全球旅游贸易时空格局及影响因素研究

doi:10.3969/j.issn.1004-9479.2023.04.2021893

摘要/Abstract

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

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

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.

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.

图/表 11

参考文献 37

1	罗明义,毛剑梅.旅游服务贸易:理论·政策·实务.昆明:云南大学出版社, 2007.
	LUO M, MAO J. Tourism Service Trade: Theory, Policy and Practice. Kunming: Yunnan University Press, 2007.
2	叶莉,陈修谦.基于旅游竞争力评价的中国与东盟国家旅游贸易互动分析.经济地理,2013, 33(12):177-181.
	YE L, CHEN X. Analysis on interaction of tourism trade between China and ASEAN Countries based on evaluation on tourism competitiveness. Economic Geography, 2013, 33(12):177-181.
3	田纪鹏.国内外旅游服务贸易逆差研究前沿与展望.旅游学刊,2019,34(1): 136-148.
	TIAN J. Frontier and prospect of research on tourism and travel-related services trade deficit both at home and abroad .Tourism Tribune, 2019, 34(1): 136-148.
4	王欠欠,夏杰长.全球价值链视角下中国旅游贸易测算分析.国际贸易,2019(2):46-54.
	WANG Q, XIA J. Analysis of China's tourism trade from the perspective of global value chain. Intertrade, 2019(2):46-54.
5	戴斌.旅游服务贸易统计规则厘清与算法修正.旅游学刊,2016,31(03): 13-15.
	DAI B. Clarification of statistical rules and algorithm revision of tourism service trade. Tourism Tribune, 2016, 31(03): 13-15.
6	殷杰,郑向敏,董斌彬."21世纪海上丝绸之路"沿线国家旅游贸易:潜力、效率及其影响因素.东南亚纵横,2015(11): 8-14.
	YIN J, ZHENG X, DONG B. The 21st Century Maritime Silk Road tourism trade: potential, efficiency and its influencing factors .Crossroads: Southeast Asian Studies. 2015(11): 8-14.
7	文艳,孙根年.中国入境旅游贸易效率及其影响因素研究——基于异质性随机前沿引力模型的估计.旅游学刊, 2021, 36(3): 29-43.
	WEN Y, SUN G. Trade efficiency of China's inbound tourism and influencing factors: Estimating trade efficiency using a heterogeneous stochastic frontier gravity model. Tourism Tribune, 2021, 36(3): 29-43.
8	SUN Y, LIN P. How far will we travel? A global distance pattern of international travel from both demand and supply perspectives. SAGE Publications, 2019, 25(8): 1200-1223.
9	CHI J. Dynamic impacts of income and the exchange rate on US tourism, 1960-2011 . Tourism Economics, 2015, 21(5): 1047-1060.
10	KUZEY C, KARAMAN A, AKMAN E. Elucidating the impact of visa regimes: A decision tree analysis . Tourism Management Perspectives, 2019, 29: 148-156.
11	JENSEN C, ZHANG J. Trade in tourism services: Explaining tourism trade and the impact of the general agreement on trade in services on the gains from trade . Journal of International Trade & Economic Development, 2013, 22(3): 398-429.
12	SANTANA G, LEDESMA-RODRÍGUEZ F, PÉREZ-RODRÍGUEZ J. International trade and tourism flows: An extension of the gravity model.Economic Modelling, 2016, 52: 1026-1033.
13	LEJÁRRAGA I, WALKENHORST P. Economic policy, tourism trade and productive diversification . International Economics, 2013, 135: 1-12.
14	PETIT S. International trade in services and inequalities: Empirical evaluation and role of tourism services . Tourism Economics, 2017, 23(5): 1069-1075.
15	HÜBLER M. How trade in ecotourism services can save nature: a policy scenario analysis. Development Southern Africa, 2019, 36(1): 127-143.
16	JULES S. A sustainability assessment of trade and liberalization in tourism services . Winnipeg: International Institute for Sustainable Development, 2005.
17	COLE S, MORGAN N E, COLE S, et al. Tourism and Inequality: Problems and Prospects . London: CABI, 2010.
18	WEBSTER C, IVANOV S. Geopolitical drivers of future tourist flows . Journal of Tourism Futures, 2014, 1(1): 59-69.
19	CHUNG M G, HERZBERGER A, FRANK K A, et al. International Tourism Dynamics in a Globalized World: A Social Network Analysis Approach . Journal of Travel Research, 2020, 59(3): 387-403.
