Research on the temporal and spatial patterns and influencing factors of global tourism trade

doi:10.3969/j.issn.1004-9479.2023.04.2021893

Abstract

Abstract:

The new crown pneumonia epidemic has caused a dramatic decline in the level of global tourism trade. It is particularly important to explore the characteristics, regularities and driving factors of the temporal and spatial evolution of tourism trade in order to achieve a rapid recovery of global tourism trade. The paper selects tourism trade data from 140 countries (regions) worldwide over the past 20 years, uses spatial analysis techniques to characterize the temporal and spatial patterns of global tourism trade, and examines the driving factors and spatial heterogeneity of global tourism trade exports through geographic detectors and geographic weighted regression models. The results reveal that: (1) The center of global tourism trade has gradually shifted to Asia, forming three major markets in North America, Europe and Asia. (2) The temporal evolution of global tourism trade shows the characteristics of turbulent growth, short-term fluctuations and strong sensitivity. (3) The intensity of spatial agglomeration in global tourism trade import and export patterns first increased and then decreased. (4) Tourism connection channels, including the level of air transportation and Internet usage rate, are the leading factors stimulating the exports of global tourism trade. (5) There is spatial heterogeneity in the impact of influencing factors on tourism trade exports.

Key words: tourism trade, pattern evolution, spatial autocorrelation, geographically weighted regression, geographical factor detection

摘要：

新冠肺炎疫情导致全球旅游贸易水平断崖式下跌，探索旅游贸易时空演化特征、规律及驱动因素，对实现全球旅游贸易复苏显得尤为重要。选取全球140个国家（地区）近20年的旅游贸易数据，采用空间分析技术刻画全球旅游贸易时空格局演化，运用地理探测器、地理加权回归模型对全球旅游贸易出口的驱动因素及其空间异质性进行分析。结果表明：（1）全球国家（地区）旅游贸易重心逐渐向亚洲移动，形成了北美、欧洲、亚洲三大旅游贸易市场；（2）全球旅游贸易演变时序呈现震荡增长、短期波动、敏感性强的特点；（3）全球旅游贸易进出口格局的空间集聚强度先增强再减弱；（4）包括航空运输水平、互联网使用率在内的旅游连接通道是刺激全球旅游贸易出口的主导因子，世界自然文化遗产、旅游价格水平对旅游贸易出口的驱动作用较弱；（5）影响因子对旅游贸易出口的影响效应存在空间异质性。

关键词: 旅游贸易, 格局演化, 空间自相关, 地理加权回归, 地理探测器

Hanni LIU, Zancai XIA, Zhangxin YIN, Huimin SHEN, Ting LIU, Hui ZHANG. Research on the temporal and spatial patterns and influencing factors of global tourism trade[J]. World Regional Studies, 2023, 32(4): 14-27.

刘涵妮, 夏赞才, 殷章馨, 申慧敏, 刘婷, 张慧. 全球旅游贸易时空格局及影响因素研究[J]. 世界地理研究, 2023, 32(4): 14-27.

Figures/Tables 11

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维度	影响因子	符号代码	指标及解释	单位
进口国	人口规模	POP	反映游客数量及旅游业从业人员供给水平	千万人
	人均GDP	PerGDP	表征国家经济实力	万美元
	区域贸易协定数量	RTA	生效中的区域贸易协定签订个数，表示对外开放程度	个
出口国	世界自然文化遗产	WCNH	衡量旅游产品丰度	个
	旅游价格	PRICE	按购买力平价（PPP）计算的汇率进行换算	%
	旅游基础服务配套	INFRA	旅游基础服务设施评分（低1~高7）	—
连接通道	签证开放程度	VISA	免签、落地签综合得分，反映旅行自由度	—
	航空运输便利性	AIRT	中大型机场数量，衡量交通便利程度	个
	互联网使用率	INTER	每百万人拥有互联网服务器数量，表示信息化水平	万个
外部环境	国家脆弱性	FRAG	根据国家间的军队冲突数以及国家主权等方面综合测算的得分（低0~高25）	—

