tourism mobility definition

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Research Article

The contribution of tourism mobility to tourism economic growth in China

Roles Conceptualization, Funding acquisition, Methodology, Resources

Affiliation School of Tourism, Hubei University, Wuhan, Hubei, China

Roles Software, Writing – original draft

Affiliation School of Urban and Regional Science, East China Normal University, Shanghai, China

Roles Data curation, Formal analysis, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Business, Hubei University, Wuhan, Hubei, China

Roles Conceptualization, Investigation, Methodology

Affiliation School of Tourism, Hainan University, Haikou, Hainan, China

Jun Liu,
Mengting Yue,
Fan Yu,

Published: October 27, 2022
https://doi.org/10.1371/journal.pone.0275605
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Mobility is the key factor in promoting tourism economic growth (TEG), and the transportation infrastructure has essential functions for maintaining an orderly flow of tourists. Based on the theory of fluid mechanics, we put forward the indicator of tourism mobility (TM). This study is the first to measure the level of TM in China and analyze the spatiotemporal evolution characteristics of TM. Applying the Exploratory Spatial Data Analysis method, we analyze the global and local spatial correlation characteristics of TM. Moreover, we further estimate the contribution of TM to TEG by econometric models and the LMDI method. The results show that (1) the TM in China has maintained rapid growth for a long time. However, there are differences in the rate of growth in different regions. The TM in each region only showed a significant positive spatial correlation in 2016–2018. The space-time pattern is constantly changing over time. The local spatial autocorrelation results of TM are stable, and various agglomeration states are stably distributed in some provinces. (2) The regression results of the traditional panel data model and spatial panel data model both show that TM has a significant positive effect on TEG. Moreover, TM has a negative spatial spillover effect on neighboring regions. (3) The result from the decomposition of LMDI shows that the overall contribution of TM to TEG is 15.76%. This shows that improving TM is a crucial way to promote the economic growth of tourism.

Citation: Liu J, Yue M, Yu F, Tong Y (2022) The contribution of tourism mobility to tourism economic growth in China. PLoS ONE 17(10): e0275605. https://doi.org/10.1371/journal.pone.0275605

Editor: Hironori Kato, The University of Tokyo, JAPAN

Received: March 3, 2022; Accepted: September 20, 2022; Published: October 27, 2022

Copyright: © 2022 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data on RAILWAY, HIGHWAY, ROAD1, ROAD2, ROAD, GDP, TERTIARY INDUSTRY, and POPULATION are from the Chinese Nation Bureau of Statistics ( https://data.stats.gov.cn/easyquery.htm?cn=C01 ). The data on TOURISM REVENUE and VISITORS are from the CEIC database ( https://insights.ceicdata.com ). The data on TRAFFIC, TOURISM MOBILITY, RECPTION, INDUSTRY, and STRUCTURE were calculated by the authors. Please see the paper for details.

Funding: This work was supported by grants from National Social Science Foundation of China [grant number 17CJY051].

Competing interests: The authors have declared that no competing interests exist.

Introduction

In recent years, the tourism industry has maintained rapid development. By 2019, the total number of global tourist trips exceeded 12.3 billion, an increase of 4.6% over the previous year. The total global tourism revenue was US$5.8 trillion, equivalent to 6.7% of global GDP (World Tourism Economy Trends Report [ 1 ]). Tourism has made important contributions to economic growth by increasing employment, improving infrastructure, and accumulating foreign exchange earnings for destinations [ 2 ]. Due to the impact of COVID-19, People’s travel is restricted. The total number of international tourists in 2021 decreased by 72% compared with 2019, and international tourism consumption dropped by nearly half compared with 2019 [ 3 ].

The above facts remind us that mobility has become an essential feature of tourism activities [ 4 , 5 ]. Tourists from origins to destinations result in a series of mobility of information, material, and capital. These mobilities have a great influence on tourist destinations [ 6 – 9 ]. If tourism mobility (TM) stagnates, tourist attractions, reception facilities and transportation facilities built for tourists will be idle. Tourism workers will lose their jobs and tourism economic growth (TEG) will also stagnate. Therefore, studying the impact of TM is necessary and important.

As one of the important tourist destinations in the world, China’s domestic tourism and inbound tourism are developing rapidly. In 2019, the total contribution of China’s tourism industry to GDP reached 10.94 trillion yuan, accounting for 11.05% of the total GDP, exceeding the proportion of international tourism in the global GDP. A total of 28.25 million people were directly employed in tourism, and 51.62 million people were indirectly employed in tourism. The total employment in tourism accounts for 10.31% of the total employed population in the country [ 10 ]. However, due to the impact of COVID-19, the development level of China’s tourism industry has not recovered to the level of 2019. In 2021, the total number of domestic tourists in China was 3.246 billion, which is only 54% of that in 2019, and directly leads to a total tourism revenue of 2.92 trillion yuan, which is only 51% of that in 2019. This shows that TM is more important to China’s tourism industry. Therefore, we decide to focus on the TM in this study and take China as the research sample.

The top priority of this study is to obtain the right measurement of TM. Transportation infrastructure is an important carrier for the exchange of factors in tourism. Existing studies have confirmed that transportation is a key factor in promoting TEG [ 11 – 13 ]. The establishment of the transportation system has an obvious effect on improving the accessibility of tourist destinations and promoting the inflow of the tourist population [ 14 ]. However, most existing studies only take tourist arrivals to characterize TM [ 15 – 21 ]. They ignore that the transportation infrastructure is also an important factor affecting the TEG. Therefore, this study redefines TM, which considers both transport infrastructure and tourist arrivals.

Another important purpose of this study is to explore the effect of TM on TEG. Existing literature analyzes the links between TM and international trade [ 22 , 23 ] or focuses on the relationship between economic growth [ 24 , 25 ]. However, less literature has focused on the relationship between TM and TEG. There are two possible reasons for the lack of attention. First, the positive and significant impact of the tourist arrivals and TEG no longer needs to be verified. It is common sense that the more tourists the destination receive, the higher the tourism income. Second, tourist arrivals, as a single indicator to measure TM, are able to affect the TEG. Our measurement of the TM concludes both transport infrastructure and tourist arrivals in this study. Therefore, we decide to explore the contribution of TM to the TEG based on the new measurement for TM.

We first use econometric methods to test whether there is a significant impact of TM on TEG. Considering the positive impact of transport infrastructure on China’s TEG [ 26 ], we hypothesize that TM has a positive impact on TEG. Previous studies have also shown that the spatial spillover effect of tourism may significantly affect the TEG [ 27 – 29 ]. Therefore, we further apply the spatial Durbin model to test the impact of TM on TEG.

Moreover, we also use the LMDI (Logarithmic Mean Divisia Index) method to further analyze the contribution of TM to TEG in more detail. The LMDI method is often used to study environmental issues such as energy consumption and carbon emissions [ 30 , 31 ]. In the field of tourism research, the LMDI method is mostly used to decompose tourism carbon emissions or energy consumption [ 32 , 33 ]. Few studies are using the LMDI to analyze TEG. Therefore, we further use the LMDI method to decompose TEG into five influencing factors including the tourism mobility effects ( TM ), the cumulative traffic effects ( Traffic ), the effects of the tertiary industry ( Industry ), the structural effects of the tourism industry ( Structure ) and the reception effects ( Reception ), and examine the contribution of TM to TEG.

Different from previous studies, this study makes two contributions to the literature. First, we introduce the related concepts of fluid mechanics to construct the indicator TM. We also consider the superposition effect of tourist arrivals and transportation infrastructure. This deepens the understanding of TM and promotes the integration of interdisciplinary knowledge. Second, we are the first to examine the impact of TM on TEG using econometric models and the LMDI method. This deepens the understanding of the mechanisms that influence TEG. The results of this study also provide a reference for tourism-related policy makers. Regions wishing to develop tourism can achieve TEG by expanding the size of the source market and promoting the construction of transportation infrastructure.

The rest of this study is organized as follows. Section 1 summarizes the relevant literature. Section 2 presents the theoretical framework, methods, and data. Section 3 introduces the spatiotemporal pattern and evolutionary trend of TM. Section 4 analyzes the contribution of TM to TEG from two different perspectives. Section 5 discusses and analyzes the research results. The last section concludes this study.

Literature review

As the core of tourism activities, TM refers to the mobility of tourists from the origin to the destination, and the stay of tourists in the region [ 34 ]. It is often associated with tourism demand and is measured by tourist arrivals [ 35 ]. Since the 1970s, many studies have paid attention to the influencing factors and the spatial structure of TM [ 15 , 16 ]. The existence of regional heterogeneity makes TM affected by many factors, such as infrastructure, income, GDP, and cultural distance [ 17 , 18 , 20 ]. Moreover, it also makes the spatial structure of TM different. Therefore, TM prediction has become one of the research hotspots [ 36 ]. A large body of research has focused on TM forecasting [ 21 ], including using a combination and integration of forecasts, using nonlinear methods for forecasting, and extending existing methods to better model the changing nature of tourism data [ 37 ]. The gravity model is an earlier method used to analyze international TM [ 38 ]. Due to its effectiveness in explaining TM [ 22 ], gravity models are often used to analyze international tourism service trade. Although the use of gravity models to predict bilateral TM still lacks a corresponding theoretical explanation mechanism, empirical evidence supports the applicability and robustness of gravity models for TM [ 23 ]. Existing research focuses on examining the movement patterns and spatial structure of international TM in destinations [ 39 ], such as the transfer of inbound TM within regions and the influencing factors of inbound TM within destinations [ 40 ]. There are still few studies on the overall spatial characteristics of TM within destination countries, and the only literature is mainly based on digital footprints or questionnaire data to analyze the spatial structure of TM [ 41 , 42 ].

Unlike the tourist arrivals indicator, which focuses more on the mobility of people, TM examines a wider range of content, including the mobility of people, the mobility of materials, the mobility of ideas (more intangible thoughts and fantasies), and the mobility of technology [ 8 ]. The early tourist movement focused more on tourist travel decisions and the resulting movement patterns. Lue et al. [ 43 ] summarized five travel patterns of tourists between destinations. Li et al. [ 44 ] revealed the spatial patterns of TM and tourism propensity in the Asia-Pacific region over the past 10 years. McKercher and Lau [ 45 ] took Hong Kong as an example and identified 78 movement patterns and 11 movement styles of TM within the destination. In recent years, with the help of technologies such as GPS, GIS, and RFID, the movement of tourists within scenic spots has attracted attention [ 46 ]. Research on visitor movement in national parks, theme parks, protected areas, etc. continues to increase [ 47 – 49 ], and explore the influencing factors of visitor movement [ 50 ], broadening the microscale visitor mobility research content. TM also has economic, social, and cultural impacts on destinations through the movement of tourists. Numerous empirical studies have shown that tourist arrivals have a positive impact on economic growth [ 51 ]. Tourism is an important driver of economic growth [ 52 ]. However, some studies have shown that tourist arrivals do not directly lead to economic growth, but promote TEG through regional economic development [ 53 – 55 ]. The mobility of tourism will also bring about changes in destination transportation facilities. Transportation is not only an important carrier of TM but also an important part of tourists’ travel experience [ 8 ]. It also has a positive impact on destination company value together with TM [ 26 ].

There are many theoretical discussions and empirical studies on the factors influencing TEG. From the perspective of suppliers, resource endowment [ 56 – 58 ] and environmental quality [ 59 – 62 ] are the fundamental factors determining tourism development. Simultaneously, as a typical service industry, human capital and physical capital in the tourism industry [ 63 , 64 ] and service level [ 65 ] will impact tourism economic efficiency. From the perspective of demanders, the rise of per capita income and consumption upgrading continue to drive the transformation in the tourism industry [ 66 ], which in turn leads to an increasing scale of market demand [ 67 ], which provides the possibility of increasing the foreign exchange earnings, local capital accumulation, and consumption spillovers. From the perspective of supporters, scholars have verified the significant effects of factors on TEG, including the transportation facilities and accessibility [ 68 – 71 ], the basis of the economy and marketization [ 72 ], industrial structure [ 73 ], public policy [ 74 – 76 ], and technological progress [ 77 ].

In summary, the research on TM has paid attention to its impact on the regional economy, but they both ignored the role of TM on TEG. Studies of TEG based on static factors have primarily relied on econometric models [ 78 ]. Although the spatial spillover effects of influencing factors have gradually gained attention, its depth is limited and fails to explore the impact of TM and other related factors on the TEG. TM is becoming central to tourism activities and understanding the capital mobility of tourism will have implications for tourism development under the new mobility paradigm [ 79 ]. This study proposes the concept of TM based on the theory of fluid mechanics, explores its impact on TEG, and analyzes the contribution of each influencing factor to TEG.

Theoretical framework, research methods, and data sources

Theoretical framework.

Traditionally, tourism research considers the tourism system as tourist sources, tourist destinations, and tourist corridors (transportation systems) [ 80 , 81 ]. Under the new mobility paradigm, this study regards the spatial transfer of tourists from the source to the destination as a mobility process. Tourist mobility is the fundamental reason for the existence of tourism. If tourists stop flowing, tourism will cease to exist.

It is known that the fluid will be affected by a variety of factors, such as viscosity, density, resistance coefficient, and altitude. As shown in Fig 1 , the total mobility of tourists from a tourist origin to a tourist destination is the number of tourists (Q). The spatial transfer of tourists, on the other hand, requires the use of transportation infrastructure as well as means of delivery. As an essential vehicle to support tourism development, transportation infrastructure directly reflects regional accessibility and relevance and is a crucial factor influencing TM [ 82 – 84 ], and its construction level has different effects on TEG in different regions [ 11 , 85 – 87 ]. According to the equations in fluid mechanics, the average velocity is equal to the flow rate ratio to the cross-sectional area. It can be deduced that TM = Q/TL. TM is determined by the number of tourists (Q) and the length of transportation infrastructure (TL). According to the definition, this indicator considers both tourist arrivals and flow rate, and its significance lies in its ability to characterize the mobility of tourism factors relying on tourists and physical transportation. This paper also connects the factor decomposition method to determine the importance of TM to TEG and presents theoretical implications for identifying essential factors to enhance tourism efficiency and stimulate tourism industry development.

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https://doi.org/10.1371/journal.pone.0275605.g001

Research methods

Measurement of tourism mobility..

Exploratory spatial data analysis.

It is generally believed that tourism has a spatial spillover effect and spatial correlation [ 28 ]. Therefore, we use Exploratory Spatial Data Analysis (ESDA) to detect spatial correlation among the variables. ESDA is used to analyze spatial characteristics through global and local spatial autocorrelation measurements [ 42 , 89 ].

The global Moran’s I is an indicator of whether factors are spatially correlated and its value ranges from -1 to 1. When 0<I≤1, it indicates a positive spatial correlation; when -1≤I <0, it indicates a negative spatial correlation; when I = 0, there is no spatial relationship. The equation is as in ( 2 ).

With a Z statistical test as in Formula ( 4 ), the cluster and outlier analyses can identify H_H (High_High) clusters, L_L (Low_Low) clusters, L_H (low value surrounded by high values) clusters, and H_L (high value surrounded by low values) clusters at a 95% confidence level.

Econometric model.

The econometric model, including tourism economic growth (TEG), tourism mobility (TM), physical capital in the tourism industry (TP), and human capital in the tourism industry (TH), is constructed according to economic growth theory without considering spatial spillover effects. Besides, since the measurement of TM only considers land transportation infrastructure data, the passenger traffic by the airport (TA) is introduced in the model to characterize the air capacity. Eq ( 5 ) represents the econometric model (TEG it ) in province i and year t, where α is the constant term, β is the parameter to be estimated, μ i denotes the spatial effect, and ε it denotes the random error term.

However, the spatial correlation of TEG will lead to biased parameter estimates of traditional econometric models. If the test results of global Moran’s I indicate that TEG is significantly spatially correlated, a spatial econometric model should be introduced to solve the bias-variance problem. The spatial Durbin model ( Eq 6 ) is developed according to Eq 5 . The spatial weight matrix used in the spatial Durbin model is an adjacency matrix. y it represents the TEG in province i and year t; x it represents the TM, TP, TH, and TA in province i and year t; and W ij y jt and W ij x jt are the TEG and lagged terms of each influencing factor, respectively. ρ and φ are spatial lagging coefficients, and v t denotes the time effect.

LMDI decomposition.

The LMDI decomposition method is widely used because it can effectively solve the residual problem in the decomposition and zero and negative values in the data. LMDI In this study, TEG is decomposed according to Eq ( 7 ). The influencing factors of TEG are decomposed into tourism mobility effects ( TE ), cumulative traffic effects ( Traffic ), effects of the tertiary industry ( Industry ), structural effects of the tourism industry ( Structure ), and reception effects ( Reception ). The equations are shown in ( 8 ) to ( 11 ). Traffic indicates the weighted road length; GDP (service) intimates the value added of the tertiary industry; Population represents the population in each province, and Visitors is the number of tourists. Introducing the log-average function L(x,y) defined in Eq ( 12 ). Eq ( 7 ) is decomposed into Eq ( 13 ) by LMDI, where ΔTEG denotes the amount of change in TEG from initial time 0 to period t, and ΔTM、ΔT、ΔI、ΔS、ΔW represent the contribution of each influencing factor to TEG. The equations are shown in ( 14 ) to ( 18 ).

Data sources

The study area is 31 provinces of China (excluding Hong Kong, Macao, and Taiwan), which is divided into seven regions according to the geographical divisions of China. The provinces included in each region are listed in supporting information. Since data availability varies widely across regions, the research period of TM and LMDI decomposition is from 2000 to 2018. As the National Bureau of Statistics of China (NBS) started to collect the employment data of private enterprises and individuals by sector in 2004 and the data for 2018 has not been updated yet, the research period of the spatial econometric model only covers the period from 2004 to 2017.

The data sources involved in the paper are as follows: the transportation infrastructure data come from the China Statistical Yearbook; the number of tourists is obtained from the Statistical Bulletin on National Economic and Social Development. Air passenger traffic data is collected from Civil Aviation Airport Production Statistics Bulletin. We employ the social fixed asset investment in transportation, storage, and postal services, wholesale and retail trade, accommodation and catering, and culture, sports, and entertainment as proxies for physical capital in the tourism industry (TP). This is because various aspects influence tourism development. Considering that only direct tourism investment does not reflect the total investment in tourism by society, we choose the four industries closely related to tourism development as physical capital in the tourism industry.

In this paper, private and individual employees in the transport, storage, and postal industry, wholesale and retail trade, and accommodation and catering industries are used to represent the human capital in the tourism industry (TH). The main reason for this is that, on the one hand, most studies only consider the number of employees in travel agencies, scenic spots, and star hotels, which differs significantly from the actual number of direct and indirect employees in tourism. On the other hand, since private enterprises and individual employment solve more than 80% of the urban employment problem, the number of private enterprises and individual employment in the three industries related to the tourism industry is chosen to represent the human capital. All the above data are collected from the NBS ( http://data.stats.gov.cn ). In the LMDI decomposition, the value added of the tertiary industry and the population in each province come from the China Statistical Yearbook.

Analysis of tourism mobility measurement results

Spatiotemporal evolution characteristics of tourism mobility.

Limited by space, Table 1 only shows the results of TM over five years. During the study period, TM increased from 56~12745 p visitors /km to 382~18865 p visitors /km, with an average annual growth rate between 2.20% and 13.46%. According to the average value of TM ( Fig 2 ), the study areas are divided into the following three types.

https://doi.org/10.1371/journal.pone.0275605.g002

https://doi.org/10.1371/journal.pone.0275605.t001

“Leading Area”, including East China and North China, ranked first and second in all regions. Their TM increased from 2679.39 and 1884.34 p visitors/km in 2000 to 5859.93 and 5209.94 p visitors/km in 2018. However, their annual average growth rates were 5.07% and 6.43%, respectively, ranking first and second from the bottom in all regions. East China is located on the coast, relying on superior natural conditions and an economic foundation, and its regional transportation system is relatively complete. Therefore, it has formed many advantageous tourist resource gathering areas and has become the main tourist destination of inbound tourists in China, and its mobility has long ranked first in the country. As a political and economic center, Beijing has become a tourist attraction for domestic and inbound tourism with a large number of historical and cultural tourism resources. It also drives the joint development of the tourism industry in North China with the Beijing-Tianjin-Hebei urban agglomeration as the core, making North China the second largest core area of TM after East China.
“Stable Area”, including South China, Southwest China, Central China, and Northeast China, ranked third to sixth in all regions. Their TM increased from 903.57p visitors/km, 695.15p visitors/km, 632.06p visitors/km, 493.33 p visitors/km in 2000 to 2626.11p visitors/km, 2754.97p visitors/km, 2857.88p visitors/km, 2244.68 p visitors/km in 2018. The average annual growth rates were 6.58%, 8.81%, 9.06%, and 9.38%, respectively. TM in South China grew rapidly during 2005~2015, while it has gradually slowed down in recent years. This is mainly due to the construction of the early transportation system in South China, which increased tourist mobility. After the basic construction of facilities, the incremental tourist inflows decreased, and the overall growth remained stable. Central China has become one of the core transportation hubs under its location and has driven regional tourism development, becoming a central province in the second echelon of TM. Due to geographical restrictions, Northeast and Southwest China are less connected to the transportation network than coastal areas, resulting in relatively low levels of TM. Northeast China focuses on the development of heavy industry but pays little attention to the tertiary industry, and tourism infrastructure construction and resource development are relatively weak, which leads to low TM. There are many mountains in Southwest China, and its early traffic development level lags. With the opening of the Chengdu-Chongqing high-speed railway and Chengdu-Guizhou high-speed railway, and the development of the air transportation industry, the land and air transportation layout in Southwest China is becoming increasingly mature. Southwest China actively developed its resources, and the tourist inflow increased from 145 million (2000) to 2.994 billion (2018), with an average value of TM catching up with that of southern China during 2016~2018.
“Potential Area”, including Northwest China, ranks last in terms of average tourist mobility. Its TM increased from 282.01 p visitors/km in 2000 to 1427.58 p visitors/km in 2018, but its average annual growth rate was 10.01%, ranking first among all regions. As less developed region, Northwest China has a poor foundation in economic development and openness to the outside world, and TM has long been at the bottom of the list. Although TM in Northwest China has long been at the bottom of the list, its mobility growth rate leads other regions as tourism infrastructure construction and resource development levels have improved under the active promotion of Western Development policies, the Five-Year Plan, and the Territorial Tourism Strategy.