20	赵明,郑喜珅. 跨境旅游资源国际合作开发探讨——以黑龙江中俄边境段为例段为例.世界地理研究, 2004(04): 86-93.
	ZHAO M, ZHENG X. Study on international cooperation in exploitation of the cross-border tourist resources—A case study of Heilongjiang valley between China and Russia. World Regional Studies, 2004(04): 86-93.
21	石张宇,徐虹,沈惊宏.中俄双边旅游与进出口贸易互动关系的实证研究.人文地理,2015, 30(2): 141-147.
	SHI Z, XU H, SHENG J. An empirical study on interaction relation of Sino-Russia bilateral tourism and import and export trade. Human Geography, 2015, 30(2):141-147.
22	濮蓉,陶卓民,李涛."一带一路"沿线主要国家旅游竞争力与旅游服务出口协调度时空演变. 世界地理研究,2021,30(5):948-956.
	PU R, TAO Z, LI T. The spatio-temporal evolution of degree of coordination between tourism competitiveness and tourism service exports of major countries along the"Belt and Road".World Regional Studies, 2021, 30(5):948-956.
23	殷杰,郑向敏,李实.合作态势与权力角色:"一带一路"沿线国家旅游合作网络解构.经济地理, 2019, 39(7): 216-224.
	YIN J, ZHENG X, LI S. Cooperation situation and power role: deconstruction of tourism cooperation network of countries along the belt and road initiative .Economic Geography, 2019, 39(7): 216-224.
24	KONADU-AGYEMANG K. Structural adjustment programmes and the international tourism trade in Ghana, 1983-1999: Some socio-spatial implications . Tourism Geographies, 2010, 3(2): 187-206.
25	VOGEL B, FIELD J. (Re)constructing borders through the governance of tourism and trade in Ladakh, India . Political Geography, 2020, 82: 1-10.
26	MATA T, LLANO C. Social networks and trade of services: modelling interregional flows with spatial and network autocorrelation effects . Journal of Geographical Systems, 2013, 15(3): 319-367.
27	陈彦光.基于Moran统计量的空间自相关理论发展和方法改进.地理研究,2009,28(6): 1449-1463.
	CHEN Y. Reconstructing the mathematical process of spatial autocorrelation based on Moran' s statistics. Geographical Research, 2009, 28(6): 1449-1463.
28	刘斌.高级货币经济学.北京:中国金融出版社,2008.
	LIU B. Advanced Monetary Economics. Beijing: China Finance Press, 2008.
29	王劲峰,徐成东.地理探测器:原理与展望.地理学报,2017,72(1):116-134.
	WANG J, XU C. Geodetector: Principle and prospective .Acta Geographica Sinica, 2017,72(1):116-134.
30	卢宾宾,葛咏,秦昆,等.地理加权回归分析技术综述.武汉大学学报(信息科学版), 2020, 45(9): 1356-1366.
	LU B, GE Y, QIN K, et al. A review on geographically weighted regression. Geomatics and Information Science of Wuhan, 2020, 45(9): 1356-1366.
31	查瑞波,伍世代,孙根年.城市型目的地入境旅游市场演化差异研究——基于中国香港和新加坡市内部结构与外部规模分时段辨析.地理科学,2018,38(10):1661-1669.
	CHA R, WU S, SUN G. Differences Comparison of Urban Scale Destination's Inbound Tourism Market Evolution: The Time Interval Study Based on Internal Structure and External Scale in Hong Kong, China and Singapore City. Scientia Geographica Sinica, 2018, 38(10):1661-1669.
32	LEIPER N. Tourism Management . Collingwood, VIC: TAFE Publication, 1995.
33	MITCHELL L. Research on the Geography of Tourism, Travel: A Handbook for Managers and Researchers, Ritchie and Goeld(eds) . NewYork: John Wiley&sons, 1987.
34	吴必虎.旅游系统:对旅游活动与旅游科学的一种解释.旅游学刊,1998(1):20-24.
	WU B. Tourism systems: An interpretation of tourism activities and the science of tourism. Tourism Tribune, 1998(1): 20-24.
35	CHANG S, LAI F. Intra-continental and intercontinental service trade in the travel and tourism industry . Tourism Economics, 2011, 17(5): 963-982.
36	LUO J, LAM C. Travel anxiety, risk attitude and travel intentions towards "Travel Bubble" destinations in Hong Kong: Effect of the fear of COVID-19. International Journal of Environmental Research and Public Health, 2020, 17(21): 7859.
37	胡志丁,刘云刚,骆华松,等."地缘政治格局重构与国际旅游新趋势"系列笔谈.旅游论坛, 2021,14(2):1-17.
	HU Z, LIU Y, LUO H, et al. Geopolitical restructuring and new trends in international tourism. Tourism Forum, 2021, 14(2): 1-17.

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