economic importance of transport and tourism

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Tourism’s Importance for Growth Highlighted in World Economic Outlook Report

• All Regions
• 10 Nov 2023

Tourism has again been identified as a key driver of economic recovery and growth in a new report by the International Monetary Fund (IMF). With UNWTO data pointing to a return to 95% of pre-pandemic tourist numbers by the end of the year in the best case scenario, the IMF report outlines the positive impact the sector’s rapid recovery will have on certain economies worldwide.

According to the World Economic Outlook (WEO) Report , the global economy will grow an estimated 3.0% in 2023 and 2.9% in 2024. While this is higher than previous forecasts, it is nevertheless below the 3.5% rate of growth recorded in 2022, pointing to the continued impacts of the pandemic and Russia's invasion of Ukraine, and from the cost-of-living crisis.

Tourism key sector for growth

The WEO report analyses economic growth in every global region, connecting performance with key sectors, including tourism. Notably, those economies with "large travel and tourism sectors" show strong economic resilience and robust levels of economic activity. More specifically, countries where tourism represents a high percentage of GDP   have recorded faster recovery from the impacts of the pandemic in comparison to economies where tourism is not a significant sector.

As the report Foreword notes: "Strong demand for services has supported service-oriented economies—including important tourism destinations such as France and Spain".

Looking Ahead

The latest outlook from the IMF comes on the back of UNWTO's most recent analysis of the prospects for tourism, at the global and regional levels. Pending the release of the November 2023 World Tourism Barometer , international tourism is on track to reach 80% to 95% of pre-pandemic levels in 2023. Prospects for September-December 2023 point to continued recovery, driven by the still pent-up demand and increased air connectivity particularly in Asia and the Pacific where recovery is still subdued.

Related links

• Download the News Release on PDF
• UNWTO World Tourism Barometer
• IMF World Economic Outlook

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The Geography of Transport Systems

The spatial organization of transportation and mobility

3.1 – Transportation and Economic Development

Authors: dr. jean-paul rodrigue and dr. theo notteboom.

The development of transportation systems is embedded within the scale and context in which they take place, from the local to the global and from environmental, historical, technological, and economic perspectives.

1. The Economic Importance of Transportation

Development can be defined as improving the welfare of a society through appropriate social, political, and economic conditions. The expected outcomes are quantitative and qualitative improvements in human capital (e.g. income and education levels) as well as physical capital such as infrastructures (utilities, transport, telecommunications).

The development of transportation systems takes place in a socioeconomic context. While development policies and strategies focus on physical capital, recent years have seen a better balance by including human capital issues. Irrespective of the relative importance of physical versus human capital, development cannot occur without their respective interactions, as infrastructures cannot remain effective without proper management, operations, and maintenance. At the same time, economic activities cannot take place without an infrastructure base. The highly transactional and service-oriented functions of many transport activities underline the complex relationship between its physical and human capital needs. For instance, effective logistics rely on infrastructures and managerial expertise.

Because of its intensive use of infrastructures , the transport sector is an important component of the economy and a common tool used for development. This is even more so in a global economy where economic opportunities have been increasingly related to the mobility of people and freight, including information and communication technologies. A relation between the quantity and quality of transport infrastructure and the level of economic development is apparent. High-density transport infrastructure and highly connected networks are commonly associated with high levels of development. When transport systems are efficient, they provide economic and social opportunities and benefits that result in positive multiplier effects, such as better accessibility to markets, employment, and additional investments. When transport systems are deficient in terms of capacity or reliability, they can have an economic cost, such as  reduced or missed opportunities and lower quality of life .

At the aggregate level, efficient transportation reduces costs in many economic sectors , while inefficient transportation increases these costs. Besides, the impacts of transportation are not always intended and can have unforeseen or unintended consequences . For instance, congestion is often an unintended consequence of providing users with free or low-cost transport infrastructure. However, congestion also indicates a growing economy where capacity and infrastructure have difficulties keeping up with the rising mobility demands. Transport carries an important social and environmental load, which cannot be neglected.