To more intuitively observe the temporal and spatial change characteristics of TM during the study period, we apply the method of natural breaks to classify the 31 provinces. Natural breaks classes are based on natural groupings inherent in the data. Class breaks are identified that best group similar values and maximize the differences between classes. The features are divided into classes whose boundaries are set where there are relatively big differences in the data values. The natural breaks classification method is a data classification method designed to determine the best arrangement of values into different classes. This is done by seeking to minimize each class’s average deviation from the class mean while maximizing each class’s deviation from the means of the other groups [ 92 ]. We divided the 31 provinces into five categories, highest-value area, higher-value area, medium-value area, lower-value area, and lowest-value area, according to the TM in 2000, 2005, 2010, 2015, and 2018. As shown in Fig 3 , (1) Shanghai and Beijing have long been in the highest-value area and higher-value area of TM. Tibet, Qinghai, Ningxia, Xinjiang, Inner Mongolia, Gansu, Jilin, Heilongjiang, Hubei, and Hainan have long been in the lowest-value and lower-value areas. (2) Over time, the number of provinces in the highest-value area and the higher-value area increased significantly, from 2 provinces in 2000 to 12 provinces in 2018. The number of provinces in the lowest-value area and lower-value area significantly decreased, from 26 provinces in 2000 to 12 provinces in 2018; the number of provinces in the medium-value area fluctuated randomly, with the fewest 3 in 2000 and the most 13 in 2015. (3) Except for Shanxi, Northwest China has been in the lowest-value area and the lower-value area for a long time; The TM values in Southwest China have changed greatly. Chongqing and Guizhou have jumped from the lower-value area to the higher-value area, and Yunnan has jumped from the low-value area to the medium-value area. Tibet is relatively stable and has been in the lowest-value area for a long time; South China is relatively stable, but the average value TM in Guangxi has changed greatly, jumping from the lower-value area to the higher-value area; The average TM in Central China has been in the low-value area for a long time. Central China is also relatively stable, and its average TM has long been located in the lower-value area and the medium-value area. Except for Shanghai, which has always been in the highest-value area, the initial value of TM in other provinces in East China has jumped upward. In the Northeast, Liaoning’s TM has always been in a leading position, and it has gradually transitioned from a lower-value area to a higher-value area. However, Jilin and Heilongjiang have always been in the lowest-value area and the lower-value area, respectively. Changes in TM in North China are diverse. Beijing has long been located in the highest-value area and higher value area. Inner Mongolia has been in the lowest-value area for a long time. Hebei is in the lower-value area most of the time. Tianjin and Shanxi changed greatly and finally jumped to the highest-value area and the higher-value area, respectively.

a. 2000, b. 2005, c. 2010, d. 2015, e. 2018.

https://doi.org/10.1371/journal.pone.0275605.g003

We use the standard deviation ellipse to identify the direction of TM in each province. As shown in Fig 3 , the lengths of the minor semiaxis and major semiaxis of the ellipse increased significantly. The growth of the short semiaxis reveals that the degree of dispersion of TM in China’s provinces is gradually increasing. This result is consistent with the previous analysis conclusions that TM in some provinces shows a more obvious transition trend, which makes the overall dispersion of TM increase.

Spatial correlation characteristics of tourism mobility

Global spatial autocorrelation of tourism mobility..

We use ArcGIS 10.8 to calculate the global Moran’s I of TM for 2000–2018, and the results are shown in the table below ( Table 2 ). The global Moran’s I values from 2000 to 2018 were all positive, and the results from 2000 to 2015 were not significant, and the results from 2016 to 2018 were all significant at the 90% level. TM presents a significant positive spatial correlation. This shows that provinces with high TM in China have relatively high TM in their surrounding areas. From the overall trend, the spatial correlation degree of China’s TM has gradually increased, but its value has not exceeded 0.1, indicating that the spatial agglomeration effect of China’s TM is still weak.

https://doi.org/10.1371/journal.pone.0275605.t002

Local spatial autocorrelation cluster of tourism mobility.

The global Moran’s I cannot reflect the spatial correlation exhibited by local regions or individual provinces. We further use ArcGIS 10.8 to draw the LISA cluster diagram for 2000, 2005, 2010, 2015, and 2018 ( Fig 4 ). The research samples are divided into four types of agglomeration: provinces with high TM are surrounded by provinces with high TM (H-H agglomeration), provinces with high TM are surrounded by provinces with low TM (H-L agglomeration), provinces with low TM are surrounded by provinces with high TM (L-H agglomeration), and provinces with low TM are surrounded by provinces with low TM (L-L agglomeration).

https://doi.org/10.1371/journal.pone.0275605.g004

The results show that (1) provinces with H-H aggregation of TM in different periods are relatively stable; L-L and L-H aggregation types are stable but mixed with changes; The H-L aggregation type does not appear, which indicates that there is no "darkness under the light" area for China’s provincial TM. Provinces with high TM can improve the TM of weekly provinces to a certain extent. (2) The H-H agglomeration is mainly concentrated in Jiangsu and Zhejiang. These regions are economically developed and have high per capita discretionary income. Moreover, the tourism infrastructure in these regions is more complete than that in other regions, and the tourist reception scale is also higher, so their TM shows a high local concentration. (3) The L-L agglomeration types are mainly distributed in geographically remote areas such as Qinghai, Tibet, Gansu, and Xinjiang in inland China. Moreover, Xinjiang and Gansu temporarily withdraw from the L-L agglomeration area. The main reason for this pattern is that the transportation infrastructure in the areas above mentioned is relatively underdeveloped. The "space-time compression effect" brought about by the rapid development of China’s transportation is not significant. Furthermore, due to the distance from the main tourist source markets, although the TM shows a high growth rate, it is still in the lowest-value area and the lower-value area for a long time. (4) L-H agglomeration is mainly transferred in Anhui, Shandong and Hebei, and these provinces are located in the “Leading Area”. The average value of TM in the surrounding provinces is generally high, forming a "collapse area" for TM.

The impact of tourism mobility on tourism economic growth

Spatial autocorrelation of tourism economic growth.

In this study, a Monte Carlo simulation was selected to analyze the spatial autocorrelation of TEG ( Table 3 ). Moran’s I was positive from 2000 to 2018. They passed the significance test of different degrees except in 2006, indicating that TEG has a significant positive spatial correlation. Therefore, a spatial econometric model should be selected to analyze the influencing factors of TEG.

https://doi.org/10.1371/journal.pone.0275605.t003

Traditional panel data model

The unit root test using LLC and Fisher showed no unit root for TEG, TM, TH, TP, and TA ( Table 4 ). The Kao test, Pedroni test, and Westerlund test were used to determine the cointegration relationship between the variables. The test results showed a cointegration relationship, indicating that the data can be used for modeling.

https://doi.org/10.1371/journal.pone.0275605.t004

In terms of the regression model, the BP Lagrangian test results show the rejection of the mixed model. Wooldridge and Wald’s test indicates the presence of heteroskedasticity and autocorrelation in the data. The presence of heteroskedasticity would lead to an increase in the variance of the model parameters and invalidate the Hausman test results. If the regression is still performed using the method without heteroskedasticity, it will undermine the validity of the t-test and F-test, while autocorrelation will exaggerate the significance of the parameters. Therefore, the panel model is selected by the over-identification test (Hausman test result is significant), and the result shows that the Sargan-Hansen statistic is 14.32 and significant, so fixed effect modeling should be selected.

To further address heteroskedasticity and autocorrelation, this study uses Driscoll-Kraay standard errors for regression. The results in Table 5 show a significant positive effect of each variable on TEG, where each 1% increase in TM will promote 0.62% growth in the tourism economy.

https://doi.org/10.1371/journal.pone.0275605.t005

Spatial panel data model

In this paper, the specific form of the spatial panel data model was determined by LM-LAG and LM-ERROR tests. If the result of LM-lag is significant and LM-error is not significant, then SLM should be used, and vice versa, SEM should be used. If LM-lag and LM-error statistics are significant, it indicates that the spatial correlation of the lag term and the spatial correlation of the residuals should be considered. In this case, the SDM can be used to set the model. Subsequently, this study determined whether the SDM model would degenerate into SLM or SEM by Wald and LR tests, and the results showed that all passed the significance test. Meanwhile, the test results of LM-lag, LM-error, LM-lag (robust), and LM-error (robust) were significant ( Table 6 ), indicating that the model set using SDM has a certain rationality.

https://doi.org/10.1371/journal.pone.0275605.t006

We selected the regression model through the Hausman test, and the result showed that the value was 19.31, and the corresponding probability value was 0.007, which indicated that the null hypothesis of random effect was rejected. Therefore, the fixed-effect model was selected for regression analysis. Table 7 shows the estimation results, where ρ rejects the original hypothesis only in the Spatio-temporal fixed-effects model. Therefore, this paper provides a specific analysis of the Spatio-temporal fixed-effects model. The regression results indicate that TM shows a significant positive effect on regional TEG.

https://doi.org/10.1371/journal.pone.0275605.t007

According to the results and spatial effect decomposition ( Table 8 ), ρ is -0.559, indicating that the growth of the tourism economy in neighboring provinces will have a negative impact on the local area. The direct effect of TM is significant, indicating that TM will promote TEG. However, the indirect effect results show that the increase in TM in neighboring provinces will have a negative impact on the local TEG.

https://doi.org/10.1371/journal.pone.0275605.t008

Decomposition of the influencing factors by LMDI.

We decompose the influencing factors and analyze their contribution trend. Table 9 shows the specific contribution of each influencing factor to the TEG in the seven regions.

https://doi.org/10.1371/journal.pone.0275605.t009

ΔT increases from 15.41% in 2000~2005 to 22.55% (2005~2010), and then decreases to 9.35% in 2010~2015 and 7.01% in 2015~2018. Overall, the ΔT showed a downward trend, but it is still an important factor in promoting TEG. The average contribution rate of the ΔT from 2000 to 2018 reached 14.82%.

ΔI maintained an overall downward trend during 2000 ~2018. It gradually decreased from 31.42% (2000~2005) to 22.94% (2015~2018). In contrast, the added-value of tertiary industry per capita increases from 3653 yuan to 34,969 yuan in the same period, indicating that the contribution of tertiary industry to TEG continues to decline, and tourism is gradually decoupled from the development of the tertiary industry.

ΔS maintained an overall upward trend during 2000~2018, from 7.09% (2000~2005) to 14.67% (2015~2018). The overall contribution rate was 11.50%, indicating that increasing the proportion of the tertiary industry in tourism can promote TEG.

ΔR shows a negative effect on TEG, and the degree of adverse effect increases slowly from 26.22% to 27.67%. The overall contribution rate was 28.67%. Reception is defined as the ratio of the resident population to the number of tourists. This shows that on the premise that the permanent resident population remains basically unchanged, the contribution to TEG can be effectively increased by expanding the scale of tourists.

Regression results of tourism mobility on tourism economic growth

This study briefly analyzes the regression results of the traditional and spatial panel data model. However, the spatial autocorrelation test results of TEG show an overall trend of fluctuating and increasing spatial correlation, especially with 2009 as the abrupt change point and a significant increase in the degree of agglomeration. Therefore, the article discusses the results of the spatial panel data model in detail, and the primary purpose of analyzing the traditional panel data model is to compare it with the spatial econometric results.

The regression results of the spatial econometric model show that both TM and TA have a significant positive impact on TEG, which verifies the hypothesis we proposed above. This result is also consistent with Wu et al. [ 93 ] and Perboli et al. [ 94 ]. In contrast, TP and TH have no significant impact on TEG. However, previous studies have also shown that the spatial spillover effect of tourism can significantly affect the TEG [ 27 – 29 ]. Therefore, the impact of TP and TH on TEG remains to be further confirmed.

According to the decomposition results, TM will promote the growth of the local tourism economy but will have a negative impact on neighboring provinces, which indicates a more obvious competition in tourism development among provinces. The increase in mobility in a particular place under a given number of tourists will lead to a diversion of tourists, which will have a negative impact on neighboring regions. Therefore, the tourism industry should also pay attention to the competitive situation in the surrounding areas. The development of tourism focus not only on improving local tourism mobility but also on neighboring areas. Both TP and TH manifest substantial spatial spillover effects. The increase in TP and TH in neighboring areas will produce positive effects, making local areas attach importance to the development of tourism resources and enhancing tourism attraction. TA has a significant positive contribution to TEG, which is consistent with the conclusion of Yang and Wong [ 27 ]. However, the spatial spillover effects of TA on TEG are not significant, which may be related to the fact that air traffic does not depend on adjacent spaces.

Analysis of influencing factors’ contribution rate to tourism economic growth

Tm and δtm..

The ΔTM in North, Central, Southwest, and South China all show a trend of "falling and rising." It should be noted that the ΔTM in North China was negative from 2005 to 2010, mainly due to the significant decline in TM in Tianjin and Hebei. The improvement in the transportation infrastructure has a significant impact on TM in Central and Southwest China. The opening of high-speed railroads is a fundamental reason for the fluctuation in ΔTM. For South China, due to the implementation of the overnight visitor count statistics in the tourism statistics system of Guangdong in 2015~2018, the number of tourists decreased significantly compared to 2010~2015, which in turn led to a significant weakening of the ΔTM. In contrast to the regions mentioned above, the ΔTM in Northeast China shows a trend of "rising and falling" changes. From 2010 to 2015, the contribution of TM to TEG in Northeast China declined and was negative. The main reason is the overall decline of the regional economy in the Northeast region at this stage. In 2014 and 2015, the GDP growth rates of Northeast China were 4.23% and -0.84%, respectively, ranking second and last among the seven regions in China during the same period. At the same time, the Northeast region began to carry out statistical "squeeze water" at this stage, which caused obvious fluctuations in the scale of tourists. Therefore, the downturn in the regional economic environment and stricter tourism statistics have negatively affected the contribution of tourism mobility to tourism economic growth. However, since 2016, China has put forward the " all-for-one tourism" policy. Provinces began to pay more attention to the role of tourism in regional economic growth. All-for-one tourism policies and new management systems have led to the continuous improvement of TM in Northeast China from 2015 to 2018, and the contribution to TEG has increased significantly compared with 2010–2015. The ΔTM in East China gradually increased from 6.35% to 25.66%, which is related to the opening of the high-speed railroad network in 2010, leading to a significant increase in TM. Northwest China has made the tourism industry a key point for economic growth, and its tourist reception and transportation construction levels have been rapidly improved under the impetus of the all-for-one tourism strategy.

Traffic and ΔT.

The contribution of ΔT to TEG generally shows a downward trend. However, during the same period, Traffic showed a gradual upward trend. In 2018, it increased by 258.72% compared with 2000. Among them, it increased by 35.61% from 2000 to 2005, increased by 91.36% from 2005 to 2010, increased by 24.83% from 2010 to 2015, and increased by 10.73% from 2015 to 2018. From this, it can be judged that there may be a "threshold" in the transportation infrastructure. When the stock of transportation infrastructure in China reaches a certain level, the accumulation of transportation infrastructure cannot improve the contribution to the TEG. The role of transportation infrastructure in influencing tourists’ decisions and determining TM cannot be ignored. However, its contribution rate gradually decreases as transportation facilities are gradually improved and regional accessibility differences narrow. The ΔT is 14.82% during the examination period, in which the contribution rate of Traffic to TEG in East China (16.15%), Central China (17.44%), Southwest China (15.75%), and Northwest China (15.40%) is higher than that in North, Northeast and South China. This is mainly because Central China and East China are the regions with the largest passenger turnover in China. From 2000 to 2018, the average passenger turnover in Central China and East China was 118.988 billion person-kilometers and 84.595 billion person-kilometers, respectively. The Southwest China and Northwest China are among the regions with the fastest growth in passenger turnover in China, increasing by 3.13 times and 1.77 times respectively, ranking first and second in all regions.

Industry and ΔI.

The tertiary industry consists of transportation, warehousing and postal industry, information transmission, real estate industry, financial industry, wholesale and retail industry, accommodation and catering industry, etc. Tourism is only a part of it. The per capita added value of the tertiary industry reflects the degree of development of the service industry in various regions, and this indicator has achieved a relatively large increase in terms of changing trends. It increased from 3,653 yuan in 2000 to 34,969 yuan, an increase of 8.57 times. The contribution of ΔI to TEG has gradually declined, mainly due to the slowdown in the growth rate of the per capita added value of the tertiary industry. The growth rate dropped from 91.30% in 2000–2005 to 34.35% in 2015–2018. The contribution of ΔI to TEG in North China, South China, Northwest China, and Southwest China is consistent with the national trend. Northeast China, East China, and Central China show different trends. Especially in the Northeast region, the contribution of ΔI to TEG has dropped significantly. The overall contribution rate of Industry reached 28.18%, indicating that the quality of tertiary industry development has a vital role in promoting TEG. ΔI is generally stable in East and Central China and declines significantly in Northeast China, which may be related to the deceleration of tertiary industry development, as the data show that the added-value of tertiary industry per capita in Liaoning, Heilongjiang, and Jilin increased by 93.04%, 75.15% and 90.43% from 2010 to 2015, while it only grew by 0.63%, 39.88% and 23.18% from 2015 to 2018. Central China was inconsistent with the overall national trend from 2005 to 2010. This is mainly due to the slow increase in the per capita added value of the tertiary industry during this period, ranking last in all regions. During this period, the industrial structure of Central China was still dominated by industry. In 2010, the average industrial added value accounted for 56.37% of GDP, the highest in all regions of the country. East China was inconsistent with the overall national trend in 2015–2018. The main reason is that the proportion of the tertiary industry in Fujian and Jiangxi in the region has not exceeded 50%, and there is a large room for optimization and improvement of the industrial structure. Therefore, the growth rate of the added value of the tertiary industry per capita exceeds the previous stage, and the contribution of ΔI to TEG is still rising.

Structure and ΔS.

The share of tertiary industry in tourism in Beijing and Tianjin increased significantly from 2010 to 2018 compared to 2000, leading to the rapid growth of ΔS in North China. The ΔS in Northeast China was -3.96% from 2005 to 2010, mainly since the growth rate of tertiary industry in Heilongjiang and Liaoning lagged behind that of the tourism industry. The ΔS in East, Central, and Southwest China is relatively stable, indicating that tourism and tertiary industry maintain a coordinated development. The ΔS in South China has achieved a shift from negative to positive growth. As the economic volume of Guangdong accounts for a large proportion in South China and the growth rate of tourism significantly lags behind the development rate of the tertiary industry, it leads to a low ΔS in South China from 2000 to 2010. The opening of high-speed rail provides new opportunities for tourism development, and the ΔS in South China gradually increased to 14.38% and 10.73% in 2010~2018. The ΔS in Northwest China has been increasing, which suggests that the tourism economy is the primary driver of tertiary industry growth. The continuous growth of the ΔS contribution to TEG is partially consistent with the findings of Chang et al. [ 95 ], De Vita and Kyaw [ 96 ], and Zuo and Huang [ 97 ]. The higher Structure is, the greater the contribution of ΔS to TEG. However, the literature above mentioned also pointed out that ΔS has a turning point. For example, Zuo and Huang [ 97 ] found that this value in China is 8.25%.

Reception and ΔR.

