地理科学 ›› 2020, Vol. 40 ›› Issue (11): 1889-1898.doi: 10.13249/j.cnki.sgs.2020.11.014
收稿日期:
2019-01-26
修回日期:
2020-09-20
出版日期:
2020-11-10
发布日期:
2020-12-04
通讯作者:
王群勇
E-mail:yuhy344@nenu.edu.cn;QunyongWang@outlook.com
作者简介:
于洪雁(1984−),女,黑龙江巴彦人,副教授,博士,主要从事旅游地理和旅游经济研究。E-mail: 基金资助:
Yu Hongyan1(), Wang Qunyong1,2,*(
), Zhang Bo1, Liu Jisheng3
Received:
2019-01-26
Revised:
2020-09-20
Online:
2020-11-10
Published:
2020-12-04
Contact:
Wang Qunyong
E-mail:yuhy344@nenu.edu.cn;QunyongWang@outlook.com
Supported by:
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。
中图分类号:
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.
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.
Table 1
The coupling coordination degree of tourism supply and demand in China in 2004-2016"
年份 | 全国 | 东北部 | 东部 | 中部 | 西部 |
注:不含港澳台数据。 | |||||
2004 | 0.505 | 0.494 | 0.606 | 0.487 | 0.434 |
2005 | 0.518 | 0.499 | 0.617 | 0.502 | 0.449 |
2006 | 0.518 | 0.503 | 0.617 | 0.508 | 0.443 |
2007 | 0.516 | 0.505 | 0.613 | 0.503 | 0.443 |
2008 | 0.511 | 0.506 | 0.610 | 0.500 | 0.435 |
2009 | 0.521 | 0.510 | 0.618 | 0.515 | 0.445 |
2010 | 0.520 | 0.514 | 0.616 | 0.511 | 0.447 |
2011 | 0.515 | 0.502 | 0.610 | 0.507 | 0.444 |
2012 | 0.528 | 0.508 | 0.620 | 0.525 | 0.457 |
2013 | 0.527 | 0.508 | 0.619 | 0.528 | 0.454 |
2014 | 0.536 | 0.510 | 0.624 | 0.534 | 0.471 |
2015 | 0.538 | 0.496 | 0.622 | 0.540 | 0.476 |
2016 | 0.537 | 0.480 | 0.626 | 0.536 | 0.477 |
Table 2
Moran’s I and significance of coupling coordinated degree of supply and demand in China in 2004-2016"
年份 | Moran’sI | P值 | Z |
注:不含港澳台数据。 | |||
2004 | 0.159 | 0.081 | 1.740 |
2005 | 0.175 | 0.056 | 1.908 |
2006 | 0.170 | 0.060 | 1.850 |
2007 | 0.156 | 0.084 | 1.724 |
2008 | 0.230 | 0.013 | 2.469 |
2009 | 0.193 | 0.037 | 2.076 |
2010 | 0.177 | 0.050 | 1.939 |
2011 | 0.161 | 0.070 | 1.797 |
2012 | 0.175 | 0.050 | 1.924 |
2013 | 0.195 | 0.035 | 2.108 |
2014 | 0.169 | 0.060 | 1.875 |
2015 | 0.163 | 0.068 | 1.822 |
2016 | 0.199 | 0.032 | 2.140 |
Table 3
Results of panel data regression on influence factors of coupling coordinated degree between tourism supply and demand in 2004-2016"
影响因素 | 固定效应 | 动态面板 | 空间误差 模型 | 空间自回归 混合模型 |
注: L.d和L2.d表示因变量的1阶滞后和2阶滞后,W.d表示因变量的空间交互项,W.u表示误差的空间交互项,Sargan为过度识别约束检验,AR(2)为二阶自相关检验。小括号内的数值为标准差。***、**、*分别表示在1%、5%和10%水平上显著。表中空白项意味着所对应模型不涉及相关参数。数据未含港澳台。 | ||||
产业结构 | 0.006*** | 0.004*** | 0.007*** | 0.007*** |
(0.001) | (0.001) | (0.001) | (0.001) | |
资源禀赋 | 0.010*** | 0.003 | 0.012*** | 0.015*** |
(0.002) | (0.002) | (0.003) | (0.003) | |
制度变迁 | 0.005*** | 0.004*** | 0.003** | 0.003* |
(0.001) | (0.000) | (0.001) | (0.001) | |
文化因素 | 0.042 | 0.021 | 0.031 | 0.029 |
(0.061) | (0.034) | (0.059) | (0.055) | |
资本因素 | 0.013*** | 0.019*** | 0.009*** | 0.012*** |
(0.004) | (0.001) | (0.001) | (0.002) | |
L.d | ?0.102** | |||
(0.051) | ||||
L2.d | ?0.082* | |||
(0.044) | ||||
W.d | ?0.316*** | |||
(0.101) | ||||
W.u | 0.357*** | 0.579*** | ||
(0.067) | (0.076) | |||
常数项 | 0.422*** | 0.514*** | ||
(0.031) | (0.041) | |||
N | 403 | 310 | 403 | 403 |
Sargan | 22.4 (P=0.21) | |||
AR(2) | ?0.83 (P=0.41) | |||
R2 | 0.449 | 0.556 | 0.551 | 0.578 |
Table 4
Spatial effect decomposition of the influence factors on the coupling coordinated degree between tourism supply and demand"
变量 | 直接效应 | 间接效应 | 总效应 |
注:括号内的数值为标准差;***表示在1%水平上显著。 | |||
产业结构 | 0.006*** | 0.003*** | 0.009*** |
(0.001) | (0.001) | (0.002) | |
资源禀赋 | 0.007*** | 0.003*** | 0.010 |
(0.002) | (0.001) | (0.003) | |
制度变迁 | 0.003*** | 0.001*** | 0.004*** |
(0.001) | (0.001) | (0.002) | |
文化因素 | 0.042 | 0.018 | 0.060 |
(0.059) | (0.025) | (0.084) | |
资本因素 | 0.005*** | 0.002*** | 0.008*** |
(0.001) | (0.001) | (0.002) |
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