Assessing the economic importance of transportation requires the categorization of the types of impacts it conveys. These involve core (the physical characteristics of transportation), operational and geographical dimensions:

• Core . The most fundamental impacts of transportation-related to the physical capacity to convey passengers and goods and the associated costs to support this mobility. This involves setting routes enabling new or existing interactions between economic entities.
• Operational . Improvement in the time performance, notably in terms of reliability, as well as reduced loss or damage. This implies a better utilization level of existing transportation assets benefiting its users as passengers and freight are conveyed more rapidly and with fewer delays.
• Geographical . Access to a broader market base where economies of scale in production, distribution, and consumption can be improved. Increases in productivity from the access to a larger and more diverse base of inputs (raw materials, parts, energy, or labor) and broader markets for diverse outputs (intermediate and finished goods). Another important geographical impact concerns the influence of transport on the location of activities and its impacts on land values.

The economic importance of the transportation industry can thus be assessed from a macroeconomic and microeconomic perspective:

• At the macroeconomic level (the importance of transportation for a whole economy), transportation and related mobility are linked to a level of output, employment , and income within a national economy. In many developed economies, transportation accounts for between 6% and 12% of the GDP. Further, logistics costs can account for between 6% and 25% of the GDP. The value of all transportation assets, including infrastructures and vehicles, can easily account for half the GDP of an advanced economy.
• At the microeconomic level (the importance of transportation for specific parts of the economy), transportation is linked to producer, consumer, and distribution costs. The importance of specific transport activities and infrastructure can thus be assessed for each sector of the economy. Usually, higher income levels are associated with a greater share of transportation in consumption expenses. Transportation accounts for between 10% and 15% of household expenditures. In comparison, it accounts for around 4% of the costs of each unit of output in manufacturing, but this figure varies greatly according to sub-sectors.

The added value and employment effects of transport services usually extend beyond those generated by that activity; indirect effects are salient. For instance, transportation companies purchase some of their inputs (fuel, supplies, maintenance) from local suppliers. These inputs generate additional value-added and employment in the local economy. In turn, the suppliers purchase goods and services from other local firms. There are further rounds of local re-spending, which generate additional value-added and employment. Similarly, households that receive income from employment in transport activities spend some of their income on local goods and services. These purchases result in additional local jobs and added value, with some of the income from these additional jobs spent on local goods and services, thereby creating further jobs and income for local households. As a result of these successive rounds of re-spending in the framework of local purchases, the overall impact on the economy exceeds the initial round of output, income, and employment generated by passenger and freight transport activities. Thus, from a general standpoint, the economic impacts of transportation can be  direct, indirect, and induced :

• Direct impacts. The outcome of improved capacity and efficiency where transport provides employment, added value, larger markets, as well as time and cost improvements. The overall demand of an economy is increasing.
• Indirect impacts. The outcome of improved accessibility and economies of scale. Indirect value-added and jobs result from local purchases by activities directly dependent upon transportation. Transport activities are responsible for a wide range of indirect value-added and employment effects through the linkages of transport with other economic sectors (e.g. office supply firms, equipment, and parts suppliers, maintenance and repair services, insurance companies, consulting, and other business services).
• Induced impacts. The outcome of the economic multiplier effects when the price of commodities, goods, or services drops and their variety increases. For instance, the steel industry requires the cost-efficient import of iron ore and coal for blast furnaces and export activities for finished products such as steel booms and coils. Manufacturers, retail outlets, and distribution centers handling imported containerized cargo rely on efficient transport and seaport operations.

Transportation links together the factors of production in a complex web of relationships between producers and consumers. The outcome is commonly a more efficient division of production by the exploitation of comparative geographical advantages, as well as the means to develop economies of scale and scope. The productivity of space, capital, and labor is thus enhanced with the efficiency of distribution and personal mobility. Economic growth is increasingly linked with transport developments, namely infrastructures, but also with managerial expertise, which is crucial for logistics. Thus, although transportation is an infrastructure-intensive activity, hard assets must be supported by an array of soft assets, namely labor, management, and information systems. Decisions about using and operating transportation systems must be made to optimize benefits and minimize costs and inconvenience.