The ΔR has a negative impact on TEG. Zuo and Huang [ 97 ] used the ratio of tourist arrivals to the permanent resident population to characterize tourism specialization in a study evaluating China’s tourism-oriented economic growth. Before reaching the inflection point of 30.34 (that is, the tourism reception effect value is 0.03), this indicator has a significant positive impact on TEG. From 2000 to 2018, the tourism reception effect value dropped from 1.47 to 0.11, still less than 0.03. Therefore, the results of our study also partially confirm the research of Zuo and Huang [ 97 ]. While expanding the scale of tourists, various regions should also pay attention to the "inflection point" of the Reception value. When the inflection point is reached, the larger the scale of tourists is, the smaller the contribution to the TEG. However, the ratio of regional population to tourist decreases from 1.47 to 0.11 during the period from 2000 to 2018, indicating that not only the number of tourists should be taken into account, but also the quality of the tourism and the per capita tourism consumption should be attached importance to the TEG. ΔR is relatively stable, among which the southwest and northwest China have the most significant negative contribution to the TEG, indicating that the growth rate of the number of tourists received in the above regions is higher than that of other regions.

Conclusions

This paper proposes the concept of TM based on the hydrodynamic equation, constructs an econometric model of TEG with TM as the core explanatory variable, explores the direct and indirect effects of TM on TEG, measures the specific contribution of each influencing factor using the LMDI decomposition, and draws the following conclusions.

The TM in China has maintained rapid growth for a long time. However, there are differences in the rate of growth in different regions. East China and North China are Leading Area, with the highest average tourism mobility, but the smallest average annual growth rate; Central China, South China, Northeast China, and Southwest China are Stable Area, with the middle average TM and average annual growth rate; Northwest China is Potential Area, which has the smallest average TM, but the largest average annual increase. The TM in each region only showed a significant positive spatial correlation in 2016–2018. The space-time pattern is constantly changing over time. The high-value areas and high-value areas of TM increased significantly, while the low-value areas and low-value areas decreased significantly. The local spatial autocorrelation results of TM are stable, and various agglomeration states are stably distributed in some provinces.
The regression results of the traditional panel data model and the spatial panel data model both show that TM has a significant positive effect on TEG. Under the premise of considering the spatial effect, the improvement of TEG in a province by TM will have a negative impact on the adjacent province.
Applying the LMDI decomposition method, the TEG is decomposed into TM , Traffic , Industry , Structure , and Reception. The results show that the contribution of TM and Structure to TEG showed an upward trend, with average annual contribution rates of 15.76% and 11.50%, respectively. It indicates that improving TM is a crucial way to promote tourism development. The contribution of the Traffic and Industry to TEG generally showed a downward trend, with average annual contribution rates of 14.82% and 28.18%, respectively. The Reception has a negative impact on the TEG, but it is still a positive contribution, with an average annual contribution rate of 28.67%. The five types of effects of TEG decomposition were different due to regional differences.

The main contributions of this study are as follows: (1) Based on fluid mechanics, we constructed an indicator of TM. We comprehensively consider the impact of tourist arrivals and transportation infrastructure on TEG, which is rarely proposed by scholars in the literature. Our research enriches the research on the influencing factors of TEG. (2) We analyze the influence of TM on TEG based on the econometric model, which highlights the importance of TM. Moreover, we found that TM has negative spatial overflow.(3) Based on the LMDI method, we decompose TEG into five major effects, rather than just considering traditional variables such as human input, capital input, and tourism resource input. Our study further enriches the research on the influencing factors of TEG.

Based on our findings above, we draw the following policy implications. To improve TEG, late-developing regions should improve TM by building large-scale tourism transportation infrastructure, promoting destination marketing to attract tourists, and paying attention to the possible negative effects of increased TM in neighboring regions. At the same time, the improvement of TM should be emphasized at different stages. The threshold effect of tourism transportation infrastructure should also be fully considered. After the transportation infrastructure reaches a certain stock, its contribution to TEG will decrease. At this time, expanding the scale of tourists should become the main tourism development policy.

There are still some limitations in this study. It is difficult to directly collect data on the inflow and outflow of tourist between certain provinces. Therefore, we only select inflow of tourists as the primary data and do not consider the influence of the tourists’ outflow on TM. In fact, increased transport accessibility will not only expand the inflow of tourists but also affect the outflow of tourists. Therefore, the superposition effect of traffic and tourist inflow/outflow should be considered comprehensively to improve the scientific rationality of TM measurement. This study lacks comparative studies across multiple countries. The research in our study may show differentiated findings for developed or less developed countries. When constructing the econometric model, we mainly consider TM as the core explanatory variable, and only select human input and capital input, and air traffic related to traffic as control variables from the perspective of the economic growth model. In the future, the theory and practice of TM will be further explored with multivariate data to form a more rigorous and systematic cognitive framework.

Supporting information

S1 fig. map of the seven regions..

https://doi.org/10.1371/journal.pone.0275605.s001

S1 File. Research data.

https://doi.org/10.1371/journal.pone.0275605.s002

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The contribution of tourism mobility to tourism economic growth in China

1 School of Tourism, Hubei University, Wuhan, Hubei, China

Mengting Yue

2 School of Urban and Regional Science, East China Normal University, Shanghai, China

3 School of Business, Hubei University, Wuhan, Hubei, China

4 School of Tourism, Hainan University, Haikou, Hainan, China

Associated Data

The data on RAILWAY, HIGHWAY, ROAD1, ROAD2, ROAD, GDP, TERTIARY INDUSTRY, and POPULATION are from the Chinese Nation Bureau of Statistics ( https://data.stats.gov.cn/easyquery.htm?cn=C01 ). The data on TOURISM REVENUE and VISITORS are from the CEIC database ( https://insights.ceicdata.com ). The data on TRAFFIC, TOURISM MOBILITY, RECPTION, INDUSTRY, and STRUCTURE were calculated by the authors. Please see the paper for details.

Introduction

Literature review

Theoretical framework, research methods, and data sources

Theoretical framework.

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Object name is pone.0275605.g001.jpg

Research methods

Measurement of tourism mobility.

The basic principle of fluid mechanics is that the average velocity is proportional to the flow rate and inversely proportional to the cross-sectional area. This paper characterizes the flow rate by the number of tourist inflows, and the length of transportation infrastructure represents the cross-sectional area, with the equation as ( 1 )

where T M i t represents the TM in province i and year t; Q i t is the number of tourists in province i and year t; and T L i t is the length of the weighted transportation infrastructure in province i and year t, including railroads, highways, primary roads, secondary roads, and other grades of roads. China’s railway and road passenger traffic accounts for the vast majority of the total passenger traffic. Furthermore, we were unable to calculate weighted air and water transportation infrastructure lengths, so we only consider the land transportation infrastructure data. The length of transportation infrastructure is weighted according to Chen et al. [ 88 ].

Exploratory spatial data analysis

Where TEG i and TEG j denote the tourism revenue of provinces i and j, respectively; n is the number of provinces; T E G ¯ denotes the average value of tourism revenue of each province; W ij represents the spatial weight matrix of provinces i and j. We choose the adjacency matrix and use Guangdong and Guangxi as the neighboring provinces of Hainan.

Local spatial autocorrelation is used to explore cluster patterns and spatial patterns [ 90 , 91 ]. We analyze the local spatial autocorrelation characteristics through cluster and outlier analyses. The calculation process is expressed as Formula ( 3 ):

Econometric model

LMDI decomposition

Data sources

Analysis of tourism mobility measurement results

Spatiotemporal evolution characteristics of tourism mobility.

An external file that holds a picture, illustration, etc.
Object name is pone.0275605.g002.jpg

“Leading Area”, including East China and North China, ranked first and second in all regions. Their TM increased from 2679.39 and 1884.34 p visitors/km in 2000 to 5859.93 and 5209.94 p visitors/km in 2018. However, their annual average growth rates were 5.07% and 6.43%, respectively, ranking first and second from the bottom in all regions. East China is located on the coast, relying on superior natural conditions and an economic foundation, and its regional transportation system is relatively complete. Therefore, it has formed many advantageous tourist resource gathering areas and has become the main tourist destination of inbound tourists in China, and its mobility has long ranked first in the country. As a political and economic center, Beijing has become a tourist attraction for domestic and inbound tourism with a large number of historical and cultural tourism resources. It also drives the joint development of the tourism industry in North China with the Beijing-Tianjin-Hebei urban agglomeration as the core, making North China the second largest core area of TM after East China.
“Stable Area”, including South China, Southwest China, Central China, and Northeast China, ranked third to sixth in all regions. Their TM increased from 903.57p visitors/km, 695.15p visitors/km, 632.06p visitors/km, 493.33 p visitors/km in 2000 to 2626.11p visitors/km, 2754.97p visitors/km, 2857.88p visitors/km, 2244.68 p visitors/km in 2018. The average annual growth rates were 6.58%, 8.81%, 9.06%, and 9.38%, respectively. TM in South China grew rapidly during 2005~2015, while it has gradually slowed down in recent years. This is mainly due to the construction of the early transportation system in South China, which increased tourist mobility. After the basic construction of facilities, the incremental tourist inflows decreased, and the overall growth remained stable. Central China has become one of the core transportation hubs under its location and has driven regional tourism development, becoming a central province in the second echelon of TM. Due to geographical restrictions, Northeast and Southwest China are less connected to the transportation network than coastal areas, resulting in relatively low levels of TM. Northeast China focuses on the development of heavy industry but pays little attention to the tertiary industry, and tourism infrastructure construction and resource development are relatively weak, which leads to low TM. There are many mountains in Southwest China, and its early traffic development level lags. With the opening of the Chengdu-Chongqing high-speed railway and Chengdu-Guizhou high-speed railway, and the development of the air transportation industry, the land and air transportation layout in Southwest China is becoming increasingly mature. Southwest China actively developed its resources, and the tourist inflow increased from 145 million (2000) to 2.994 billion (2018), with an average value of TM catching up with that of southern China during 2016~2018.
“Potential Area”, including Northwest China, ranks last in terms of average tourist mobility. Its TM increased from 282.01 p visitors/km in 2000 to 1427.58 p visitors/km in 2018, but its average annual growth rate was 10.01%, ranking first among all regions. As less developed region, Northwest China has a poor foundation in economic development and openness to the outside world, and TM has long been at the bottom of the list. Although TM in Northwest China has long been at the bottom of the list, its mobility growth rate leads other regions as tourism infrastructure construction and resource development levels have improved under the active promotion of Western Development policies, the Five-Year Plan, and the Territorial Tourism Strategy.

An external file that holds a picture, illustration, etc.
Object name is pone.0275605.g003.jpg

a. 2000, b. 2005, c. 2010, d. 2015, e. 2018.

Spatial correlation characteristics of tourism mobility

Global spatial autocorrelation of tourism mobility.

Local spatial autocorrelation cluster of tourism mobility

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Object name is pone.0275605.g004.jpg

The impact of tourism mobility on tourism economic growth

Spatial autocorrelation of tourism economic growth.

***, **, * indicate passing the significance test at the 1%, 5%, and 10% levels, respectively.

Traditional panel data model

***, **, * indicate significance at the 1%, 5%, and 10% levels, respectively.

Spatial panel data model

***, **indicate passing the significance test at the 1% and 5% levels, respectively.

***, * indicate passing the significance test at the 1% and 10% levels, respectively.

Decomposition of the influencing factors by LMDI

We decompose the influencing factors and analyze their contribution trend. Table 9 shows the specific contribution of each influencing factor to the TEG in the seven regions.

ΔTM declined from 12.54% (2000~2005) to 7.35% (2005~2010), increased to 19.38% (2010~2015), and then reached 21.87% (2015~2018). The contribution of TM to TEG is stable at 14.66%~17.92%, except for Central China (11.96%), with an overall contribution of 15.76%, indicating that TM has a catalytic effect on TEG, and enhancing TM is a crucial way to promote tourism development.

Regression results of tourism mobility on tourism economic growth

Analysis of influencing factors’ contribution rate to tourism economic growth

Tm and Δtm.

Traffic and ΔT

Industry and ΔI

Structure and ΔS

Reception and ΔR

Conclusions

The TM in China has maintained rapid growth for a long time. However, there are differences in the rate of growth in different regions. East China and North China are Leading Area, with the highest average tourism mobility, but the smallest average annual growth rate; Central China, South China, Northeast China, and Southwest China are Stable Area, with the middle average TM and average annual growth rate; Northwest China is Potential Area, which has the smallest average TM, but the largest average annual increase. The TM in each region only showed a significant positive spatial correlation in 2016–2018. The space-time pattern is constantly changing over time. The high-value areas and high-value areas of TM increased significantly, while the low-value areas and low-value areas decreased significantly. The local spatial autocorrelation results of TM are stable, and various agglomeration states are stably distributed in some provinces.
The regression results of the traditional panel data model and the spatial panel data model both show that TM has a significant positive effect on TEG. Under the premise of considering the spatial effect, the improvement of TEG in a province by TM will have a negative impact on the adjacent province.
Applying the LMDI decomposition method, the TEG is decomposed into TM , Traffic , Industry , Structure , and Reception. The results show that the contribution of TM and Structure to TEG showed an upward trend, with average annual contribution rates of 15.76% and 11.50%, respectively. It indicates that improving TM is a crucial way to promote tourism development. The contribution of the Traffic and Industry to TEG generally showed a downward trend, with average annual contribution rates of 14.82% and 28.18%, respectively. The Reception has a negative impact on the TEG, but it is still a positive contribution, with an average annual contribution rate of 28.67%. The five types of effects of TEG decomposition were different due to regional differences.

Supporting information

Funding Statement

This work was supported by grants from National Social Science Foundation of China [grant number 17CJY051].

Data Availability

PLoS One. 2022; 17(10): e0275605.

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23 May 2022

PONE-D-22-06360The Contribution of Tourism Mobility to Tourism Economic Growth in ChinaPLOS ONE

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Reviewer #2: No

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for the opportunity to review the manuscript. This paper is aiming to examine the contribution of variables to TEG, focusing on tourism mobility. The authors also used a statistical method to verify the model or variables and provided validity. Despite those efforts being seemingly great, the authors can further improve the manuscript with revisions. The reviewer would like to comment as follows and hope these comments will be helpful for the authors.

1. The purpose and the implications of this study

The authors mentioned the purpose of this study as follows. “This paper put forward the concept of tourism mobility based on the theory of fluid mechanics, explores its impact on TEG, and analyzes the contribution of each influencing factor to TEG which will provide implications for the high-quality development of tourism economy.” Please clearly define “high-quality development.” What is the definition of quality? What are the relationships between quality and tourism mobility? As for the implications mentioned in the Conclusion, it is unclear which is of high quality. The authors only mentioned that improving tourism mobility is crucial for promoting tourism development. The findings of this study were comprehensible; however, the authors should clearly define high quality because that is one of the aims of this study.

2. Econometric model and LMDI decomposition

In 1.2.4, there are the same issues as above. For example, traffic effects (Traffic) cannot be found in Equations (5)-(8). Is it the same as Transportation? Industry cannot be found in the equation. Additionally, it was mentioned that tourism mobility only considers land transportation in 1.2.3. In 1.2.4, Transportation indicates the weighted road length. What is the reason behind employing only road length as the weight value?

3. Sections 2.1 and 4.1: Regions

This paper segmented China into seven regions and introduced each region with the data in section 2.1. The authors also introduce each transportation network region, mobility, and policies regarding transportation in 4.1. However, the introduction of each region in 4.1 should be provided in 2.1. Those situations are the presumptions of the analysis, not resulting from the estimation.

The explanation for Figure 2 should be reconfirmed and revised appropriately. The authors mentioned that tourism mobility in South China grew rapidly; however, the classification defined in Group (2) includes South China, Southwest China, Central China, and Northeast China. If South China means Group (2) in the sentence, it should describe these regions instead of “South China” only.

4. Sections 3.4 and 4.3: Factors

In section 3.4, the influencing factors in each period are considered with the estimation result. However, some factors were discussed by comparing the result between 2000~2005 and 2015~2008; others were only with the overall contribution rate, meaning 2000~2018. What is the reason for considering the factors differently? It seems the authors want to mention (inversed) V shape change. If so, the authors should mention the meaning of (inverse) V change in 3.4 and discuss it in 4.3 using the term “(inverse) V change.”

As for the factor of host-guest interaction, the result showed a negative effect on TEG. What does the effect of host-guest interaction mean to TEG? Please discuss this point in 4.2.

Table 8 should be revised for the comprehension of readers. At the bottom of the table, the overall estimation result is described as 2000~2018; however, this description is confusing. For example, it is more understandable if named “Overall contribution through 2000~2018.”

In 4.3, the authors mentioned that the support for the tourism industry has weakened in Northeast China; however, there was no evidence for it. This is an insufficient explanation and a jump of logic. Please provide evidence to support the logic.

5. Recommendation: words and definitions

For the sake of clarity and appropriateness, the authors should consider changing some words and definitions. In Figure 1, tourist sources seem better changed to tourist origins. In 1.2.4, the authors defined host-guest interactions effects; however, host-guest interactions are defined and developed on the scale in other areas, such as residents’ attitudes toward tourism development. The host-guest interactions in this study seem to mean the ratio of tourists per resident, and it is more appropriate to change the variable’s name. Additionally, please consider clarifying what the host-guest interaction effect means to TEG?

6. Recommendation: Shifting the future assignments from the Discussion section to Conclusion.

The authors mentioned the limitation and the future assignments of this study at the end of the Discussion section. It would be better to mention those points at the end of the Conclusion. The reviewer would like to recommend shifting the last paragraph (future assignment) to the end of the Conclusion.

7. Minor comments: Spelling errors and inconsistent terms

The authors should check and revise the misspelled words and inconsistencies throughout the manuscript. For example, GDP (ervice) in Equation 8 should be corrected to GDP (service).

Reviewer #2: Review of “The Contribution of Tourism Mobility to Tourism Economic Growth in China”

The manuscript has the potential to make various contributions to the tourism-related policy literature but should be strengthened by addressing several critical deficiencies (outlined below).

(a) Introduction

o It would be helpful to briefly explain the connections between tourism mobility (TM) and tourism economic growth (TEG) from the geospatial perspective early in the paper and return to this when discussing the policy implications of the research findings.

o Although the authors have given some explanation of the current situation of economic development in China, it is currently insufficient. More context is needed regarding the present situation of the tourism industry, TM, and TEG.

o The research question(s) and main hypothesis or hypotheses should be included in this section.

o The second and third paragraphs of this section should be restructured as a new, separate literature review section. The authors are encouraged to include more previous studies and discuss them in greater depth in the literature review, as the literature reviewed in the original version of the manuscript is insufficient.

(b) Econometric model

o A few words or a sentence should be sufficient to describe the spatial weight matrix used in equation 4.

o The last paragraph of this section (about the seven regions of the study area) should be moved to the Appendix. Including a map inset of these areas would be helpful here, as well.

(d) Spatial evolution

o It would be helpful to include a LISA cluster map of to help readers better understand the spatial patterns described here.

(e) Discussion

o This section does not sufficiently analyze the authors’ findings in the context of previous studies. How and why does this study differ from previous research? Relatedly, there are some similarities between this study’s results and those of previous research—what is the unique contribution of this paper? Please emphasize these differences more clearly.

o It seems that the policy implications of this manuscript are limited. Thus, the authors are encouraged to derive policy implications or recommendations based on their findings. Such implications need not be restricted to Chinese tourism-related policies but may also be applicable to other countries with conditions similar to China. The authors are encouraged to clarify these issues in this section.

o Theoretical and practical contributions should be highlighted more explicitly in this section.

o The authors are encouraged to expand their discussion of the research limitation(s), as this issue is currently insufficiently explained.

Final comments

Given the several critical deficiencies pointed out above, this paper is not ready for publication in its current form. I recommend a major revision, with resubmission possible if the authors are able to logically and satisfactorily address these deficiencies.

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Reviewer #1: No

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Submitted filename: 20220523_Review_Plos_One_01.docx

Author response to Decision Letter 0

Letter to Reviewer ’s Comments

Dear anonymous reviewer,

Thanks deeply for your comments. We had studied all of your constructive and helpful comments very carefully and tried to incorporate all of them into the current version of the paper, it definitely improved the paper a great deal.

Please find the point-by-point response to each of your comment and more detailed descriptions of how we did that below. For ease of distinction, your originals comments are underlined, and our responses to those comments and amends are shown in regular font. The sentences from manuscript are in italic font.

*For your convenience, the page number and section number listed below are based on the “Revised Manuscript with Track Changes”.

Reviewer #1:

Thank you for the opportunity to review the manuscript. This paper is aiming to examine the contribution of variables to TEG, focusing on tourism mobility. The authors also used a statistical method to verify the model or variables and provided validity. Despite those efforts being seemingly great, the authors can further improve the manuscript with revisions. The reviewer would like to comment as follows and hope these comments will be helpful for the authors..

1.The purpose and the implications of this study

The authors mentioned the purpose of this study as follows.“This paper put forward the concept of tourism mobility based on the theory of fluid mechanics, explores its impact on TEG, and analyzes the contribution of each influencing factor to TEG which will provide implications for the high-quality development of tourism economy.” Please clearly define “high-quality development.”What is the definition of quality? What are the relationships between quality and tourism mobility? As for the implications mentioned in the Conclusion, it is unclear which is of high quality. The authors only mentioned that improving tourism mobility is crucial for promoting tourism development. The findings of this study were comprehensible; however, the authors should clearly define high quality because that is one of the aims of this study.