维度	影响因子	符号代码	指标及解释	单位
进口国	人口规模	POP	反映游客数量及旅游业从业人员供给水平	千万人
	人均GDP	PerGDP	表征国家经济实力	万美元
	区域贸易协定数量	RTA	生效中的区域贸易协定签订个数，表示对外开放程度	个
出口国	世界自然文化遗产	WCNH	衡量旅游产品丰度	个
	旅游价格	PRICE	按购买力平价（PPP）计算的汇率进行换算	%
	旅游基础服务配套	INFRA	旅游基础服务设施评分（低1~高7）	—
连接通道	签证开放程度	VISA	免签、落地签综合得分，反映旅行自由度	—
	航空运输便利性	AIRT	中大型机场数量，衡量交通便利程度	个
	互联网使用率	INTER	每百万人拥有互联网服务器数量，表示信息化水平	万个
外部环境	国家脆弱性	FRAG	根据国家间的军队冲突数以及国家主权等方面综合测算的得分（低0~高25）	—

年份	进口		出口		净出口
年份	Moran’s I	P值	Moran’s I	P值	Moran’s I	P值
2000	0.139	0.005	0.108	0.007	－0.017	0.360
2001	0.139	0.005	0.117	0.006	－0.020	0.307
2002	0.143	0.005	0.121	0.006	－0.010	0.465
2003	0.140	0.007	0.127	0.006	－0.012	0.423
2004	0.149	0.005	0.133	0.007	－0.011	0.463
2005	0.158	0.005	0.128	0.008	－0.020	0.309
2006	0.165	0.004	0.140	0.007	－0.016	0.387
2007	0.167	0.004	0.136	0.011	－0.024	0.287
2008	0.172	0.004	0.132	0.009	－0.047	0.089
2009	0.168	0.004	0.124	0.010	－0.046	0.103
2010	0.190	0.005	0.124	0.008	－0.077	0.024
2011	0.182	0.006	0.123	0.007	－0.080	0.020
2012	0.179	0.004	0.137	0.006	－0.079	0.017
2013	0.142	0.007	0.129	0.006	－0.080	0.020
2014	0.087	0.013	0.128	0.006	－0.069	0.020
2015	0.078	0.014	0.113	0.006	－0.062	0.023
2016	0.082	0.011	0.126	0.005	－0.054	0.031
2017	0.090	0.010	0.131	0.004	－0.050	0.037
2018	0.085	0.012	0.137	0.005	－0.057	0.026
2019	0.093	0.012	0.140	0.004	－0.060	0.029

年份	进口		出口		净出口
年份	Moran’s I	P值	Moran’s I	P值	Moran’s I	P值
2000	0.139	0.005	0.108	0.007	－0.017	0.360
2001	0.139	0.005	0.117	0.006	－0.020	0.307
2002	0.143	0.005	0.121	0.006	－0.010	0.465
2003	0.140	0.007	0.127	0.006	－0.012	0.423
2004	0.149	0.005	0.133	0.007	－0.011	0.463
2005	0.158	0.005	0.128	0.008	－0.020	0.309
2006	0.165	0.004	0.140	0.007	－0.016	0.387
2007	0.167	0.004	0.136	0.011	－0.024	0.287
2008	0.172	0.004	0.132	0.009	－0.047	0.089
2009	0.168	0.004	0.124	0.010	－0.046	0.103
2010	0.190	0.005	0.124	0.008	－0.077	0.024
2011	0.182	0.006	0.123	0.007	－0.080	0.020
2012	0.179	0.004	0.137	0.006	－0.079	0.017
2013	0.142	0.007	0.129	0.006	－0.080	0.020
2014	0.087	0.013	0.128	0.006	－0.069	0.020
2015	0.078	0.014	0.113	0.006	－0.062	0.023
2016	0.082	0.011	0.126	0.005	－0.054	0.031
2017	0.090	0.010	0.131	0.004	－0.050	0.037
2018	0.085	0.012	0.137	0.005	－0.057	0.026
2019	0.093	0.012	0.140	0.004	－0.060	0.029

维度	因子	q值	P值	解释力排序
客源国	人口规模	0.851 5	<0.001	5
	人均收入	0.860 6	<0.001	4
	服务贸易协定数量	0.331 7	<0.001	10
目的国	世界自然文化遗产数量	0.483 2	<0.001	8
	旅游价格	0.362 7	<0.001	9
	旅游基础服务设施	0.931 6	<0.001	3
连接通道	签证开放程度	0.510 0	<0.001	7
	航空运输便利性	0.948 0	<0.001	1
	互联网使用率	0.941 8	<0.001	2
外部环境	国家脆弱性	0.723 4	<0.001	6