2. Transportation and Economic Opportunities

Transportation developments that have taken place since the beginning of the Industrial Revolution have been linked to growing economic opportunities . At each development stage of the global economy , a particular transport technology has been developed or adapted with an array of impacts. Economic cycles are associated with a variety of innovations , including transportation, influencing economic opportunities for production, distribution, and consumption. Historically, six major waves of economic development where a specific transport technology created new economic, market, and social opportunities can be suggested:

• Seaports . The historical importance of seaports in trade has been enduring. This importance was reinforced by the early stages of European expansion from the 16th to the 18th centuries, commonly known as the Age of Exploration. Seaports supported the early development of international trade through colonial empires but were constrained by limited inland access. Later in the industrial revolution, many ports became important industrial platforms. With globalization and containerization, seaports increased their importance in supporting global trade and supply chains. The cargo handled by seaports reflects the economic complexity of their hinterlands. Simple economies are usually associated with bulk cargoes, while complex economies generate more containerized flows. Technological and commercial developments have incited a greater reliance on the oceans as an economic and circulation space.
• Rivers and canals . River trade has prevailed throughout history, and even canals were built where no significant altitude change existed since lock technology was rudimentary. The first stage of the Industrial Revolution in the late 18th and early 19th centuries was linked with the development of canal systems with locks in Western Europe and North America, mainly to transport heavy goods. This permitted the development of rudimentary and constrained inland distribution systems, many of which are still used today.
• Railways . The second stage of the industrial revolution in the 19th century was linked with the development and implementation of rail systems, enabling more flexible and high-capacity inland transportation systems. This opened substantial economic and social opportunities through the extraction of resources, the settlement of regions, and the growing mobility of freight and passengers.
• Roads . The 20th century saw the rapid development of comprehensive road transportation systems, such as national highway systems and automobile manufacturing, as a major economic sector. After the Second World War, individual transportation became widely available to mid-income social classes. This was associated with significant economic opportunities to service industrial and commercial markets with reliable door-to-door deliveries. The automobile also permitted new forms of social opportunities, particularly with suburbanization.
• Airways and information technologies . The second half of the 20th century saw the development of global air and telecommunication networks in conjunction with economic globalization. New organizational and managerial forms became possible, especially in the rapidly developing realm of logistics and supply chain management. Although maritime transportation is the physical linchpin of globalization, air transportation and IT support the accelerated mobility of passengers, specialized cargoes, and their associated information flows.

No single transport mode has been solely responsible for economic growth. Instead, modes have been linked with the economic functions they support and the geography in which growth was taking place. The first trade routes established a rudimentary system of distribution and transactions that would eventually be expanded by long-distance  maritime shipping networks and the setting of the first multinational corporations managing these flows. Major flows of international migration that occurred since the 18th century were linked with the expansion of international and continental transport systems that radically shaped emerging economies such as North America and Australia. Transport played a catalytic role in these migrations, transforming the economic and social geography of many nations.

Transportation has been a tool of territorial control , particularly during the colonial era, where  resource-based transport systems supported the extraction of commodities in the developing world and forwarded them to the industrializing nations of the time. The goal to capture resource and market opportunities was a strong impetus in the setting and structure of transport networks. More recently, port development, particularly container ports, has been of strategic interest as a tool of integration into the global economy, as the case of China illustrates. There is a direct relationship, or coordination, between foreign trade and container port volumes, so container port development is commonly seen as a tool to capture the opportunities brought by globalization. The growth of container shipping has systematically been 3 to 4 times the GDP growth rate, underlining a significant multiplier effect between economic growth and container trade. However, this multiplying effect has substantially receded since 2009, underlining the  maturity of the diffusion of containerization  and its dissociation from economic growth.

Due to demographic pressures and urbanization, developing economies are characterized by a mismatch between the limited supply and growing demand for transport infrastructure. While some regions benefit from the development of transport systems, others are often marginalized by conditions in which inadequate transportation plays a role. Transport by itself is not a sufficient condition for development. However, the lack of transport infrastructures can be a constraining factor in development. The lack of transportation infrastructures and regulatory impediments are jointly impacting economic development by conferring higher transport costs, but also delays rendering supply chain management unreliable. A poor transport service level can negatively affect the competitiveness of regions and their economic activities and thus impair the regional added value, economic opportunities, and employment. Tools and measures are being developed to assess and compare the performance of national transportation systems. For instance, in 2007, the World Bank published its first-ever report ranking nations according to their logistics performance based on the  Logistics Performance Index . Logistic performance is commonly associated with economic opportunities.