Response 1：Thank you very much for your constructive comment. High-quality development is proposed by the Chinese government in 2017. It shows that China's economy shifts from a high-speed growth stage to a high-quality development stage. Since 2020, researches on the high-quality development of tourism have begun to appear (Liu and Han, 2020;Shi et al.,2021;Lu, 2022). However, there is still no unified definition of high-quality tourism. Some studies use specific indicators such as the average consumption level of tourists or the conversion efficiency of tourism resources to measure the level of high-quality tourism. Some other studies also build an index system to measure the level of high quality. No matter what the widely accepted definition of high-quality tourism development is, it implies that the development of tourism should shift from the pursuit of scale to the requirement of quality. Whether tourism mobility can be used as an indicator to measure the high-quality development of tourism in this study is still inconclusive. However, tourism mobility is a comprehensive indicator that comprehensively considers the flow of tourists and regional transportation infrastructure. At the same time, in the process of LMDI decomposition, tourism economic growth is decomposed into different effects. These are not only the total output, but also the quality of tourism. To sum up, the research conclusions put forward in the introduction part of this study have reference value for the high-quality development of regional tourism. Since the focus of this study is not on the high-quality development of the tourism economy, we believe that specifically defining the high-quality development of tourism may make the research go off-topic. Therefore, to avoid ambiguity, we decided to delete the expression “high-quality development”. Thanks still for your suggestion!

In section 1.2.3, the authors defined Equation (3) as an econometric model and mentioned each variable in the first paragraph. However, some variables are inconsistent or cannot be found in Equation (3). For example, tourism mobility (TM) cannot be found in the equation. Mobility in the equation seems to mean TM; however, their meanings are different. TP, TH, and TA in the paragraph cannot be found in the equation. TK in the equation was not mentioned in the first paragraph. Please check the equations carefully and revise them.In 1.2.4, there are the same issues as above. For example, traffic effects (Traffic) cannot be found in Equations (5)-(8). Is it the same as Transportation? Industry cannot be found in the equation. Additionally, it was mentioned that tourism mobility only considers land transportation in 1.2.3. In 1.2.4, Transportation indicates the weighted road length. What is the reason behind employing only road length as the weight value?

Response 2: Thank you very much for your careful reading and reminder. First, we are sorry for the confusing equation and inconsistent variable names. We have corrected this inconsistent variable names and checked for them all throughout the paper again. The one-to-one correspondence between variable names and variable meanings is as follows：

TEG——tourism economic growth

TM——tourism mobility

TH——human capital in the tourism industry

TA——passenger traffic by the airport

TP——physical capital in the tourism industry

Traffic——the cumulative traffic effects by LMDI decomposition

Industry——the effects of the tertiary industry by LMDI decomposition

Structure——the structural effects of the tourism industry by LMDI decomposition

Reception——the reception effects by LMDI decomposition

——the contribution of TM to TEG

——the contribution of the Traffic to TEG

——the contribution of the Industry to TEG

——the contribution of the Structure to TEG

——the contribution of the Reception to TEG

Second, the reason for employing only land transportation to measure the tourism mobility is that China's railway and road passenger traffic accounts for the vast majority of the total passenger traffic. The average share of rail and road passenger traffic from 2000 to 2018 was 97.6%. We added a few sentences “China's railway and road passenger traffic accounts for the vast majority of the total passenger traffic. Furthermore, we were unable to calculate weighted air and water transportation infrastructure lengths, so we only consider the land transportation infrastructure data.” in section 2.2.1 (page 7) to explain the reason for employing only road length as the weight value.

Response 3: Thank you very much for your constructive comment. We rewrite the section 3.1 (page 15) Spatiotemporal evolution characteristics of tourism mobility. First, we moved the content as you suggested. Second, we revise the content of section to explain the Figure 2. We divided into three paragraphs to analyze the temporal evolution of tourism mobility in the three types regions. For your convenience, we present this section as below:

Limited by space, Table 1 only shows the results of TM over five years. During the study period, TM increased from 56~12745 p visitors /km to 382~18865 p visitors /km, with an average annual growth rate between 2.20% and 13.46%. According to the average value of TM (Figure 2), the study areas are divided into the following three types.

(1) “Leading Area”，including East China and North China, ranked first and second in all regions.Their TM increased from 2679.39 and 1884.34 p visitors/km in 2000 to 5859.93 and 5209.94 p visitors/km in 2018. However,their annual average growth rates were 5.07% and 6.43%, respectively, ranking first and second from the bottom in all regions. East China is located on the coast, relying on superior natural conditions and an economic foundation, and its regional transportation system is relatively complete.Therefore, it has formed a number of advantageous tourist resource gathering areas, and has become the main tourist destination of inbound tourists in China, and its mobility has long ranked first in the country. As a political and economic center, Beijing has become a tourist attraction for domestic and inbound tourism with a large number of historical and cultural tourism resources. It also drives the joint development of the tourism industry in North China with the Beijing-Tianjin-Hebei urban agglomeration as the core, making North China the second largest core area of TM after East China.

(2)“Stable Area”,including South China, Southwest China, Central China, and Northeast China, ranked third to sixth in all regions.Their TM increased from 903.57p visitors/km, 695.15p visitors/km, 632.06p visitors/km, 493.33 p visitors/km in 2000 to 2626.11p visitors/km, 2754.97p visitors/km, 2857.88p visitors/km, 2244.68 p visitors/km in 2018.The average annual growth rates were 6.58%, 8.81%, 9.06% and 9.38%, respectively.TM in South China grew rapidly during 2005~2015, while it has gradually slowed down in recent years. This is mainly due to the construction of the early transportation system in South China, which increased tourist mobility. After the basic construction of facilities, the incremental tourist inflows decreased, and the overall growth remained stable. Central China has become one of the core transportation hubs under its location and has driven regional tourism development, becoming a central province in the second echelon of TM. Due to geographical restrictions, Northeast and Southwest China are less connected to the transportation network than coastal areas, resulting in relatively low levels of TM. Northeast China focuses on the development of heavy industry but pays little attention to tertiary industry, and tourism infrastructure construction and resource development are relatively weak, which leads to low TM. There are many mountains in Southwest China, and its early traffic development level lags behind.With the opening of the Chengdu-Chongqing high-speed railway, and Chengdu-Guizhou high-speed railway, and the development of the air transportation industry, the land and air transportation layout in Southwest China is becoming increasingly mature. Southwest China actively developed its resources, and the tourist inflow increased from 145 million (2000) to 2.994 billion (2018), with an average value of TM catching up with that of southern China during 2016~2018.

(3)“Potential Area”, including Northwest China, ranks last in terms of average tourist mobility. Its TM increased from 282.01 p visitors/km in 2000 to 1427.58 p visitors/km in 2018, but its average annual growth rate was 10.01% , ranking first among all regions.As a less developed regions, northwestern China has a poor foundation in economic development and openness to the outside world, and TM has long been at the bottom of the list. Although TM in Northwest China has long been at the bottom of the list, its mobility growth rate leads other regions as tourism infrastructure construction and resource development levels have improved under the active promotion of Western Development policies, the Five-Year Plan, and the Territorial Tourism Strategy.

Third, in order to more intuitively observe the temporal and spatial change characteristics of TM during the study period, we apply the method of natural breaks to classify the 31provinces and apply the standard deviation ellipse to identify the direction of TM in each province. It read as

To more intuitively observe the temporal and spatial change characteristics of TM during the study period, we apply the method of natural breaks to classify the 31 provinces.Natural breaks classes are based on natural groupings inherent in the data. Class breaks are identified that best group similar values and that maximize the differences between classes. The features are divided into classes whose boundaries are set where there are relatively big differences in the data values. The natural breaks classification method is a data classification method designed to determine the best arrangement of values into different classes. This is done by seeking to minimize each class’s average deviation from the class mean, while maximizing each class’s deviation from the means of the other groups（Chen et al.,2013). We divided the 31 provinces into five categories,highest-value area, higher-value area,medium-value area,lower-value area, and lowest-value area, according to the TM in 2000, 2005, 2010, 2015, and 2018.As shown in Figure 3, (1) Shanghai and Beijing have long been in the highest-value area and higher-value area of TM. Tibet, Qinghai, Ningxia, Xinjiang, Inner Mongolia, Gansu, Jilin, Heilongjiang, Hubei, and Hainan have long been in the lowest-value and lower-value areas.(2) Over time, the number of provinces in the highest-value area and the higher-value area increased significantly, from 2 provinces in 2000 to 12 provinces in 2018. The number of provinces in the lowest-value area and lower-value area significantly decreased, from 26 provinces in 2000 to 12 provinces in 2018; the number of provinces in the medium-value area fluctuated randomly, with the fewest 3 in 2000 and the most 13 in 2015.(3) Except for Shanxi, Northwest China has been in the lowest-value area and the lower-value area for a long time; The TM values in Southwest China have changed greatly. Chongqing and Guizhou have jumped from the lower-value area to the higher-value area,and Yunnan has jumped from the low-value area to the medium-value area.Tibet is relatively stable and has been in the lowest-value area for a long time;South China is relatively stable, but the average value TM in Guangxi has changed greatly, jumping from the lower-value area to the higher-value area;The average TM in Central China has been in the low-value area for a long time.Central China is also relatively stable, and its average of TM has long been located in the lower-value area and the medium-value area. With the exception of Shanghai, which has always been in the highest-value area, the initial value of TM in other provinces in East China has jumped upward. In the Northeast, Liaoning's TM has always been in a leading position, and it has gradually transitioned from a lower-value area to a higher-value area.However,Jilin and Heilongjiang have always been in the lowest-value area and the lower-value area, respectively. Changes in TM in North China are diverse. Beijing has long been located in the highest-value area and higher value area. Inner Mongolia has been in the lowest-value area for a long time. Hebei is in the lower-value area most of the time.Tianjin and Shanxi changed greatly, and finally jumped to the highest-value area and the higher-value area, respectively.

We use the standard deviation ellipse to identify the direction of TM in each province. As shown in Figure 3, the lengths of the minor semiaxis and major semiaxis of the ellipse increased significantly.The growth of the short semiaxis reveals that the degree of dispersion of TM in China's provinces is gradually increasing. This result is basically consistent with the previous analysis conclusions that TM in some provinces shows a more obvious transition trend, which makes the overall dispersion of TM increase.

Fig.3 The spatiotemporal pattern and direction distribution of provincial TM

Response 4: Thank you very much for your insightful comment that helps us to improve this manuscript.

First, there are slight differences in how we describe the estimate result of each influencing factor. This is because some factors have a V-shaped trend of rising first and then falling, or first falling and then rising. However, some other factors have been maintaining the same trend of change, rising or falling. Therefore, we describe the former in more detail. Following your suggestion, we have marked the period next to the description of each value for ease of reading.

Second, we think it is inaccurate to use the expression V-shaped or inverted V-shaped to represent the trend of rising first and then falling.We have revised the relevant content and provided a more detailed explanation of the trend of each variable in section 4.3 and section 5.2.

Third, we change the define “host-guest interaction effect” to “Reception Effects” according your suggestion 5. Our results showed a negative effect of reception effects on TEG. Since the resident population changes less in a period of time, it means that when the value of the Reception is larger, the actual number of tourists is less.Therefore, the Reception is an inverse indicator, that is, it has a negative impact on TEG.We give more explanation about the results in section 5.2, which reads as

Reception and : The has a negative impact on TEG.Zuo and Huang (2017) used the ratio of tourist arrivals to the permanent resident population to characterize tourism specialization in a study evaluating China's tourism-oriented economic growth. Before reaching the inflection point of 30.34 (that is, the tourism reception effect value is 0.03), this indicator has a significant positive impact on TEG. From 2000 to 2018, the tourism reception effect value dropped from 1.47 to 0.11, still less than 0.03. Therefore, the results of our study also partially confirm the research of Zuo and Huang (2017). While expanding the scale of tourists, various regions should also pay attention to the "inflection point" of the Reception value. When the inflection point is reached, the larger the scale of tourists is, the smaller the contribution to the TEG. However, the ratio of regional population to tourist decreases from 1.47 to 0.11 during the period from 2000 to 2018, indicating that not only the number of tourists should be taken into account, but also the quality of the tourism and the per capita tourism consumption should be attached importance to the TEG. The is relatively stable, among which the southwest and northwest China have the most significant negative contribution to the TEG, indicating that the growth rate of the number of tourists received in the above regions is higher than that of other regions.

Fourth, we renamed the description in the bottom of the table 8 to “Overall contribution through 2000~2018”.

Fifth, we explain the reason for “the in Northeast China shows a trend of "rising and falling" changes.” in more details. It reads as

In contrast to the regions mentioned above, the in Northeast China shows a trend of "rising and falling" changes.From 2010 to 2015, the contribution of TM to TEG in Northeast China declined and was negative. The main reason is the overall decline of the regional economy in the Northeast region at this stage. In 2014 and 2015, the GDP growth rates of Northeast China were 4.23% and -0.84%, respectively, ranking second and last among the seven regions in China during the same period. .At the same time, the Northeast region began to carry out statistical "squeeze water" at this stage, which caused obvious fluctuations in the scale of tourists. Therefore, the downturn in the regional economic environment and stricter tourism statistics have negatively affected the contribution of tourism mobility to tourism economic growth. However, since 2016, China has put forward the " all-for-one tourism" policy. Provinces began to pay more attention to the role of tourism in regional economic growth. All-for-one tourism policies and new management systems have led to the continuous improvement of TM in Northeast China from 2015 to 2018, and the contribution to TEG has increased significantly compared with 2010-2015.

Response 5: Thank you very much for your detailed comment. First, we revised the “tourist sources” to “tourist origins” in Figure1.Second, we change the variable name “host-guest interactions effects” to “the reception effects”, which is abbreviated as Reception. Third, we give more explanation for the negative effects of Reception on TEG.We have shown the content in the response 4.

Response 6: Thank you very much for your detailed comment. We moved the paragraph you mentioned to the end of the Conclusion.

Response 7: Thank you very much for your careful reading and reminder. We are sorry for the typo. We have corrected this error and checked for typos throughout the paper again.

Reviewer #2:

oIt would be helpful to briefly explain the connections between tourism mobility (TM) and tourism economic growth (TEG) from the geospatial perspective early in the paper and return to this when discussing the policy implications of the research findings.

oAlthough the authors have given some explanation of the current situation of economic development in China, it is currently insufficient. More context is needed regarding the present situation of the tourism industry, TM, and TEG.

oThe research question(s) and main hypothesis or hypotheses should be included in this section.

oThe second and third paragraphs of this section should be restructured as a new, separate literature review section. The authors are encouraged to include more previous studies and discuss them in greater depth in the literature review, as the literature reviewed in the original version of the manuscript is insufficient.

Response a: Thank you very much for your insightful comment that helps us to improve this manuscript. We add the section “Literature Review” and improve the manuscript in introduction and literature review, respectively:

(1)We rewrite the introduction to address those question you mentioned above.We explain the connections between tourism mobility and tourism economic growth in the second and fourth paragraphs. We also add the explanation of the current situation of tourism in China in the third paragraph. In the 4-7 paragraphs, we give the research questions, research methods, and main hypothesis and research contributions. The introduction reads as:

In recent years, the tourism industry has maintained rapid development. By 2019, the total number of global tourist trips exceeded 12.3 billion, an increase of 4.6% over the previous year. The total global tourism revenue was US$5.8 trillion, equivalent to 6.7% of global GDP (World Tourism Economy Trends Report (2020)).Tourism has made important contributions to economic growth by increasing employment, improving infrastructure, and accumulating foreign exchange earnings for destinations(Li, et al, 2018).Due to the impact of COVID-19, People's travel is restricted. The total number of international tourists in 2021 decreased by 72% compared with 2019, and international tourism consumption dropped by nearly half compared with 2019 (UNWTO, 2022).

The above facts remind us that mobility has become an essential feature of tourism activities(Szivas et al., 2003; Liu and Wall, 2006). Tourist from origins to desinations resulting in a series of mobility of information, material, and capital. These mobilities have a great influence on tourist destinations (Urry, 2003; Cárdenas - García et al, 2013;Hannam et al., 2014; Kim et al., 2021).If tourism mobility(TM) stagnates, tourist attractions, reception facilities and transportation facilities built for tourists will be idle. Tourism workers will lose their jobs and tourism economic growth(TEG) will also stagnate.Therefore, studying the impact of TM is necessary and important.

As one of the important tourist destinations in the world, China's domestic tourism and inbound tourism are developing rapidly. In 2019, the total contribution of China's tourism industry to GDP reached 10.94 trillion yuan, accounting for 11.05% of the total GDP, exceeding the proportion of international tourism in global GDP. A total of 28.25 million people were directly employed in tourism, and 51.62 million people were indirectly employed in tourism. The total employment in tourism accounts for 10.31% of the total employment population in the country (Ministry of Culture and Tourism of China, 2020). However, due to the impact of the COVID-19, the development level of China's tourism industry has not recovered to the level of 2019. In 2021, the total number of domestic tourists in China was 3.246 billion, which is only 54% of that in 2019, and directly leads to a total tourism revenue of 2.92 trillion yuan, which is only 51% of that in 2019.This shows that TM is more important to China's tourism industry.Therefore, we decide to focus on the TM in this study and take China as the research sample.

The top priority of this study is to obtain the right measurement of TM.Transportation infrastructure is an important carrier for the exchange of factors in tourism. Existing studies have confirmed that transportation is a key factor in promoting TEG (Prideaux, 2005; Wang et al., 2012; Massidda and Etzo, 2012).The establishment of the transportation system has an obvious effect on improving the accessibility of tourist destinations and promoting the inflow of the tourist population (Li et al., 2019). However, most existing studies only take tourist arrivals to characterize TM (Pearce, 1979; Gunn, 1988 ; Khadaroo and Seetanah, 2008; Chi, 2014; Liu et al., 2016; Zhang et al.,2019;Saayman and de klerk, 2019). They ignore that the transportation infrastructure is also an important factor affecting the TEG.Therefore, this study redefines TM, which considers both transport infrastructure and tourist arrivals.

Another important purpose of this study is to explore the effect of TM on TEG. Existing literature analyzes the links between TM and international trade (Keum, 2010; Morley, 2014) or focuses on the relationship between economic growth (Kadir and Karim, 2012; Nuno and Muhammad, 2016). However, less literature has focused on the relationship between TM and TEG.There are two possible reasons for the lack of attention. First, the positive and significant impact of the tourist arrivals and TEG no longer needs to be verified. It is common sense that the more tourists the destination receives, the higher the tourism income. Second, tourist arrivals, as a single indicator to measure TM, is obviously able to affect the TEG. Our measurement of the TM concludes both transport infrastructure and tourist arrivals in this study. Therefore, we decide to explore the contribution of TM to the TEG based on the new measurement for TM.

We first use econometric methods to test whether there is a significant impact of TM on TEG. Considering the positive impact of transport infrastructure on China's TEG (Zhang et al, 2020), we hypothesize that TM has a positive impact on TEG.Previous studies have also shown that the spatial spillover effect of tourism may significantly affect the TEG (Yang et al.,2012; Yang and Fik,2014;Yuming, 2014). Therefore, we further apply the spatial Durbin model to test the impact of TM on TEG.

Moreover, we also use the LMDI (Logarithmic Mean Divisia Index) method to further analyze the contribution of TM to TEG in more detail. The LMDI method is often used to study environmental issues such as energy consumption and carbon emissions (Colinet et al, 2015; Chong et al, 2017). In the field of tourism research, the LMDI method is mostly used to decompose tourism carbon emissions or energy consumption (Pablo-Romero et al, 2021; Zha et al, 2021). There are few studies using the LMDI to analyze TEG. Therefore, we further use the LMDI method to decompose TEG into five influencing factors including the tourism mobility effects (TM), the cumulative traffic effects (Traffic), the effects of the tertiary industry (Industry), the structural effects of the tourism industry (Structure) and the reception effects (Reception), and examine the contribution of TM to TEG.

Different from previous studies, this study makes two contributions to the literature. First,we introduce the related concepts of fluid mechanics to construct the indicator TM. We also consider the superposition effect of tourist arrivals and transportation infrastructure. This deepens the understanding of TM and promotes the integration of interdisciplinary knowledge.Second, we are the first to examine the impact of TM on TEG using econometric models and the LMDI method. This deepens the understanding of the mechanisms that influence TEG. The results of this study also provide a reference for tourism-related policy makers. Regions wishing to develop tourism can achieve TEG by expanding the size of the source market and promoting the construction of transportation infrastructure.

