SEM)和空间自回归混合模型(SAC)估计结果表明,产业结构、资源禀赋、制度变迁、资本因素与旅游供需耦合协调发展呈正相关关系,直接效应和间接效应显著。③ 地理加权回归模型(GWR)估计结果表明各影响因素的空间分异作用明显,产业结构由东南和北部边缘地带向内陆递减;资源禀赋以西北部为核心逆时针扩散;制度变迁由东南沿海向西北内陆逐渐减少,“一高一低”两极对峙;资本因素沿东南沿海向西北内陆顺时针递减。④ 旅游供需耦合协调发展演化的驱动机制包括产业结构、资源禀赋、资本驱动力和制度变迁调控力。

关 键 词:旅游供需  耦合协调  地理加权回归模型  驱动机制  
收稿时间:2019-01-26
修稿时间:2020-09-20

Driving Mechanism and the Spatial Differentiation of Coupling Coordinated Development of Tourism Supply and Demand in China
Yu Hongyan,Wang Qunyong,Zhang Bo,Liu Jisheng.Driving Mechanism and the Spatial Differentiation of Coupling Coordinated Development of Tourism Supply and Demand in China[J].Scientia Geographica Sinica,2020,40(11):1889-1898.
Authors:Yu Hongyan  Wang Qunyong  Zhang Bo  Liu Jisheng
Institution:1. College of Economics and Management, Mudanjiang Normal University, Mudanjiang 157012, Heilongjiang, China
2. Institute of Statistics and Econometrics & Collaborative Innovation Center for China Economy, Nankai University, Tianjin 300071, China
3. School of Geography Science, Northeast Normal University, Changchun 130024, Jilin, China
Abstract:The relationship between tourism supply and demand is an important issue in tourism economic research, and it is also an urgent problem to be solved under the background of supply-side structural reform. Based on coupling coordination theory, this article calculates coupling coordinated degree of tourism supply and demand of 31 provinces in 2004-2016 years. Fixed Effect Model, Dynamic GMM Model, Spatial Error Model and Spatial Autoregressive Combined Model are used to grasp the influencing factors of coupling coordinated development of tourism supply and demand. Geographically weighted regression model is used to study the spatial differentiation of influencing factors in 2016 from a local perspective, in order to explore the driving mechanism of coupling coordinated development of tourism supply and demand in China. The research shows that: 1) The spatial zonality difference of coupling coordinated development of tourism supply and demand is significant, the spatial distribution pattern continues to evolve, and the spatial correlation is more and more obvious. 2) The fixed effect estimation of static panel shows that, institutional factors, resources endowment, industrial structure and capital factor have significant positive correlation, cultural factors had not yet passed the significant test. The results are very robust, and the direct and indirect effects of space are significant. 3) GWR model estimated that the spatial differentiation of the coupling coordinated development of tourism supply and demand is obvious, and industrial structure decreases from southeast and northern fringe to the inland, resource endowment is centered in the northwest and diffuses counterclockwise to the south, the central and the northeast, institutional changes gradually decreased from the southeast coast to the Northwest inland, and the two poles confronted each other, capital factor decreases clockwise along the southeast coast to the northwest inland and to the northeast. 4) The coordinated development of tourism supply and demand is constrained by the internal production unit and external environment orientation. The development and dynamics of tourism demand and supply system itself have evolved into internal primary driving forces. The external macro-environment-oriented driving forces include industrial structure, resource endowment, capital driving force and institutional change control force.
Keywords:tourism demand and supply  coupling coordination  geographically weighted regression  driving mechanism  
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