3. Economic Returns of Transport Investments

A common expectation is that transport investments will generate economic returns, which should justify the initial capital commitment in the long run. Like most infrastructure projects, transportation infrastructure can generate a 5 to 20% annual return on the capital invested, with such figures often used to promote and justify investments. However, transport investments tend to have  declining marginal returns ( diminishing returns ) . While initial infrastructure investments tend to have a high return since they provide an entirely new range of mobility options, the more the system is developed, the more likely additional investment would lower returns. The marginal returns can sometimes be close to zero or even negative. A common fallacy assumes that additional transport investments will have a similar multiplying effect than the initial investments had, which can lead to capital misallocation. The most common reasons for the declining marginal returns of transport investments are:

• High accumulation of existing infrastructure . Where there is a high level of accessibility and where transportation networks are already extensive, further investments usually result in marginal improvements. This means that the economic impacts of transport investments tend to be significant when infrastructures were previously lacking and tend to be marginal when an extensive network is already present. Additional investments can thus have a limited impact outside convenience.
• Economic changes . As economies develop, their function shifts from the primary (resource extraction) and secondary (manufacturing) sectors towards advanced manufacturing, distribution, and services. These sectors rely on different transport systems and capabilities. While an economy depending on manufacturing will rely on road, rail, and port infrastructures, a service economy is more oriented toward logistics and urban transportation efficiency. Transport infrastructure is important in all cases, but its relative importance in supporting the economy may shift.
• Clustering . Due to clustering and agglomeration, several locations develop advantages that cannot be readily reversed through improvements in accessibility. Transportation can be a factor of concentration and dispersion depending on the context and the level of development. Less accessible regions do not necessarily benefit from transport investments if they are embedded in a system of unequal relations.

Therefore, each transport development project must be considered independently and contextually. Since transport infrastructures are capital-intensive fixed assets, they are particularly vulnerable to misallocations and malinvestments . The standard assumption is that transportation investments tend to be more wealth-producing than wealth-consuming investments such as services. Still, several transportation investments can be wealth consuming if they merely provide conveniences, such as parking and  sidewalks , or service a market size well below any possible economic return, with, for instance, projects labeled “bridges to nowhere”. In such a context, transport investment projects can be counterproductive by draining the resources of an economy instead of creating wealth and additional opportunities.

Since many transport infrastructures are provided through public funds, they can be pressured by special interest groups, which can result in poor economic returns, even if those projects are often sold to the public as strong catalysts for growth. Further, large transportation projects, such as public transit, can have inadequate cost control mechanisms, implying systematic budget overruns . Infrastructure projects in the United States are particularly prone to these engineered fallacies. Efficient and sustainable transport markets and systems play a key role in regional development, although the causality between transport and wealth generation is not always clear. To better document and monitor the economic returns of transport investments, a  series of indicators can be used, such as transportation prices and productivity. Investment in transport infrastructures is thus seen as a regional development tool, particularly in developing countries.

4. Types of Transportation Impacts

The relationship between transportation and economic development is difficult to establish formally and has been debated for many years. In some circumstances, transport investments appear to catalyze economic growth, while in others, economic growth puts pressure on existing transport infrastructures and incites additional investments. Transport markets and related transport infrastructure networks are key drivers in promoting more balanced and sustainable development, particularly by improving accessibility and opportunities for less-developed regions or disadvantaged social groups. Initially, there are different impacts on transport providers (transport companies) and transport users . There are several layers of activity that transportation can  valorize , from a suitable location that experiences the development of its accessibility through infrastructure investment to better usage of existing transport assets through more efficient management. This is further nuanced by the  nature, scale, and scope of possible impacts:

• Timing of the development . The impacts of transportation can precede (lead), occur during (concomitantly), or take place after (lag) economic development. The lag, concomitant, and lead impacts make it difficult to separate the specific contributions of transport to development. Each case appears specific to a set of timing circumstances that are difficult to replicate elsewhere.
• Types of impacts . They vary considerably as the spectrum ranges from positive to negative. Usually, transportation investments promote economic development, while in rarer cases, they may hinder a region by draining its resources in unproductive transportation projects.