(2)We add a new section “Literature review” to include more previous studies and discuss them in greater depth. The literature review reads as:

1.Literature review

As the core of tourism activities, TM refers to the mobility of tourists from the origin to the destination, and the stay of tourists in the region (Oppermann, 1995).It is often associated with tourism demand and is measured by tourist arrivals (Song and Witt, 2006).Since the 1970s, many studies have paid attention to the influencing factors and the spatial structure of TM (Pearce, 1979; Gunn, 1988). The existence of regional heterogeneity makes TM affected by many factors, such as infrastructure, income, GDP and cultural distance (Khadaroo and Seetanah, 2008; Chi, 2014; Zhang, 2019). Moreover, it also makes the spatial structure of TM different.Therefore, TM prediction has become one of the research hotspots (Song, 2008). A large body of research has focused on TM forecasting (Saayman and de klerk, 2019), including using a combination and integration of forecasts, using nonlinear methods for forecasting, and extending existing methods to better model the changing nature of tourism data (Saayman and Botha, 2017).The gravity model is an earlier method used to analyze international TM (Durden and Silberman, 1975). Due to its effectiveness in explaining TM (Keum, 2010), gravity models are often used to analyze international tourism service trade. Although the use of gravity models to predict bilateral TM still lacks a corresponding theoretical explanation mechanism, empirical evidence supports the applicability and robustness of gravity models for TM (Morley, 2014). Existing research focuses on examining the movement patterns and spatial structure of international TM in destinations (Lozano and Gutiérrez, 2018), such as the transfer of inbound TM within regions and the influencing factors of inbound TM within destinations (Hwang et al., 2006). There are still few studies on the overall spatial characteristics of TM within destination countries, and the only literature is mainly based on digital footprints or questionnaire data to analyze the spatial structure of TM (Wang et al., 2016; Liu et al., 2021).

Unlike the tourist arrivals indicator, which focuses more on the group flow of people, TM examines a wider range of content, including the flow of people, the flow of materials, the flow of ideas (more intangible thoughts and fantasies), and the flow of technology. (Hannam et al, 2014). Early tourist movement focused more on tourist travel decisions and the resulting movement patterns. Lue et al. (1993) summarized five travel patterns of tourists between destinations. Li et al. (2008) revealed the spatial patterns of TM and tourism propensity in the Asia-Pacific region over the past 10 years. Mcckercher and Lau (2008) took Hong Kong as an example and identified 78 movement patterns and 11 movement styles of TM within the destination. In recent years, with the help of technologies such as GPS, GIS, and RFID, the movement of tourists within scenic spots has attracted attention (Zheng et al, 2017). Research on visitor movement in national parks, theme parks, protected areas, etc. continues to increase (Connell and Page, 2008; Hallo et al, 2012; Smallwood et al, 2011), and explore the influencing factors of visitor movement (Xia et al, 2011), broadening the microscale visitor mobility research content. TM also has economic, social, and cultural impacts on destinations through the movement of tourists. Numerous empirical studies have shown that tourist arrivals have a positive impact on economic growth (Pablo-Romero et al, 2013). Tourism is an important driver of economic growth (Tugcu, 2014). However, some studies have shown that tourist arrivals do not directly lead to economic growth, but promote TEG through regional economic development (Lee and Chien, 2008; Payne and Mervar, 2010; Odhiambo, 2011). The mobility of tourism will also bring about changes in destination transportation facilities. Transportation is not only an important carrier of TM, but also an important part of tourists' travel experience (Hannam et al, 2014). It also has a positive impact on destination company value together with TM (Zhang et al, 2020).

There are many theoretical discussions and empirical studies on the factors influencing TEG. From the perspective of suppliers, resource endowment (Melián-González and García Falcón , 2003; Wang, 2010; Zhu and He, 2019) and environmental quality (Katircioglu et al., 2014; Nitivattananon and Srinonil, 2019; Hamaguchi, 2021; Yong, 2021) are the fundamental factors determining tourism development. Simultaneously, as a typical service industry, human capital and physical capital in the tourism industry (Fahimi et al., 2018; Elsharnouby and Elbanna, 2021) and service level (Lin 2011) will impact tourism economic efficiency. From the perspective of demanders, the rise of per capita income and consumption upgrading continue to drive the transformation in the tourism industry (Feng and Sun, 2016), which in turn leads to an increasing scale of market demand (Fu et al., 2020), which provides the possibility of increasing the foreign exchange earnings, local capital accumulation, and consumption spillovers. From the perspective of supporters, scholars have verified the significant effects of factors on TEG, including the transportation facilities and accessibility (Macchiavelli and Pozzi, 2015; Khan et al., 2017; Zhong et al., 2019; Kanwal, 2020), the basis of the economy and marketization (Dritsakis, 2004), industrial structure (Zuo et al., 2020), public policy (Causevic and Lynch, 2013; Liu et al., 2020; Matteo, 2021), and technological progress (Sigala, 2018).

In summary, the research on TM has paid attention to its impact on the regional economic, but they both ignored the role of TM on TEG.Studies of TEG based on static factors have primarily relied on econometric models (Tu and Zhang, 2020). Although the spatial spillover effects of influencing factors have gradually gained attention, its depth is limited and fails to explore the impact of TM and other related factors on the TEG. TM is becoming central to tourism activities and that understanding the capital mobility of tourism will have implications for tourism development under the new mobility paradigm(Sun et al. ,2016). This study proposes the concept of TM based on the theory of fluid mechanics, explores its impact on TEG, and analyzes the contribution of each influencing factor to TEG.

oA few words or a sentence should be sufficient to describe the spatial weight matrix used in equation 4.

Response b: Thank you very much for your reminder. The matrix we use in equation 4 is a adjacency matrix. We have supplemented it in section 2.2.3.

oThe last paragraph of this section (about the seven regions of the study area) should be moved to the Appendix. Including a map inset of these areas would be helpful here, as well.

Response c: Thank you very much for your constructive comment. First, we moved the content of the seven regions of the study area to the Appendix as your suggestion. Second, we also added a map to more clearly present the geographical distribution of the study area.For your convenience, we present this section as below:

The study area is divided into seven regions according to geographical divisions of China. North China including Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia. Northeast including Heilongjiang, Jilin, Liaoning. East China including Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Shandong, Fujian. Central China including Henan, Hubei, Hunan. South China including Guangdong, Guangxi, Hainan. Southwest China including Chongqing, Sichuan, Guizhou, Yunnan, Tibet. Northwest China including Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang. Figure S shows the map of the seven regions.

Fig. S Map of the seven regions

oIt would be helpful to include a LISA cluster map of to help readers better understand the spatial patterns described here.

Response d: Thank you very much for your constructive comment.We added the section 3.2.2 including LISA cluster map in page 14. For your convenience, we present this section as below:

3.2.2 Local Spatial Autocorrelation Cluster of Tourism Mobility

The global Moran's I cannot reflect the spatial correlation exhibited by local regions or individual provinces. We further use ArcGIS 10.8 to draw the LISA cluster diagram for 2000, 2005, 2010, 2015, and 2018 (Figure 4). The research samples are divided into four types of agglomeration: provinces with high TM are surrounded by provinces with high TM (H-H agglomeration), provinces with high TM are surrounded by provinces with low TM (H-L agglomeration), provinces with low TM are surrounded by provinces with high TM (L-H agglomeration), and provinces with low TM are surrounded by provinces with low TM (L-L agglomeration).

The results show that (1) provinces with H-H aggregation of TM in different periods are relatively stable; L-L and L-H aggregation types are stable but mixed with changes;The H-L aggregation type does not appear, which indicates that there is no "darkness under the light" area for China's provincial TM.Provinces with high TM can improve the TM of weekly provinces to a certain extent.(2) The H-H agglomeration is mainly concentrated in Jiangsu and Zhejiang. These regions are economically developed and have high per capita discretionary income. Moreover, the tourism infrastructure in these regions is more complete than taht in other regions, and the tourist reception scale is also higher, so their TM shows a high local concentration.(3) The L-L agglomeration types are mainly distributed in geographically remote areas such as Qinghai, Tibet, Gansu, and Xinjiang in inland China. Moreover, Xinjiang and Gansu temporarily withdraw from the L-L agglomeration area. The main reason for this pattern is that the transportation infrastructure in the areas above mentioned is relatively underdeveloped. The "space-time compression effect" brought about by the rapid development of China's transportation is not significant.Furthermore, due to the distance from the main tourist source markets, although the TM shows a high growth rate, it is still in the lowest-value area and the lower-value area for a long time.(4) L-H agglomeration is mainly transferred in Anhui, Shandong and Hebei, and these provinces are located in the “Leading Area”.The average value of TM in the surrounding provinces is generally high, forming a "collapse area" for TM.

Figure 4 LISA Clustering Results of TM

oThis section does not sufficiently analyze the authors’ findings in the context of previous studies. How and why does this study differ from previous research? Relatedly, there are some similarities between this study’s results and those of previous research—what is the unique contribution of this paper? Please emphasize these differences more clearly.

oIt seems that the policy implications of this manuscript are limited. Thus, the authors are encouraged to derive policy implications or recommendations based on their findings. Such implications need not be restricted to Chinese tourism-related policies but may also be applicable to other countries with conditions similar to China. The authors are encouraged to clarify these issues in this section.

oTheoretical and practical contributions should be highlighted more explicitly in this section.

oThe authors are encouraged to expand their discussion of the research limitation(s), as this issue is currently insufficiently explained.

Response e: Thank you very much for your constructive comment. We rewrite the Section “Discussion” and “Conclusion”.

First,we discuss and explain the findings of this study more fully and compare it with the existing literature in Section 5 Discussion. This section is presented as below:

5.1 Regression results of tourism mobility on tourism economic growth

The regression results of the spatial econometric model show that both TM and TA have a significant positive impact on TEG, which verifies the hypothesis we proposed above.This result is also consistent with Wu et al. (2012) and Perboli et al. (2015).In contrast, TP and TH have no significant impact on TEG.However, previous studies have also shown that the spatial spillover effect of tourism can significantly affect the TEG (Yang et al, 2012; Yang and Fik, 2014;Yuming, 2014).Therefore, the impact of TP and TH on TEG remains to be further confirmed.

According to the decomposition results, TM will promote the growth of the local tourism economy but will have a negative impact on neighboring provinces, which indicates a more obvious competition in tourism development among provinces. The increase in mobility in a particular place under a given number of tourists will lead to a diversion of tourists, which will have a negative impact on neighboring regions.Therefore, the tourism industry should also pay attention to the competitive situation in the surrounding areas. The development of tourism focus not only on improving local tourism mobility but also on neighboring areas. Both TP and TH manifest substantial spatial spillover effects. The increase in TP and TH in neighboring areas will produce positive effects, making local areas attach importance to the development of tourism resources and enhancing tourism attraction. TA has a significant positive contribution to TEG, which is consistent with the conclusion of Yang et al, (2012).However, the spatial spillover effects of TA on TEG are not significant, which may be related to the fact that air traffic does not depend on adjacent spaces.

5.2 Analysis of influencing factors’ contribution rate to tourism economic growth

TM and : The in North, Central, Southwest, and South China all show a trend of "falling and rising." It should be noted that the in North China was negative from 2005 to 2010, mainly due to the significant decline in TM in Tianjin and Hebei. The improvement in the transportation infrastructure has a significant impact on TM in Central and Southwest China. The opening of high-speed railroads is a fundamental reason for the fluctuation in . For South China, due to the implementation of the overnight visitor count statistics in the tourism statistics system of Guangdong in 2015~2018, the number of tourists decreased significantly compared to 2010~2015, which in turn led to a significant weakening of the . In contrast to the regions mentioned above, the in Northeast China shows a trend of "rising and falling" changes.From 2010 to 2015, the contribution of TM to TEG in Northeast China declined and was negative. The main reason is the overall decline of the regional economy in the Northeast region at this stage. In 2014 and 2015, the GDP growth rates of Northeast China were 4.23% and -0.84%, respectively, ranking second and last among the seven regions in China during the same period. .At the same time, the Northeast region began to carry out statistical "squeeze water" at this stage, which caused obvious fluctuations in the scale of tourists. Therefore, the downturn in the regional economic environment and stricter tourism statistics have negatively affected the contribution of tourism mobility to tourism economic growth. However, since 2016, China has put forward the " all-for-one tourism" policy. Provinces began to pay more attention to the role of tourism in regional economic growth. All-for-one tourism policies and new management systems have led to the continuous improvement of TM in Northeast China from 2015 to 2018, and the contribution to TEG has increased significantly compared with 2010-2015. The in East China gradually increased from 6.35% to 25.66%, which is related to the opening of the high-speed railroad network in 2010, leading to a significant increase in TM. Northwest China has made the tourism industry a key point for economic growth, and its tourist reception and transportation construction levels have been rapidly improved under the impetus of the all-for-one tourism strategy.

Traffic and : The contribution of to TEG generally shows a downward trend. However, during the same period, Traffic showed a gradual upward trend. In 2018, it increased by 258.72% compared with 2000. Among them, it increased by 35.61% from 2000 to 2005, increased by 91.36% from 2005 to 2010, increased by 24.83% from 2010 to 2015, and increased by 10.73% from 2015 to 2018. From this, it can be judged that there may be a "threshold" in the transportation infrastructure. When the stock of transportation infrastructure in China reaches a certain level, the accumulation of transportation infrastructure cannot improve the contribution to the TEG.The role of transportation infrastructure in influencing tourists' decisions and determining tourist flow cannot be ignored. However, its contribution rate gradually decreases as transportation facilities are gradually improved and regional accessibility differences narrow. The is 14.82% during the examination period, in which the contribution rate of Traffic to TEG in East China (16.15%), Central China (17.44%), Southwest China (15.75%), and Northwest China (15.40%) is higher than that in North, Northeast and South China. This is mainly because Central China and East China are the regions with the largest passenger turnover in China. From 2000 to 2018, the average passenger turnover in Central China and East China was 118.988 billion person-kilometers and 84.595 billion person-kilometers, respectively. The Southwest China and Northwest China are among the regions with the fastest growth in passenger turnover in China, increasing by 3.13 times and 1.77 times respectively, ranking first and second in all regions.

Industry and : The tertiary industry consists of transportation, warehousing and postal industry, information transmission, real estate industry, financial industry, wholesale and retail industry, accommodation and catering industry, etc. Tourism is only a part of it. The per capita added value of the tertiary industry reflects the degree of development of the service industry in various regions, and this indicator has achieved a relatively large increase in terms of changing trends. It increased from 3,653 yuan in 2000 to 34,969 yuan, an increase of 8.57 times. The contribution of to TEG has gradually declined, mainly due to the slowdown in the growth rate of the per capita added value of the tertiary industry. The growth rate dropped from 91.30% in 2000-2005 to 34.35% in 2015-2018. The contribution of to TEG in North China, South China, Northwest China, and Southwest China is consistent with the national trend. Northeast China, East China, and Central China show different trends. Especially in the Northeast region, the contribution of to TEG has dropped significantly.The overall contribution rate of Industry reached 28.18%, indicating that the quality of tertiary industry development has a vital role in promoting TEG. is generally stable in East and Central China and declines significantly in Northeast China, which may be related to the deceleration of tertiary industry development, as the data show that the added-value of tertiary industry per capita in Liaoning, Heilongjiang, and Jilin increased by 93.04%, 75.15% and 90.43% from 2010 to 2015, while it only grew by 0.63%, 39.88% and 23.18% from 2015 to 2018.Central China was inconsistent with the overall national trend from 2005 to 2010. This is mainly due to the slow increase in the per capita added value of the tertiary industry during this period, ranking last in all regions. During this period, the industrial structure of Central China was still dominated by industry. In 2010, the average industrial added value accounted for 56.37% of GDP, the highest in all regions of the country. East China was inconsistent with the overall national trend in 2015-2018. The main reason is that the proportion of the tertiary industry in Fujian and Jiangxi in the region has not exceeded 50%, and there is a large room for optimization and improvement of the industrial structure. Therefore, the growth rate of the added value of the tertiary industry per capita exceeds the previous stage, and the contribution of to TEG is still rising.

Structure and : The share of tertiary industry in tourism in Beijing and Tianjin increased significantly from 2010 to 2018 compared to 2000, leading to the rapid growth of in North China. The in Northeast China was -3.96% from 2005 to 2010, mainly since the growth rate of tertiary industry in Heilongjiang and Liaoning lagged behind that of the tourism industry. The in East, Central, and Southwest China is relatively stable, indicating that tourism and tertiary industry maintain a coordinated development. The in South China has achieved a shift from negative to positive growth. As the economic volume of Guangdong accounts for a large proportion in South China and the growth rate of tourism significantly lags behind the development rate of the tertiary industry, it leads to a low ∆S in South China from 2000 to 2010. The opening of high-speed rail provides new opportunities for tourism development, and the ∆S in South China gradually increased to 14.38% and 10.73% in 2010~2018. The ∆S in Northwest China has been increasing, which suggests that the tourism economy is the primary driver of tertiary industry growth. The continuous growth of the contribution to TEG is partially consistent with the findings of Chang et al. (2009), De Vita and Kyaw (2016), and Zuo and Huang (2017). The higher Structure is, the greater the contribution of to TEG. However, the literature above mentioned also pointed out that has a turning point. For example, Zuo and Huang (2017) found that this value in China is 8.25%.

Second,we give more policy implications in section conclusion based on our findings. The revised part read as

Based on our findings above, we draw the following policy implications. To improve TEG, late-developing regions should improve TM by building large-scale tourism transportation infrastructure, promoting destination marketing to attract tourists, and paying attention to the possible negative effects of increased TM in neighboring regions.At the same time, the improvement of TM should be emphasized at different stages. The threshold effect of tourism transportation infrastructure should also be fully considered. After the transportation infrastructure reaches a certain stock, its contribution to TEG will decrease. At this time, expanding the scale of tourists should become the main tourism development policy.

Third, we highlight the theoretical and practical contributions more explicitly in section conclusion.Specifically, it read as

Fourth, we expand the discussion of the research limitations. For your convenience, we present the paragraphs as below:

There are still some limitations in this study. It is difficult to directly collect data on the inflow and outflow of tourist between certain provinces. Therefore, we only select inflow of tourists as the primary data and do not consider the influence of the tourists’ outflow on TM. In fact, increased transport accessibility will not only expand the inflow of tourists but also affect the outflow of tourists. Therefore, the superposition effect of traffic and tourist inflow/outflow should be considered comprehensively to improve the scientific rationality of TM measurement. This study lacks comparative studies across multiple countries. The research in our study may show differentiated findings for developed or less developed countries.When constructing the econometric model, we mainly consider TM as the core explanatory variable, and only select human input and capital input, and air traffic related to traffic as control variables from the perspective of the economic growth model. In the future, the theory and practice of TM will be further explored with multivariate data to form a more rigorous and systematic cognitive framework.

We appreciate the detailed and constructive comments provided by the editor and all the reviewers, which have improved and refined this study. Thank you very much for consideration our manuscript.

Best regards,

The authors

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Glossary of tourism terms

Tourism is a social, cultural and economic phenomenon which entails the movement of people to countries or places outside their usual environment for personal or business/professional purposes. These people are called visitors (which may be either tourists or excursionists; residents or non-residents) and tourism has to do with their activities, some of which involve tourism expenditure.

A B C D E F G H I J K L M N O P Q R S T U V W Y Z

Activity/activities : In tourism statistics, the term activities represent the actions and behaviors of people in preparation for and during a trip in their capacity as consumers ( IRTS 2008, 1.2 ).

Activity (principal): The principal activity of a producer unit is the activity whose value added exceeds that of any other activity carried out within the same unit ( SNA 2008, 5.8 ).

Activity (productive): The (productive) activity carried out by a statistical unit is the type of production in which it engages. It has to be understood as a process, i.e. the combination of actions that result in a certain set of products. The classification of productive activities is determined by their principal output.

Administrative data : Administrative data is the set of units and data derived from an administrative source. This is a data holding information collected and maintained for the purpose of implementing one or more administrative regulations.

Adventure tourism : Adventure tourism is a type of tourism which usually takes place in destinations with specific geographic features and landscape and tends to be associated with a physical activity, cultural exchange, interaction and engagement with nature. This experience may involve some kind of real or perceived risk and may require significant physical and/or mental effort. Adventure tourism generally includes outdoor activities such as mountaineering, trekking, bungee jumping, rock climbing, rafting, canoeing, kayaking, canyoning, mountain biking, bush walking, scuba diving. Likewise, some indoor adventure tourism activities may also be practiced.

Aggregated data : The result of transforming unit level data into quantitative measures for a set of characteristics of a population.

Aggregation : A process that transforms microdata into aggregate-level information by using an aggregation function such as count, sum average, standard deviation, etc.

Analytical unit : Entity created by statisticians, by splitting or combining observation units with the help of estimations and imputations.

Balance of payments : The balance of payments is a statistical statement that summarizes transactions between residents and non-residents during a period. It consists of the goods and services account, the primary income account, the secondary income account, the capital account, and the financial account ( BPM6, 2.12 ).

Bias : An effect which deprives a statistical result of representativeness by systematically distorting it, as distinct from a random error which may distort on any one occasion but balances out on the average.

Business and professional purpose (of a tourism trip): The business and professional purpose of a tourism trip includes the activities of the self-employed and employees, as long as they do not correspond to an implicit or explicit employer-employee relationship with a resident producer in the country or place visited, those of investors, businessmen, etc. ( IRTS 2008, 3.17.2 ).

Business tourism : Business tourism is a type of tourism activity in which visitors travel for a specific professional and/or business purpose to a place outside their workplace and residence with the aim of attending a meeting, an activity or an event. The key components of business tourism are meetings, incentives, conventions and exhibitions. The term "meetings industry" within the context of business tourism recognizes the industrial nature of such activities. Business tourism can be combined with any other tourism type during the same trip.

Business visitor : A business visitor is a visitor whose main purpose for a tourism trip corresponds to the business and professional category of purpose ( IRTS 2008, 3.17.2 ).

Central Product Classification : The Central Product Classification (CPC) constitutes a complete product classification covering goods and services. It is intended to serve as an international standard for assembling and tabulating all kinds of data requiring product detail, including industrial production, national accounts, service industries, domestic and foreign commodity trade, international trade in services, balance of payments, consumption and price statistics. Other basic aims are to provide a framework for international comparison and promote harmonization of various types of statistics dealing with goods and services.

Census : A census is the complete enumeration of a population or groups at a point in time with respect to well defined characteristics: for example, Population, Production, Traffic on particular roads.

Coastal, maritime and inland water tourism : Coastal tourism refers to land-based tourism activities such as swimming, surfing, sunbathing and other coastal leisure, recreation and sports activities which take place on the shore of a sea, lake or river. Proximity to the coast is also a condition for services and facilities that support coastal tourism. Maritime tourism refers to sea-based activities such as cruising, yachting, boating and nautical sports and includes their respective land-based services and infrastructure. Inland water tourism refers to tourism activities such as cruising, yachting, boating and nautical sports which take place in aquatic- influenced environments located within land boundaries and include lakes, rivers, ponds, streams, groundwater, springs, cave waters and others traditionally grouped as inland wetlands.

Coherence : Adequacy of statistics to be combined in different ways and for various uses.

Competitiveness of a tourism destination : The competitiveness of a tourism destination is the ability of the destination to use its natural, cultural, human, man-made and capital resources efficiently to develop and deliver quality, innovative, ethical and attractive tourism products and services in order to achieve a sustainable growth within its overall vision and strategic goals, increase the added value of the tourism sector, improve and diversify its market components and optimize its attractiveness and benefits both for visitors and the local community in a sustainable perspective.

Consistency : Logical and numerical coherence.

Country of reference : The country of reference refers to the country for which the measurement is done. ( IRTS 2008, 2.15 ).

Country of residence : The country of residence of a household is determined according to the centre of predominant economic interest of its members. If a person resides (or intends to reside) for more than one year in a given country and has there his/her centre of economic interest (for example, where the predominant amount of time is spent), he/she is considered as a resident of this country.

Country-specific tourism characteristic products and activities : To be determined by each country by applying the criteria of IRTS 2008, 5.10 in their own context; for these products, the activities producing them will be considered as tourism characteristic, and the industries in which the principal activity is tourism-characteristic will be called tourism industries ( IRTS 2008, 5.16 ).

Cultural tourism : Cultural tourism is a type of tourism activity in which the visitor's essential motivation is to learn, discover, experience and consume the tangible and intangible cultural attractions/products in a tourism destination. These attractions/products relate to a set of distinctive material, intellectual, spiritual and emotional features of a society that encompasses arts and architecture, historical and cultural heritage, culinary heritage, literature, music, creative industries and the living cultures with their lifestyles, value systems, beliefs and traditions.

Data checking : Activity whereby the correctness conditions of the data are verified. It also includes the specification of the type of error or of the condition not met, and the qualification of the data and their division into "error-free data" and "erroneous data".

Data collection : Systematic process of gathering data for official statistics.

Data compilation : Operations performed on data to derive new information according to a given set of rules.

Data confrontation : The process of comparing data that has generally been derived from different surveys or other sources, especially those of different frequencies, in order to assess and possibly improve their coherency, and identify the reasons for any differences.

Data processing : Data processing is the operation performed on data by the organization, institute, agency, etc., responsible for undertaking the collection, tabulation, manipulation and preparation of data and metadata output.

Data reconciliation : The process of adjusting data derived from two different sources to remove, or at least reduce, the impact of differences identified.

Destination (main destination of a trip): The main destination of a tourism trip is defined as the place visited that is central to the decision to take the trip. See also purpose of a tourism trip ( IRTS 2008, 2.31 ).

Destination management / marketing organization (DMO) : A destination management/marketing organization (DMO) is the leading organizational entity which may encompass the various authorities, stakeholders and professionals and facilitates tourism sector partnerships towards a collective destination vision. The governance structures of DMOs vary from a single public authority to a public/ private partnership model with the key role of initiating, coordinating and managing certain activities such as implementation of tourism policies, strategic planning, product development, promotion and marketing and convention bureau activities. The functions of the DMOs may vary from national to regional and local levels depending on the current and potential needs as well as on the decentralization level of public administration. Not every tourism destination has a DMO.

Documentation: Processes and procedures for imputation, weighting, confidentiality and suppression rules, outlier treatment and data capture should be fully documented by the survey provider. Such documentation should be made available to at least the body financing the survey.

Domestic tourism : Domestic tourism comprises the activities of a resident visitor within the country of reference, either as part of a domestic tourism trip or part of an outbound tourism trip ( IRTS 2008, 2.39 ).

Domestic tourism consumption : Domestic tourism consumption is the tourism consumption of a resident visitor within the economy of reference ( TSA:RMF 2008, figure 2.1 ).

Domestic tourism expenditure : Domestic tourism expenditure is the tourism expenditure of a resident visitor within the economy of reference, (IRTS 2008, 4.15(a)).

Domestic tourism trip : A domestic tourism trip is one with a main destination within the country of residence of the visitor (IRTS 2008, 2.32).

Domestic visitor : As a visitor travels within his/her country of residence, he/she is a domestic visitor and his/her activities are part of domestic tourism.

Durable consumer goods : Durable consumer goods are goods that may be used repeatedly or continuously over a period of a year or more, assuming a normal or average rate of physical usage. When acquired by producers, these are considered to be capital goods used for production processes, as is the case of vehicles, computers, etc. When acquired by households, they are considered to be consumer durable goods ( TSA:RMF 2008, 2.39 ). This definition is identical to the definition of SNA 2008, 9.42 : A consumer durable is a goodthat may be used for purposes of consumption repeatedly or continuously over a period of a year or more.

Dwellings : Each household has a principal dwelling (sometimes also designated as main or primary home), usually defined with reference to time spent there, whose location defines the country of residence and place of usual residence of this household and of all its members. All other dwellings (owned or leased by the household) are considered secondary dwellings ( IRTS 2008, 2.26 ).

Ecotourism : Ecotourism is a type of nature-based tourism activity in which the visitor's essential motivation is to observe, learn, discover, experience and appreciate biological and cultural diversity with a responsible attitude to protect the integrity of the ecosystem and enhance the well-being of the local community. Ecotourism increases awareness towards the conservation of biodiversity, natural environment and cultural assets both among locals and the visitors and requires special management processes to minimize the negative impact on the ecosystem.

Economic analysis : Tourism generates directly and indirectly an increase in economic activity in the places visited (and beyond), mainly due to demand for goods and services thatneed to be produced and provided. In the economic analysis of tourism, one may distinguish between tourism's 'economic contribution' which refers to the direct effect of tourism and is measurable by means of the TSA, and tourism's 'economic impact' which is a much broader concept encapsulating the direct, indirect and induced effects of tourism and which must be estimated by applying models. Economic impact studies aim to quantify economic benefits, that is, the net increase in the wealth of residents resulting from tourism, measured in monetary terms, over and above the levels that would prevail in its absence.

Economic territory : The term "economic territory" is a geographical reference and points to the country for which the measurement is done (country of reference) ( IRTS 2008, 2.15 ).

Economically active population : The economically active population or labour force comprises all persons of either sex who furnish the supply of labour for the production of goods and services as defined by the system of national accounts during a specified time-reference period (ILO, Thirteenth ICLS, 6.18).

Economy (of reference): "Economy" (or "economy of reference") is an economic reference defined in the same way as in the balance of payments and in the system of national accounts: it refers to the economic agents that are resident in the country of reference ( IRTS 2008, 2.15 ).

Education tourism : Education tourism covers those types of tourism which have as a primary motivation the tourist's engagement and experience in learning, self-improvement, intellectual growth and skills development. Education Tourism represents a broad range of products and services related to academic studies, skill enhancement holidays, school trips, sports training, career development courses and language courses, among others.

Employees : Employees are all those workers who hold the type of job defined as "paid employment" (ILO, Fifteenth ICLS, pp. 20-22).

Employer-employee relationship : An employer-employee relationship exists when there is an agreement, which may be formal or informal, between an entity and an individual, normally entered into voluntarily by both parties, whereby the individual works for the entity in return for remuneration in cash or in kind ( BPM6, 11.11 ).

Employers : Employers are those workers who, working on their own account with one or more partners, hold the type of job defined as a "self-employment job" and, in this capacity, on a continuous basis (including the reference period) have engaged one or more persons to work for them in their business as "employee(s)" (ILO, Fifteenth ICLS, pp. 20-22).

Employment : Persons in employment are all persons above a specified age who, during a specified brief period, either one week or one day, were in paid employment or self-employment (OECD GST, p. 170).

Employment in tourism industries : Employment in tourism industries may be measured as a count of the persons employed in tourism industries in any of their jobs, as a count of the persons employed in tourism industries in their main job, or as a count of the jobs in tourism industries ( IRTS 2008, 7.9 ).

Enterprise : An enterprise is an institutional unit engaged in production of goods and/or services. It may be a corporation, a non-profit institution, or an unincorporated enterprise. Corporate enterprises and non-profit institutions are complete institutional units. An unincorporated enterprise, however, refers to an institutional unit —a household or government unit —only in its capacity as a producer of goods and services (OECD BD4, p. 232)

Establishment : An establishment is an enterprise, or part of an enterprise, that is situated in a single location and in which only a single productive activity is carried out or in which the principal productive activity accounts for most of the value added ( SNA 2008, 5.14 ).

Estimation : Estimation is concerned with inference about the numerical value of unknown population values from incomplete data such as a sample. If a single figure is calculated for each unknown parameter the process is called "point estimation". If an interval is calculated within which the parameter is likely, in some sense, to lie, the process is called "interval estimation".

Exports of goods and services : Exports of goods and services consist of sales, barter, or gifts or grants, of goods and services from residents to non-residents (OECD GST, p. 194)

Frame : A list, map or other specification of the units which define a population to be completely enumerated or sampled.

Forms of tourism : There are three basic forms of tourism: domestic tourism, inbound tourism, and outbound tourism. These can be combined in various ways to derive the following additional forms of tourism: internal tourism, national tourism and international tourism.

Gastronomy tourism : Gastronomy tourism is a type of tourism activity which is characterized by the visitor's experience linked with food and related products and activities while travelling. Along with authentic, traditional, and/or innovative culinary experiences, Gastronomy Tourism may also involve other related activities such as visiting the local producers, participating in food festivals and attending cooking classes. Eno-tourism (wine tourism), as a sub-type of gastronomy tourism, refers to tourism whose purpose is visiting vineyards, wineries, tasting, consuming and/or purchasing wine, often at or near the source.

Goods : Goods are physical, produced objects for which a demand exists, over which ownership rights can be established and whose ownership can be transferred from one institutional unit to another by engaging in transactions on markets ( SNA 2008, p. 623 ).

Gross fixed capital formation : Gross fixed capital formation is defined as the value of institutional units' acquisitions less disposals of fixed assets. Fixed assets are produced assets (such as machinery, equipment, buildings or other structures) that are used repeatedly or continuously in production over several accounting periods (more than one year) ( SNA 2008, 1.52 ).

Gross margin : The gross margin of a provider of reservation services is the difference between the value at which the intermediated service is sold and the value accrued to the provider of reservation services for this intermediated service.

Gross value added : Gross value added is the value of output less the value of intermediate consumption ( TSA:RMF 2008, 3.32 ).

Gross value added of tourism industries : Gross value added of tourism industries (GVATI) is the total gross value added of all establishments belonging to tourism industries, regardless of whether all their output is provided to visitors and the degree of specialization of their production process ( TSA:RMF 2008, 4.86 ).

Grossing up : Activity aimed at transforming, based on statistical methodology, micro-data from samples into aggregate-level information representative of the target population.

Health tourism : Health tourism covers those types of tourism which have as a primary motivation, the contribution to physical, mental and/or spiritual health through medical and wellness-based activities which increase the capacity of individuals to satisfy their own needs and function better as individuals in their environment and society. Health tourism is the umbrella term for the subtypes wellness tourism and medical tourism.

Imputation : Procedure for entering a value for a specific data item where the response is missing or unusable.

Inbound tourism : Inbound tourism comprises the activities of a non-resident visitor within the country of reference on an inbound tourism trip ( IRTS 2008, 2.39 ).

Inbound tourism consumption : Inbound tourism consumption is the tourism consumption of a non-resident visitor within the economy of reference ( TSA:RMF 2008, figure 2.1 ).

Inbound tourism expenditure : Inbound tourism expenditure is the tourism expenditure of a non-resident visitor within the economy of reference ( IRTS 2008, 4.15(b) ).

Innovation in tourism : Innovation in tourism is the introduction of a new or improved component which intends to bring tangible and intangible benefits to tourism stakeholders and the local community, improve the value of the tourism experience and the core competencies of the tourism sector and hence enhance tourism competitiveness and /or sustainability. Innovation in tourism may cover potential areas, such as tourism destinations, tourism products, technology, processes, organizations and business models, skills, architecture, services, tools and/or practices for management, marketing, communication, operation, quality assurance and pricing.

Institutional sector : An aggregation of institutional units on the basis of the type of producer and depending on their principal activity and function, which are considered to be indicative of their economic behaviour.

Institutional unit : The elementary economic decision-making centre characterised by uniformity of behaviour and decision-making autonomy in the exercise of its principal function.

Intermediate consumption : Intermediate consumption consists of the value of the goods and services consumed as inputs by a process of production, excluding fixed assets whose consumption is recorded as consumption of fixed capital ( SNA 2008, 6.213 ).

Internal tourism : Internal tourism comprises domestic tourism and inbound tourism, that is to say, the activities of resident and non-resident visitors within the country of reference as part of domestic or international tourism trips ( IRTS 2008, 2.40(a) ).

Internal tourism consumption : Internal tourism consumption is the tourism consumption of both resident and non-resident visitors within the economy of reference. It is the sum of domestic tourism consumption and inbound tourism consumption ( TSA:RMF 2008, figure 2.1 ).

Internal tourism expenditure : Internal tourism expenditure comprises all tourism expenditure of visitors, both resident and non-resident, within the economy of reference. It is the sum of domestic tourism expenditure and inbound tourism expenditure. It includes acquisition of goods and services imported into the country of reference and sold to visitors. This indicator provides the most comprehensive measurement of tourism expenditure in the economy of reference ( IRTS 2008, 4.20(a) ).

International Standard Industrial Classification of All Economic Activities : The International Standard Industrial Classification of All Economic Activities (ISIC) consists of a coherent and consistent classification structure of economic activities based on a set of internationally agreed concepts, definitions, principles and classification rules. It provides a comprehensive framework within which economic data can be collected and reported in a format that is designed for purposes of economic analysis, decision-taking and policymaking. The classification structure represents a standard format to organize detailed information about the state of an economy according to economic principles and perceptions (ISIC, Rev.4, 1).

International tourism : International tourism comprises inbound tourism and outbound tourism, that is to say, the activities of resident visitors outside the country of reference, either as part of domestic or outbound tourism trips and the activities of non-resident visitors within the country of reference on inbound tourism trips ( IRTS 2008, 2.40(c) ).

International visitor : An international traveller qualifies as an international visitor with respect to the country of reference if: (a) he/she is on a tourism trip and (b) he/she is a non-resident travelling in the country of reference or a resident travelling outside of it ( IRTS 2008, 2.42 ).

Job : The agreement between an employee and the employer defines a job and each self-employed person has a job ( SNA 2008, 19.30 ).

Measurement error : Error in reading, calculating or recording numerical value.

Medical tourism : Medical tourism is a type of tourism activity which involves the use of evidence-based medical healing resources and services (both invasive and non-invasive). This may include diagnosis, treatment, cure, prevention and rehabilitation.

Meetings industry : To highlight purposes relevant to the meetings industry, if a trip's main purpose is business/professional, it can be further subdivided into "attending meetings, conferences or congresses, trade fairs and exhibitions" and "other business and professional purposes". The term meetings industry is preferred by the International Congress and Convention Association (ICCA), Meeting Professionals International (MPI) and Reed Travel over the acronym MICE (Meetings, Incentives, Conferences and Exhibitions) which does not recognize the industrial nature of such activities.

Metadata : Data that defines and describes other data and processes.

MICE : See meetings industry.

Microdata : Non-aggregated observations, or measurements of characteristics of individual units.

Mirror statistics : Mirror statistics are used to conduct bilateral comparisons of two basic measures of a trade flow and are a traditional tool for detecting the causes of asymmetries in statistics (OECD GST, p. 335).

Mountain tourism : Mountain tourism is a type of tourism activity which takes place in a defined and limited geographical space such as hills or mountains with distinctive characteristics and attributes that are inherent to a specific landscape, topography, climate, biodiversity (flora and fauna) and local community. It encompasses a broad range of outdoor leisure and sports activities.

National tourism : National tourism comprises domestic tourism and outbound tourism, that is to say, the activities of resident visitors within and outside the country of reference, either as part of domestic or outbound tourism trips ( IRTS 2008, 2.40(b) ).

National tourism consumption : National tourism consumption is the tourism consumption of resident visitors, within and outside the economy of reference. It is the sum of domestic tourism consumption and outbound tourism consumption ( TSA:RMF 2008, figure 2.1 ).

National tourism expenditure : National tourism expenditure comprises all tourism expenditure of resident visitors within and outside the economy of reference. It is the sum of domestic tourism expenditure and outbound tourism expenditure ( IRTS 2008, 4.20(b) ).

Nationality : The concept of "country of residence" of a traveller is different from that of his/her nationality or citizenship ( IRTS 2008, 2.19 ).

Non-monetary indicators : Data measured in physical or other non-monetary units should not be considered a secondary part of a satellite account. They are essential components, both for the information they provide directly and in order to analyse the monetary data adequately ( SNA 2008, 29.84 ).

Observation unit : entity on which information is received and statistics are compiled.

Outbound tourism : Outbound tourism comprises the activities of a resident visitor outside the country of reference, either as part of an outbound tourism trip or as part of a domestic tourism trip ( IRTS 2008, 2.39(c) ).

Outbound tourism consumption : Outbound tourism consumption is the tourism consumption of a resident visitor outside the economy of reference ( TSA:RMF 2008, figure 2.1 ).

Outbound tourism expenditure : Outbound tourism expenditure is the tourism expenditure of a resident visitor outside the economy of reference ( IRTS 2008, 4.15(c) ).

Output : Output is defined as the goods and services produced by an establishment, a) excluding the value of any goods and services used in an activity for which the establishment does not assume the risk of using the products in production, and b) excluding the value of goods and services consumed by the same establishment except for goods and services used for capital formation (fixed capital or changes in inventories) or own final consumption ( SNA 2008, 6.89 ).

Output (main): The main output of a (productive) activity should be determined by reference to the value added of the goods sold or services rendered (ISIC rev.4, 114).

Pilot survey : The aim of a pilot survey is to test the questionnaire (pertinence of the questions, understanding of questions by those being interviewed, duration of the interview) and to check various potential sources for sampling and non-sampling errors: for instance, the place in which the surveys are carried out and the method used, the identification of any omitted answers and the reason for the omission, problems of communicating in various languages, translation, the mechanics of data collection, the organization of field work, etc.

Place of usual residence : The place of usual residence is the geographical place where the enumerated person usually resides, and is defined by the location of his/her principal dwelling (Principles and recommendations for population and housing censuses of the United Nations, 2.20 to 2.24).

Probability sample : A sample selected by a method based on the theory of probability (random process), that is, by a method involving knowledge of the likelihood of any unit being selected.

Production account : The production account records the activity of producing goods and services as defined within the SNA. Its balancing item, gross value added, is defined as the value of output less the value of intermediate consumption and is a measure of the contribution to GDP made by an individual producer, industry or sector. Gross value added is the source from which the primary incomes of the SNA are generated and is therefore carried forward into the primary distribution of income account. Value added and GDP may also be measured net by deducting consumption of fixed capital, a figure representing the decline in value during the period of the fixed capital used in a production process ( SNA 2008, 1.17 ).

Production : Economic production may be defined as an activity carried out under the control and responsibility of an institutional unit that uses inputs of labour, capital, and goods and services to produce outputs of goods or services ( SNA 2008, 6.24. ).

Purpose of a tourism trip (main): The main purpose of a tourism trip is defined as the purpose in the absence of which the trip would not have taken place ( IRTS 2008, 3.10. ). Classification of tourism trips according to the main purpose refers to nine categories: this typology allows the identification of different subsets of visitors (business visitors, transit visitors, etc.) See also destination of a tourism trip ( IRTS 2008, 3.14 ).

Quality of a tourism destination : Quality of a tourism destination is the result of a process which implies the satisfaction of all tourism product and service needs, requirements and expectations of the consumer at an acceptable price, in conformity with mutually accepted contractual conditions and the implicit underlying factors such as safety and security, hygiene, accessibility, communication, infrastructure and public amenities and services. It also involves aspects of ethics, transparency and respect towards the human, natural and cultural environment. Quality, as one of the key drivers of tourism competitiveness, is also a professional tool for organizational, operational and perception purposes for tourism suppliers.

Questionnaire and Questionnaire design : Questionnaire is a group or sequence of questions designed to elicit information on a subject, or sequence of subjects, from a reporting unit or from another producer of official statistics. Questionnaire design is the design (text, order, and conditions for skipping) of the questions used to obtain the data needed for the survey.

Reference period : The period of time or point in time to which the measured observation is intended to refer.

Relevance : The degree to which statistics meet current and potential users' needs.

Reliability : Closeness of the initial estimated value to the subsequent estimated value.

Reporting unit : Unit that supplies the data for a given survey instance, like a questionnaire or interview. Reporting units may, or may not, be the same as the observation unit.

Residents/non-residents : The residents of a country are individuals whose centre of predominant economic interest is located in its economic territory. For a country, the non-residents are individuals whose centre of predominant economic interest is located outside its economic territory.

Response and non-response : Response and non-response to various elements of a survey entail potential errors.

Response error : Response errors may be defined as those arising from the interviewing process. Such errors may be due to a number of circumstances, such as inadequate concepts or questions; inadequate training; interviewer failures; respondent failures.

Rural tourism : Rural tourism is a type of tourism activity in which the visitor's experience is related to a wide range of products generally linked to nature-based activities, agriculture, rural lifestyle / culture, angling and sightseeing. Rural tourism activities take place in non-urban (rural) areas with the following characteristics:

Low population density;
Landscape and land-use dominated by agriculture and forestry; and
Traditional social structure and lifestyle

Same-day visitor (or excursionist): A visitor (domestic, inbound or outbound) is classified as a tourist (or overnight visitor), if his/her trip includes an overnight stay, or as a same-day visitor (or excursionist) otherwise ( IRTS 2008, 2.13 ).

Sample : A subset of a frame where elements are selected based on a process with a known probability of selection.

Sample survey : A survey which is carried out using a sampling method.

Sampling error : That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.

Satellite accounts : There are two types of satellite accounts, serving two different functions. The first type, sometimes called an internal satellite, takes the full set of accounting rules and conventions of the SNA but focuses on a particular aspect of interest by moving away from the standard classifications and hierarchies. Examples are tourism, coffee production and environmental protection expenditure. The second type, called an external satellite, may add non-economic data or vary some of the accounting conventions or both. It is a particularly suitable way to explore new areas in a research context. An example may be the role of volunteer labour in the economy ( SNA 2008, 29.85 ).

SDMX, Statistical Data and Metadata Exchange : Set of technical standards and content-oriented guidelines, together with an IT architecture and tools, to be used for the efficient exchange and sharing of statistical data and metadata (SDMX).

Seasonal adjustment : Seasonal adjustment is a statistical technique to remove the effects of seasonal calendar influences on a series. Seasonal effects usually reflect the influence of the seasons themselves, either directly or through production series related to them, or social conventions. Other types of calendar variation occur as a result of influences such as number of days in the calendar period, the accounting or recording practices adopted or the incidence of moving holidays.

Self-employment job : Self-employment jobs are those jobs where remuneration is directly dependent upon the profits (or the potential of profits) derived from the goods or services produced.

Self-employed with paid employees : Self-employed with paid employees are classified as employers.

Self-employed without employees : Self-employed without employees are classified as own-account workers.

Services : Services are the result of a production activity that changes the conditions of the consuming units, or facilitates the exchange of products or financial assets. They cannot be traded separately from their production. By the time their production is completed, they must have been provided to the consumers ( SNA 2008, 6.17 ).

Social transfers in kind : A special case of transfers in kind is that of social transfers in kind. These consist of goods and services provided by general government and non-profit institutions serving households (NPISHs) that are delivered to individual households. Health and education services are the prime examples. Rather than provide a specified amount of money to be used to purchase medical and educational services, the services are often provided in kind to make sure that the need for the services is met. (Sometimes the recipient purchases the service and is reimbursed by the insurance or assistance scheme. Such a transaction is still treated as being in kind because the recipient is merely acting as the agent of the insurance scheme) (SNA 2008, 3.83).

Sports tourism : Sports tourism is a type of tourism activity which refers to the travel experience of the tourist who either observes as a spectator or actively participates in a sporting event generally involving commercial and non-commercial activities of a competitive nature.

Standard classification : Classifications that follow prescribed rules and are generally recommended and accepted.

Statistical error : The unknown difference between the retained value and the true value.

Statistical indicator : A data element that represents statistical data for a specified time, place, and other characteristics, and is corrected for at least one dimension (usually size) to allow for meaningful comparisons.

Statistical metadata : Data about statistical data.

Statistical unit : Entity about which information is sought and about which statistics are compiled. Statistical units may be identifiable legal or physical entities or statistical constructs.

Survey : An investigation about the characteristics of a given population by means of collecting data from a sample of that population and estimating their characteristics through the systematic use of statistical methodology.

System of National Accounts : The System of National Accounts (SNA) is the internationally agreed standard set of recommendations on how to compile measures of economic activity in accordance with strict accounting conventions based on economic principles. The recommendations are expressed in terms of a set of concepts, definitions, classifications and accounting rules that comprise the internationally agreed standard for measuring indicators of economic performance. The accounting framework of the SNA allows economic data to be compiled and presented in a format that is designed for purposes of economic analysis, decision-taking and policymaking ( SNA 2008, 1.1 ).

Total tourism internal demand : Total tourism internal demand, is the sum of internal tourism consumption, tourism gross fixed capital formation and tourism collective consumption ( TSA:RMF 2008, 4.114 ). It does not include outbound tourism consumption.

Tourism : Tourism refers to the activity of visitors ( IRTS 2008, 2.9 ).

Tourism characteristic activities : Tourism characteristic activities are the activities that typically produce tourism characteristic products. As the industrial origin of a product (the ISIC industry that produces it) is not a criterion for the aggregation of products within a similar CPC category, there is no strict one-to-one relationship between products and the industries producing them as their principal outputs ( IRTS 2008, 5.11 ).

Tourism characteristic products : Tourism characteristic products are those that satisfy one or both of the following criteria: a) Tourism expenditure on the product should represent a significant share total tourism expenditure (share-of-expenditure/demand condition); b) Tourism expenditure on the product should represent a significant share of the supply of the product in the economy (share-of-supply condition). This criterion implies that the supply of a tourism characteristic product would cease to exist in meaningful quantity in the absence of visitors ( IRTS 2008, 5.10 ).

Tourism connected products : Their significance within tourism analysis for the economy of reference is recognized although their link to tourism is very limited worldwide. Consequently, lists of such products will be country-specific ( IRTS 2008, 5.12 ).

Tourism consumption : Tourism consumption has the same formal definition as tourism expenditure. Nevertheless, the concept of tourism consumption used in the Tourism Satellite Account goes beyond that of tourism expenditure. Besides the amount paid for the acquisition of consumption goods and services, as well as valuables for own use or to give away, for and during tourism trips, which corresponds to monetary transactions (the focus of tourism expenditure), it also includes services associated with vacation accommodation on own account, tourism social transfers in kind and other imputed consumption. These transactions need to be estimated using sources different from information collected directly from the visitors, such as reports on home exchanges, estimations of rents associated with vacation homes, calculations of financial intermediation services indirectly measured (FISIM), etc. ( TSA:RMF 2008, 2.25 ).

Tourism destination : A tourism destination is a physical space with or without administrative and/or analytical boundaries in which a visitor can spend an overnight. It is the cluster (co-location) of products and services, and of activities and experiences along the tourism value chain and a basic unit of analysis of tourism. A destination incorporates various stakeholders and can network to form larger destinations. It is also intangible with its image and identity which may influence its market competitiveness.

Tourism direct gross domestic product : Tourism direct gross domestic product (TDGDP) is the sum of the part of gross value added (at basic prices) generated by all industries in response to internal tourism consumption plus the amount of net taxes on products and imports included within the value of this expenditure at purchasers' prices ( TSA:RMF 2008, 4.96 ).

Tourism direct gross value added : Tourism direct gross value added (TDGVA) is the part of gross value added generated by tourism industries and other industries of the economy that directly serve visitors in response to internal tourism consumption ( TSA:RMF 2008, 4.88 ).

Tourism expenditure : Tourism expenditure refers to the amount paid for the acquisition of consumption goods and services, as well as valuables, for own use or to give away, for and during tourism trips. It includes expenditures by visitors themselves, as well as expenses that are paid for or reimbursed by others ( IRTS 2008, 4.2 ).

Tourism industries : The tourism industries comprise all establishments for which the principal activity is a tourism characteristic activity. Tourism industries (also referred to as tourism activities) are the activities that typically producetourism characteristic products. The term tourism industries is equivalent to tourism characteristic activities and the two terms are sometimes used synonymously in the IRTS 2008, 5.10, 5.11 and figure 5.1 .

Tourism product : A tourism product is a combination of tangible and intangible elements, such as natural, cultural and man-made resources, attractions, facilities, services and activities around a specific center of interest which represents the core of the destination marketing mix and creates an overall visitor experience including emotional aspects for the potential customers. A tourism product is priced and sold through distribution channels and it has a life-cycle.

Tourism ratio : For each variable of supply in the Tourism Satellite Account, the tourism ratiois the ratio between the total value of tourism share and total value of the corresponding variable in the Tourism Satellite Account expressed in percentage form ( TSA:RMF 2008, 4.56 ). (See also Tourism share).

Tourism Satellite Account : The Tourism Satellite Account is the second international standard on tourism statistics (Tourism Satellite Account: Recommended Methodological Framework 2008 –TSA:RMF 2008) that has been developed in order to present economic data relative to tourism within a framework of internal and external consistency with the rest of the statistical system through its link to the System of National Accounts. It is the basic reconciliation framework of tourism statistics. As a statistical tool for the economic accounting of tourism, the TSA can be seen as a set of 10 summary tables, each with their underlying data and representing a different aspect of the economic data relative to tourism: inbound, domestic tourism and outbound tourism expenditure, internal tourism expenditure, production accounts of tourism industries, the Gross Value Added (GVA) and Gross Domestic Product (GDP) attributable to tourism demand, employment, investment, government consumption, and non-monetary indicators.

Tourism Satellite Account aggregates : The compilation of the following aggregates, which represent a set of relevant indicators of the size of tourism in an economy is recommended ( TSA:RMF 2008, 4.81 ):

Internal tourism expenditure;
Internal tourism consumption;
Gross value added of tourism industries (GVATI);
Tourism direct gross value added (TDGVA);
Tourism direct gross domestic product (TDGDP).

Tourism sector : The tourism sector, as contemplated in the TSA, is the cluster of production units in different industries that provide consumption goods and services demanded by visitors. Such industries are called tourism industries because visitor acquisition represents such a significant share of their supply that, in the absence of visitors, their production of these would cease to exist in meaningful quantity.

Tourism share : Tourism share is the share of the corresponding fraction of internal tourism consumption in each component of supply ( TSA:RMF 2008, 4.51 ). For each industry, the tourism share of output (in value), is the sum of the tourism share corresponding to each product component of its output ( TSA:RMF 2008, 4.55 ). (See also Tourism ratio ).

Tourism single-purpose consumer durable goods : Tourism single-purpose consumer durables is a specific category of consumer durable goods that include durable goods that are used exclusively, or almost exclusively, by individuals while on tourism trips ( TSA:RMF 2008 , 2.41 and Annex 5 ).

Tourism trip : Trips taken by visitors are tourism trips ( IRTS 2008, 2.29 ).

Tourist (or overnight visitor): A visitor (domestic, inbound or outbound) is classified as a tourist (or overnight visitor), if his/her trip includes an overnight stay, or as a same-day visitor (or excursionist) otherwise ( IRTS 2008, 2.13 ).

Tourism value chain : The tourism value chain is the sequence of primary and support activities which are strategically fundamental for the performance of the tourism sector. Linked processes such as policy making and integrated planning, product development and packaging, promotion and marketing, distribution and sales and destination operations and services are the key primary activities of the tourism value chain. Support activities involve transport and infrastructure, human resource development, technology and systems development and other complementary goods and services which may not be related to core tourism businesses but have a high impact on the value of tourism.

Travel / traveller : Travel refers to the activity of travellers. A traveller is someone who moves between different geographic locations, for any purpose and any duration ( IRTS 2008, 2.4 ). The visitor is a particular type of traveller and consequently tourism is a subset of travel.

Travel group : A travel group is made up of individuals or travel parties travelling together: examples are people travelling on the same package tour or youngsters attending a summer camp ( IRTS 2008, 3.5 ).

Travel item (in balance of payments): Travel is an item of the goods and services account of the balance of payments: travel credits cover goods and services for own use or to give away acquired from an economy by non-residents during visits to that economy. Travel debits cover goods and services for own use or to give away acquired from other economies by residents during visits to other economies ( BPM6, 10.86 ).

Travel party : A travel party is defined as visitors travelling together on a trip and whose expenditures are pooled ( IRTS 2008, 3.2 ).

Trip : A trip refers to the travel by a person from the time of departure from his/her usual residence until he/she returns: it thus refers to a round trip. Trips taken by visitors are tourism trips.

Urban/city tourism : Urban/city tourism is a type of tourism activity which takes place in an urban space with its inherent attributes characterized by non-agricultural based economy such as administration, manufacturing, trade and services and by being nodal points of transport. Urban/city destinations offer a broad and heterogeneous range of cultural, architectural, technological, social and natural experiences and products for leisure and business.

Usual environment: The usual environment of an individual, a key concept in tourism, is defined as the geographical area (though not necessarily a contiguous one) within which an individual conducts his/her regular life routines ( IRTS 2008, 2.21 ).

Usual residence : The place of usual residence is the geographical place where the enumerated person usually resides (Principles and recommendations for population and housing censuses of the United Nations, 2.16 to 2.18).

Vacation home : A vacation home (sometimes also designated as a holiday home) is a secondary dwelling that is visited by the members of the household mostly for purposes of recreation, vacation or any other form of leisure ( IRTS 2008, 2.27 ).

Valuables : Valuables are produced goods of considerable value that are not used primarily for purposes of production or consumption but are held as stores of value over time ( SNA 2008, 10.13 ).

Visit : A trip is made up of visits to different places.The term "tourism visit" refers to a stay in a place visited during a tourism trip ( IRTS 2008, 2.7 and 2.33 ).

Visitor : A visitor is a traveller taking a trip to a main destination outside his/her usual environment, for less than a year, for any main purpose (business, leisure or other personal purpose) other than to be employed by a resident entity in the country or place visited ( IRTS 2008, 2.9 ). A visitor (domestic, inbound or outbound) is classified as a tourist (or overnight visitor), if his/her trip includes an overnight stay, or as a same-day visitor (or excursionist) otherwise ( IRTS 2008, 2.13 ).

Wellness tourism : Wellness tourism is a type of tourism activity which aims to improve and balance all of the main domains of human life including physical, mental, emotional, occupational, intellectual and spiritual. The primary motivation for the wellness tourist is to engage in preventive, proactive, lifestyle-enhancing activities such as fitness, healthy eating, relaxation, pampering and healing treatments.

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Tourism and Mobility

Mobility has emerged in recent years as a key concept in the social sciences however its application in tourism studies has been relatively limited. The paper provides a framework for placing tourism within the broader context of mobility, and leisure oriented mobility in particular and argues that concepts of mobility provide an opportunity to connect understandings of broader patterns of tourist flows with individual life trajectories. This conference paper introduced a session on tourism and mobility presented at CAUTHE in Brisbane in 2004. It is included here because it is occasionally cited as it is accessible from the Otago repository. If interested in tourism and mobility from this period however i would recommend looking at the paper in Geographical Research (2005) or the papers with Tim Coles and David Duval as these provide a much fuller expose of ideas. Hall, C.M. 2004, Tourism and mobility, pp.212-6 in Creating Tourism Knowledge, 14th International Research Conference of the Council for Australian University Tourism and Hospitality Education, Book of Abstracts, 10-13 February, School of Tourism and Leisure Management, University of Queensland.

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Prior to the COVID-19 pandemic, tourism had permeated all spaces of experience, reaching nearly every country, region, community, and corner of the globe. In recent decades, the meanings, implications, and roles of tourism have also expanded significantly. This article focuses on unconventional tourism mobilities, including same-day visits, which are an important but often neglected part of the tourism system, constantly challenging both scholars and tourism industry stakeholders. Unconventional tourism is an umbrella term that covers most kinds of unregistered or unaccounted tourist mobilities, some of which might not appear to be ‘tourism’ but should be in certain localities and under certain conditions. Given the growth of unregistered tourist flows and unaccounted leisure (or utilitarian) mobilities, there is a need in tourism studies to apply innovative research methods and to reconceptualize the meanings of tourism in different geographical and social contexts. It is expected ...

Jaume Franquesa

The “mobility turn” claims that conceding analytical priority to the study of mobility is the best way to overcome methodological approaches based on fixed and stable categories argued to be unviable in a world that is increasingly mobile. In this paper I argue that the mobility approach, far from reaching this goal, in fact reifies the cleavage between mobility and immobility, relegating immobility to a passive, undertheorized position, and collapsing the complex workings of power, thus foreclosing a dialectical understanding of the contradictory albeit co-produced processes of mobilization and immobilization. Drawing on an ethnographic analysis of the impacts of changing patterns of accumulation of the tourist industry on the urban space of Palma (Majorca, Spain), I suggest a relational approach attentive to the dialectics of mobility and stability, continuity and change.

Current Issues in Tourism

C. Michael Hall

Tourism mobilities are increasing over time and over space. However, while overall growth is clearly of significance, there is a need for a greater interrogation of some of the underlying assumptions made with respect to the nature of tourism mobility in the highly North American and Eurocentric English language tourism literature. Therefore, closer examination of mobilities in the so-called emerging economies that are becoming of growing importance with respect to aggregate tourism consumption and production may shed significant light on our understandings of tourism and associated mobilities. Keywords: mobility; immobility; globalisation; neoliberalism; mobility gap; academic fashion The paper is an introductory commentary to a cluster of papers on non-Western mobilities in Current Issues in Tourism featuring papers: Cohen, S., & Cohen, E. (2015a). A mobilities approach to tourism from emerging world regions. Current Issues in Tourism, 18(1). doi:10.1080/13683500.2014.898617; Cohen, S., & Cohen, E. (2015b). Tourism mobilities from emerging world regions: A response to commentaries. Current Issues in Tourism, 18(1). doi:10.1080/13683500.2014.956705; Coles, T. (2015). Tourism mobilities: Still a current issue in tourism? Current Issues in Tourism, 18(1). doi:10.1080/13683500.2014.937325; Chen, J., & Chang, T. C. (2015). Mobilising tourism research in emerging world regions: Contributions and advances. Current Issues in Tourism, 18(1). doi:10.1080/13683500.2014.932337; Rogerson, C. (2015). Unpacking business tourism mobilities in sub-Saharan Africa. Current Issues in Tourism, 18(1). doi:10.1080/13683500.2014.898619. The version provided here is the page proof. For the authoritative version please consult the journal website.

draft of book chapter, for the authoritative version please see the CABI book (http://bookshop.cabi.org/default.aspx?site=191&page=2633&pid=2258) This chapter focuses on those who are relatively immobile because of economic and other structural and regulative mechanisms such as class, race, gender, and religion that affect the economic and social capital of individuals in society and therefore their life chances, including with respect to tourism related mobility. This chapter primarily focuses on the allocation of economic resources as a central regulative mechanism of tourism mobility but other forms of regulation are noted. The chapter is divided into three main sections. First, a discussion of inequality in relation to concepts of tourism mobilities. Second, the use of national travel survey data to illustrate the way in which mobility is unevenly distributed in society and the strong relationship of those mobilities to economic unevenness. British, EU, American and New Zealand data are used to illustrate how poverty and lack of car access in particular affects leisure mobility, while New Zealand data is also expanded with reference to findings from a qualitative assessment of access to tourism and leisure mobility. Finally, the chapter concludes by stressing the importance of connecting social exclusion to understandings of leisure mobility and how restricted activity space may serve as an indicator of social justice.

Williams, A.M. & Hall, C.M. 2002, Tourism, migration, circulation and mobility: the contingencies of time and place, pp.1-52 in Tourism and Migration: New Relationships Between Production and Consumption, eds C.M. Hall & A.M. Williams, Kluwer, Dordrecht. This is a final draft of the chapter. For the authoritative version please consult the book.

Tourismus und Migration gelten gemeinhin als ganz unterschiedliche, gar einander entgegengesetzte Formen von Mobilität. Die mobility studies jedoch nehmen beide Phänomene gemeinsam in den Blick und können so die oft fließenden Grenzen und vielfältigen Überschneidungen zwischen Migration und Tourismus sichtbar machen. Der Kommentar diskutiert verschiedene Tourismusformen, ihren Zusammenhang mit Migrationsprozessen und thematisiert die Verhandlung nationaler (und anderer) Identitäten on the move. Mit C. Michael Hall and Allan M. Williams plädiert der Text für das Konzept eines Mobilitätskontinuums, das die rechtlich-politische Kategorisierung und Gegenüberstellung verschiedener Mobilitätsformen zu problematisieren erlaubt. Darüber hinaus wird nach dem touristischen Moment in ganz unterschiedlichen Reiseformaten gefragt und eine stärkere Berücksichtigung der performativ-körperlichen Dimension von Mobilität gefordert.

Claudio Milano

#The complexity of the tourist phenomenon requires a wider relational perspective of the multitude of actors and forces involved in tourism as a transnational phenomenon. #Tourism is the product of a confluence of multiple material and imaginary elements that are both subjective and collective and must be placed in specific political, economic, cultural and social contexts. #Tourism is a field of negotiation between the sociopolitical, financial, ecological and cultural relations of a globalisation that is understood as a process, not as a final phase. #Tourist mobility, as it is at the start of this millennium, charts multiple mobilities and directionalities, including new inbound and outbound countries, that is to say, new tourists and new destinations. #The emergence of the Asian tourist class and, in particular the increase in Chinese tourist flows at international level, has grown from 10 million in 2000 and 83 million in 2012 to 109 million Chinese tourists travelling the world in 2014. #The concept of touristification refers to the process by which a historical, social and cultural phenomenon becomes of value to the tourism market. #For tourism and its field of activity to exist there is one necessary condition: security. #Through human mobility the uses and customs of otherness have grown closer and more recognisable. #The change that has had a decisive influence is the emergence of new mobility technologies, real and virtual, that have converted tourism into a movement of greater reach and new global interconnections.

Philippe Duhamel

Stefan Gössling , Scott Cohen , Julia Hibbert

Late modernity in developed nations is characterized by changing social and psychological conditions, including individualization, processes of competition and loneliness. Remaining socially connected is becoming increasingly important. In this situation, travel provides meaning through physical encounters, inclusion in traveller Gemeinschaft based on shared norms, beliefs and interests, and social status in societies increasingly defined by mobilities. As relationships are forged and found in mobility, travel is no longer an option, rather a necessity for sociality, identity construction, affirmation or alteration. Social contexts and the underlying motivations for tourism have changed fundamentally in late modernity: non-tourism has become a threat to self-conceptions. By integrating social and psychological perspectives, this paper expands and deepens existing travel and mobilities discussions to advance the understanding of tourism as a mechanism of social connectedness, and points to implications for future tourism research.

What is Tourist Mobility

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Promoting Sustainable Mobility in Tourist Destinations: Mobility Center 2.0

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Over the past decades, leisure-related mobility in European regions has increased continuously, especially in tourist destinations. New mobility patterns put enormous strain on sustainability issues in tourist regions, which are particularly vulnerable in this regard since the amount of individual mobility often is higher than in non-tourist regions leading to road congestion, seasonal changes of transport demand causing capacity problems, and high level of private car use increasing the need for parking spaces, etc. The rising importance of ecological tourism demands new perspectives of the tourist destinations in establishing new sustainable mobility structures and strategies for supporting regional economic development. Mobility Centers 2.0 are an efficient tool to reduce individual car use and the negative impact of visitor’s travel in tourist regions, as well as to upgrade the quality of the leisure offer and the external image of the region. They can help to strengthen tourist regions as growth poles and improve the economic vitality of the targeted destination.

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Sustainable Urban Mobility–Multimodality as a Chance for Greener Cities: Evidence from Slovakia

Evaluating Urban Mobility Sustainability Through a Set of Indicators: The Case of the City of Lamia, Greece

Sustainable Mobility at the Core of Sustainable Tourism in 6 European Islands

Mobility center
Mobility management
Mobility behaviour
Multi-modal information
Accessibility
Sustainable tourism
Eco-tourism
Cooperation

1 Introduction

“We are millennials and we are looking to have different travel experiences than our parents” says Patrick Quayle, Vice President for international planning at United Airlines.

According to Booking.com ( 2019 ), “over half (55%) of global travellers are being more determined to make sustainable travel choices than they were a year ago, but barriers include a lack of knowledge and available or appealing options when trying to put this into practice”. When it comes to in-destination experiences, over half (52%) of global travelers say they now alter behaviors to be more sustainable while traveling, such as walking, riding a bike, or hiking whenever possible.

Discount airlines and self-booking accommodation platforms have led to increased tourist flows in parallel with rising societal demand for more energy-efficient transportation. Such flows have created increased traffic congestion, less availability of public space due to increased demand for parking areas and higher use and circulation of private cars. The high motorization rate among tourists is a direct consequence of the lack of integrated public transport, tickets fit for visitors, insufficient provision of public transport connections to airports/harbors and leisure attractions (beaches, nature parks, golf courts, historic monuments, etc.), limited network perception, fragmented mobility information (which usually does not have the appropriate design and contents to ease usage), a lack of facilities and safety elements to ride bicycles or even walk in urban and interurban areas.

Changing demographics with an aging population put additional pressure on tourist destinations to create the proper precondition for the regions’ future competitiveness, as the above-mentioned comfort requirements for tourists become more evident with age. The variability of touristic flows in terms of seasonality and space usage are in competition with the local resident supply of transportation and services, the local transport network and the local area demographic and social characteristics.

It is evident that inherent difficulty of small communities for the provision of integrated efficient transport services to residents and tourists often leads to higher individual car usage in order to compensate for the lack of appropriate mobility information and alternatives. Mobility Centers 2.0 are an efficient tool to reduce individual car use and the negative impact of visitor’s travel in tourist regions, as well as to upgrade the quality of the leisure offer and the external image of the region. They can help to strengthen tourist regions as growth poles, which in turn improve the economic vitality of the targeted areas for both citizens and tourists, employees and employers, men and women.

2 Mobility Centers: A One-Stop Shop for Sustainable Mobility

Apart from the choice of transport mode to reach a tourist destination, the smart choice on-site plays a crucial role. Car rental businesses welcome the visitors already at the airport arrival halls and although a growing number of travelers become more and more aware of the necessity of sustainable, climate-friendly mobility, most of the tourist kilometers on-site are still done by car. In fact, the UN World Tourism Organisation ( 2008 ) states that climatically sustainable tourism requires fundamental shifts in consumer behaviour but the most probable explanation for the current mobility behaviour of tourists and visitors is the lack of information in unfamiliar settings.

Moreover, the still ongoing economic crisis and the growing trend of ecological tourism are demanding new integral strategies in a highly competitive field. Tourist destinations require solid and sustainable mobility structures, which can effectively support regional economic growth, as well as the development of safe, livable, and attractive places for all.

This is, where a Mobility Center 2.0–tailor-made for tourist destinations—steps in, and can serve as an information hub for all issues related to sustainable mobility in a region. All over Europe, Mobility Centers serve as information platforms, shaped after a model of a one-stop shop and their core business is multi-modal mobility information and advice (Fig. 1 ). Mobility centers are service facilities that offer users and potential users of public transport, information and services on the subject of mobility across all modes of transport. Ideally, they are the contact point for all questions about mobility.

Mobility centers, established in the frame of SEE MMS project, 2012; South East Europe Transnational Cooperation Programme

The core business of the mobility centers is, in addition to multi-operational timetable information and ticket sales, to advise customers. Organizational services such as vehicle rental, car sharing, the sale of accessories, or the disposition of flexible transportation modes are also offered. Apparently, linking mobility services with leisure, cultural, and tourist information is becoming increasingly important. Tourism and leisure mobility requires a high degree of information about alternatives to the car. Successful examples for linking tourist information and mobility services are, e.g., the mobility center “Mobilito”, Bischofshofen (AT), the “Mobicenter” Wuppertal (GER), or the “M.Punkt” in Wolfsburg (GER).

The special importance of the mobility center arises from the offer described above, combined with the simplicity of the system (one-stop shop) and the customer-friendly opening times. Through their work, mobility centers ensure that users of public transport feel very well looked after and are thus able to satisfy their mobility needs even without their own car. Especially in combination or cooperation with tourist information points, mobility centers can make a major contribution to the environmentally friendly handling of mobility needs in sensitive areas.

The Mobility centers which increasingly popped-up all over Europe differ significantly in terms of the range of services, external image, sponsorship, and financing. There are no specific networks and standards are usually not defined. This leads to a certain confusion of the offer and a lack of transparency for the user. Standardization, however, is to be seen as a prerequisite and is therefore essential, last but not least, to clearly communicate to the customer what services can be expected from a mobility center.

The minimum standards concern:

Offers and work content

Accessibility and opening times

Personnel qualifications

The Mobility Center 2.0 expertise should at least cover:

Information and advice on all modes of transport (public transport, on-demand services, e-mobility, walking, cycling)

Regional/national ticket sales for public transport, taking into account the entire service chain: information—advice—sales

Advice on further alternatives to motorized vehicles

Idea and complaint management

Tourist information (guided tours, day-trips, landmarks) and ticket sales

Organization or booking or information on demand-driven transports

The expansion stages of the mobility center relate to Mobility Management advice for schools or companies, cooperation with accommodation providers, the rental business, the implementation of a mobility shop, or even transport planning activities.

3 The Five Steps of Implementation

When implementing a Mobility Center, it is advisable to follow the five steps approach, as shown in Fig. 2 :

The five steps of implementation of a mobility center

Step 1: Involving all stakeholders from the start.

A diverse group of stakeholders has to be brought together for the implementation of a mobility center. Usually, transport operators, politicians, tourism institutions, residents, and lobby groups do not have a common view, which—on the other hand—is a pre-requisite for a tailor-made mobility center. The floor for successful implementation and long-term operation can be prepared through workshops and discussion rounds with key stakeholders, politicians, and all potentially involved people. This is where the foundation stone for the Mobility Center is laid and a tailor-made concept can jointly be developed, based on best practice in Europe. Besides the services to be offered and the technical needs, also possible cooperations, expectations, strengths, and weaknesses should be investigated and discussed in a joint effort.

Step 2: Definition of tasks and services.

Mobility Centers offer information and service tasks for customers and represent customers’ mobility interests. The target group includes all users of public transport, but also people who do not yet use public transport and who could be considered potential future customers. Other target groups, such as tourism, schools, and businesses, health organizations are increasingly being considered in tailor-made concepts. The following tasks and services are characteristic of Mobility Centers:

General information on the transport network (routes, destinations, timetables, etc.)

Route planning.

Information on tickets and special offers.

Ticket service (sales and/or reservation).

Tourist Information.

Information on car-sharing, car-pooling, e-moblity, and other regional offers.

Bike rental.

Information on bike rental (rental options available and/or tariffs).

Information on pedestrian routes.

Information on Park & Ride spaces.

Complaints management.

Additional services of a Mobility Center could include:

Individual marketing for public transport.

Project/event implementation.

Organization of tourist tours.

Planning and implementation of measures in the field of mobility management.

Transport coordination.

Traffic/urban planning, etc.

Step 3: Set-up of cooperations.

Creating alliances or setup collaborations is the best way to get public support for the Mobility Center and increases the chance of a successful implementation of the project. It is advisable to base the operation of a Mobility Center on several supporting pillars. A minimum standard requirement is the cooperation with transport companies, transport providers, and associations. Further possible cooperation partners are as follows:

Tourist offices/municipalities

Travel agencies and tour operators

Local economy (e.g., shopping centers)

Chambers of Commerce

Employers’ organizations

Lobby groups (e.g., Cyclist Federations)

Organizer of events and meetings

Delivery or rental services, etc.

Step 4: The right location, equipment, and staff.

The location of a Mobility Center is crucial. It should be located in the city center or at a public transport hub to ensure the best possible presence and accessibility.

Another critical point in the implementation and organization of a Mobility Center is the personnel. Regarding personnel requirements, one can at least identify two different profiles, which are a manager on the policy level and the staff on the operational level. The task of mobility managers involves promoting the Mobility Center on the public and political levels. The mobility manager is in charge of the overall coordination, administration, team leading, and daily management of the information hub, as well as for the further development of the mobility center and the implementation of new mobility services. The staff´s tasks on the operational level are provision of information by phone, in writing and personal, as well as advice regarding all issues of mobility and travel awareness, Europe-wide ticket sale, planning of trips, bike rental, CarSharing—advisory service, management of information material, regular participation in team meetings, and general office work.

The standard equipment in a Mobility Center should be up to date, professional, inviting, and customer friendly. Concerning the technical facilities, computer hardware and software, a modern telephone system, a high-speed internet access, e-mail, and fax should be available. In times of digitalization, its services should be accessible 24 h online via Internet.

In case of further requirements for the stage of expansion of the Mobility Center or in order to create higher customer satisfaction, there are almost no limitations. However, a more sophisticated offer is also connected with higher costs and should be considered carefully.

Step 5: Funding and long-term operation.

The financial means are always scarce. Therefore, it is very important to consider the financial aspects in a realistic way and look out for co-operations. With regard to the time perspective for financing mobility centers, a distinction can be made between the introduction stage and the stage of running the Mobility Center in the longer term:

Investments and initial costs

Mobility Centers include the concept study, initial costs for the equipment (hardware, software, office equipment, mobile information facilities, etc.), training costs, and marketing (opening event).

Running Costs

The running costs include the rent and running overhead expenses (maintenance, communication, office equipment), costs for service staff and management, IT costs (Software, etc.), IT maintenance, office equipment, telephone expenses, office supplies, postal charges and marketing and costs for the conception and the production of information and publicity material as well as costs for advertisements and public awareness campaigns/publicity campaigns;

4 Cost/Benefit Analysis

In tourist regions, a Mobility Center has to take into account the seasonal and temporary needs of tourists and visitors. Those additional offers and services may lead to additional operational costs, but in fact, the combination of tourism and mobility provides valuable synergies, not only by building up on existing structures like, e.g., tourist information centers. Such synergies are immanent and can reduce the running costs, which are crucial for a long-term operation.

In terms of the cost/benefits, the traditional economic ROI evaluation methods are not found to be adequate for evaluating Mobility Centers based on a pure business enterprise selling goods and services. If these methods are used, it may not be possible to prove that the Mobility Center is “a profitable company”. On the other hand, the cost/benefit analysis should be used to evaluate the Mobility Center as a “public service provider” and to consider the quantification and monetization of the social benefit in a more complex way.

An action-theoretical model for the choice of means of transport using the example of the mobility center in Graz (Weiss, 2004 ) analyzed the benefits of MOBIL ZENTRAL—the 1st Austrian Mobility Center in Graz, with a sample of 230 people, reliably proved the monetary advantages for the Styrian transport association (from the increased sale of tickets) after using the offers of the Graz Mobility Center. This advantage was put at 459.984 Euros through additional ticket sales per year and an overall change in behaviour, which would not have been realized without the work of the Mobility Center.

Other, rather social benefits (e.g., less noise, less road accidents, higher property values, etc.) were not included in this analysis. Also, it has to be taken into consideration, that the commitment to sustainable mobility in tourism, addresses a continuously growing group of tourists that is willing to travel the region sustainably. This results in growing numbers of overnight stays has the potential to boost the local and regional economy, and should be further analyzed.

5 Conclusions and Recommendations

The role of sustainable transport and mobility in the development of sustainable tourism is increasing, as leisure-time traffic is on the rise, and contributes considerably to greenhouse gas emissions, pollution, and climate change (DestiNet Services, 2019 ).

While arrival, accommodation and departure can be easily planned and booked via online platforms, the information to cover on-site needs and preferences of tourists and visitors, e.g., distribution in time and space, comfort requirements (carrying luggage, families with children, etc.), familiarity with the region’s transport network, number of persons traveling together, purchasing power, language barriers, and special mobility information requirements, etc., is not covered online. The lack of multimodal on-site information is one of the main obstacles for tourists and visitors too, when trying to put their wish for sustainable leisure trips into practice.

Mobility centers providing high-quality information and advisory services on environmentally friendly on-site mobility, as well as promoting environmentally-friendly packages are an efficient tool to change the traveler´s point of view on a destination and can create a competitive advantage for tourist regions.

A main barrier in the implementation and long-term operation of mobility centers is the financing. Since the cost /benefit analysis in the traditional economic sense may not be applicable, the Mobility Center should be seen as a “public service provider” and the financial basis should include as many financing bodies as feasible. Particularly, in regions with high unemployment or a lack of alternative income, it may appear politically justified to use public funds in a way that people are kept in work or are given additional work. An alternative option to secure the operation of a mobility center is to tie it to existing regional-local structures and to integrate it into an overall organizational context.

One important success factor is that the service of mobility center for car-free tourism is communicated under the sustainability aspect, and should thereby focus on the additional benefits of car-free holidays and the additional relaxation that comes along with it. Sustainability as a unique selling proposition cannot create the product value that is worth buying.

If tourists or visitors are able to perceive the personal benefit of a car-free holiday, the journey is already booked and the indirect profitability inevitably will lead to economic success for the tourist region.

Booking.com. (2019). Booking.com Sustainable Travel Report 2019 . Retrieved August 20, 2020 from https://globalnews.booking.com/bookingcom-reveals-key-findings-from-its-2019-sustainable-travel-report .

DestiNet Services (2019). Tourism 2030: Travel, transport & mobility . Retrieved Accessed 20, 2020 from https://destinet.eu/topics/sustainable-transport

UN World Tourism Organisation. (2008) Climate change and tourism . Retrieved August 20, 2020 from https://webunwto.s3-eu-west-1.amazonaws.com/imported_images/30875/climate2008.pdf

Weiss, E. (2004). Ein handlungstheoretisches Modell zur Verkehrsmittelwahl am Beispiel der Mobilitätszentrale in Graz, supported by Univ.- Prof. Dr. Karl Steininger, Inst. VWL, KFU Graz

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Briesner, I. (2022). Promoting Sustainable Mobility in Tourist Destinations: Mobility Center 2.0. In: Tsoutsos, T. (eds) Sustainable Mobility for Island Destinations. Springer, Cham. https://doi.org/10.1007/978-3-030-73715-3_7

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Introduction

Literature review

Theoretical framework, research methods, and data sources

Research methods

Exploratory spatial data analysis.

Econometric model.

LMDI decomposition.

Data sources

Analysis of tourism mobility measurement results

Spatial correlation characteristics of tourism mobility

Local spatial autocorrelation cluster of tourism mobility.

The impact of tourism mobility on tourism economic growth

Traditional panel data model

Spatial panel data model

Decomposition of the influencing factors by LMDI.

Regression results of tourism mobility on tourism economic growth

Analysis of influencing factors’ contribution rate to tourism economic growth

Traffic and ΔT.

Industry and ΔI.

Structure and ΔS.

Reception and ΔR.

Conclusions

Supporting information

S1 File. Research data.

Mengting Yue

Associated Data

Literature review

Theoretical framework, research methods, and data sources

Research methods

Exploratory spatial data analysis

Econometric model

LMDI decomposition

Data sources

Analysis of tourism mobility measurement results

Spatial correlation characteristics of tourism mobility

Local spatial autocorrelation cluster of tourism mobility

The impact of tourism mobility on tourism economic growth

Traditional panel data model

Spatial panel data model

Decomposition of the influencing factors by LMDI

Regression results of tourism mobility on tourism economic growth

Analysis of influencing factors’ contribution rate to tourism economic growth

Traffic and ΔT

Industry and ΔI

Structure and ΔS

Reception and ΔR

Conclusions

Funding Statement

Data Availability

Decision Letter 0

Author response to Decision Letter 0

Decision Letter 1

Acceptance letter

UN Tourism | Bringing the world closer

Glossary of tourism terms

Tourism and Mobility

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Promoting Sustainable Mobility in Tourist Destinations: Mobility Center 2.0

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Sustainable Urban Mobility–Multimodality as a Chance for Greener Cities: Evidence from Slovakia

Evaluating Urban Mobility Sustainability Through a Set of Indicators: The Case of the City of Lamia, Greece

Sustainable Mobility at the Core of Sustainable Tourism in 6 European Islands

1 Introduction

2 Mobility Centers: A One-Stop Shop for Sustainable Mobility

3 The Five Steps of Implementation

4 Cost/Benefit Analysis

5 Conclusions and Recommendations

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