Cycles of economic development provide a revealing conceptual perspective on how transport systems evolve in time and space , including the timing and nature of transport’s impact on economic development. This perspective underlines that after a phase of introduction and growth, a transport system will eventually reach maturity through geographical and market saturation. There is also the risk of overinvestment, particularly when economic growth is credit driven, which can lead to significant  misallocations of capital . The outcome is surplus capacity in infrastructures and modes, creating deflationary pressures that undermine profitability. In periods of recession that commonly follow periods of expansion, transportation activities may experiment with a  setback in terms of lower demand and a scarcity of capital investment. Because of their characteristics, several transport activities are highly synchronized with the level of economic activity. For instance, if rail freight or maritime rates were to decline rapidly, this could indicate deteriorating economic conditions.

Transport, as a technology, typically follows a path of experimentation, introduction, adoption, diffusion, and, finally, obsolescence, each of which impacts the rate of economic development. The most significant benefits and productivity gains are realized in the early to mid-diffusion phases, while later phases are facing diminishing returns. Containerization is a relevant example of such a diffusion behavior as its productivity benefits were mostly derived in the 1990s and 2000s when economic globalization was accelerating.

If relying upon new technologies, transportation investments can go through what is called a “ hype phase ” with unrealistic expectations about their potential and benefits. Some projects are eventually abandoned as the technology is ineffective at addressing market or operational requirements or is too expensive for its benefits. Since transportation is capital intensive , operators tend to be cautious before committing to new technologies and the significant sunk costs they require. This is particularly the case where transportation is capital-intensive and has a long lifespan. In addition, transport modes and infrastructures are depreciating assets that continuously require maintenance and upgrades. Eventually, their useful lifespan is exceeded, and the vehicle must be retired or the infrastructure rebuilt. Thus, the amortization of transport investments must consider the lifespan of the concerned mode or infrastructure.

5. Transportation as an Economic Factor

Contemporary trends have underlined that economic development has become less dependent on relations with the environment (resources) and more dependent on relations across space . While resources remain the foundation of economic activities, the commodification of the economy has been linked with higher levels of material flows. Concomitantly, resources, capital, and even labor have shown increasing levels of mobility. This is particularly the case for multinational firms that can benefit from transport improvements in two significant markets:

• Commodity market . Improvements in the efficiency with which firms have access to raw materials and parts as well as to their respective customers. Thus, transportation expands opportunities to acquire and sell a variety of commodities necessary for industrial and manufacturing systems.
• Labor market . Improvements in access to labor and a reduction in access costs, mainly by improved commuting (local scale) or the use of lower-cost labor (global scale).

Transportation provides market accessibility by linking producers and consumers so that transactions can occur. A common fallacy in assessing the importance and impact of transportation on the economy is to focus only on transportation costs, which tend to be relatively low; in the range of 5 to 10% of the value of a good. Transportation is an economic factor of production of goods and services, implying that it is fundamental in their generation, even if it accounts for a small share of input costs. This means that irrespective of the cost, an activity cannot take place without the transportation factor and the mobility it provides. Thus, relatively small transport costs, capacity, and performance changes can substantially impact dependent economic activities.

An efficient transport system with modern infrastructures favors many economic changes, most of them positive. The major impacts of transport on economic factors can be categorized as follows:

• Geographic specialization . Improvements in transportation and communication favor a process of geographical specialization that increases productivity and spatial interactions. An economic entity tends to produce goods and services with the most appropriate combination of capital, labor, and raw materials. A region will thus tend to specialize in producing goods and services for which it has the greatest advantages (or the least disadvantages) compared to other regions as long as appropriate transport is available for trade. Through geographic specialization supported by efficient transportation, economic productivity is promoted. This process is known in economic theory as  comparative advantages that have enabled the economic specialization of regions.
• Scale and scope of production . An efficient transport system offering cost, time, and reliability advantages enables goods to be transported over longer distances. This facilitates mass production through economies of scale because larger markets can be accessed. The concept of  “just-in-time” in supply chain management has further expanded the productivity of production and distribution with benefits such as lower inventory levels and better responses to shifting market conditions. Thus, the more efficient transportation becomes, the larger the markets that can be serviced, and the larger the scale of production. This results in lower unit costs.
• Increased competition . When transport is efficient, the potential market for a given product (or service) increases, and so does competition. A wider array of goods and services becomes available to consumers through competition, reducing costs and promoting quality and innovation. Globalization has been associated with a competitive environment that spans the world and enables consumers to access a wider range of goods and services.
• Increased land value . Land adjacent or serviced by good transport services generally has greater value due to its utility. Consumers can have access to a wider range of services and retail goods. In contrast, residents can have better accessibility to employment, services, and social networks, all of which result in higher land value. Irrespective of if used or not, the accessibility conveyed by transportation impacts the land value. In some cases, due to the externalities they generate, transportation activities can lower land value, particularly for residential activities. Land located near airports and highways, near noise and pollution sources, will thus be impacted by corresponding diminishing land value.

Transport also contributes to economic development through  job creation and derived economic activities . Accordingly, many direct (freighters, managers, shippers) and indirect (insurance, finance, packaging, handling, travel agencies, transit operators) employment are associated with transport. Producers and consumers make economic decisions on products, markets, costs, location, and prices, which are based on transport services, availability, costs, capacity, and reliability.

Related Topics

• 1.5 – Trans p ortation and Commercial Geography
• 3.3 – Transport Costs
• 3.4- The Provision and Demand of Transportation Services
• 1.3 – The Emergence of Mechanized Transportation Systems
• 1.4 – The Setting of Global Transportation Systems
• 2.2 – Transport and Spatial Organization
• B.16 – The Financing of Transportation Infrastructure

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Open Access

Peer-reviewed

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
• Peer Review
• Reader Comments

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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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.

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• “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.

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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.

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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).

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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.

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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.

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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.

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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.

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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.

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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.

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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 role of transportation in developing the tourism sector at high altitude destination, Kinnaur

Ravinder jangra.

1 Department of Geography, Kurukshetra University, Kurukshetra, Haryana 136119 India

S. P. Kaushik

Etender singh.

2 Department of Geography, Guru Nanak Khalsa College, Yamunanagar, Haryana India

Parveen Kumar

3 Town and Country Planning, Kurukshetra, India

Poonam Jangra

4 Department of Mathematics, Maharishi Markandeshwar University, Mullana, Ambala, Haryana India

As a supporting factor, transportation is an important element of destination image that provides a base for the successful tourism industry. It is like the blood vessels of an area and is considered a determinant in developing a tourist destination. The article aims to characterize the status or problem of transportation accessibility in Kinnaur. GARMIN hand GPS (Global Positioning System) has been used to identify the damaged roads from their start to endpoints. In addition, a simple random sample technique has been used to register the opinion of 280 tourists about the transport facilities. Study results suggest that the bad condition of National Highway–22 is one of the barriers to tourism development in Kinnaur. There were no significant differences found between the selected destinations. Overall, the district headquarters, Kalpa, has been perceived significantly higher agreements by tourists. The government should ensure that the Border Road Organization, the organization entrusted with the responsibility of construction and maintenance of roads in international border areas, has sufficient resources to invest in transport development and its maintenance.

Introduction

Nowadays, the transport system is a fundamental component of the tourism industry and a prerequisite for the development in any region of the world (Khadaroo and Seetanah, 2007 ; Schiefelbusch et al., 2007 ; Musa & Ndawayo, 2011 ; Currie & Falconer, 2014 ; Szymanska et al., 2021 ). A good transport structure increases travelers’ convenience to diverse destinations and is indispensable to enjoy more leisure activities (Khadaroo and Seetanah, 2007 ). Tourism and transport infrastructure are interconnected and economically beneficial (Chen et al., 2021 ; Haller et al., 2020 ; Nenavath, 2021 ). Tourism and transport are not two diverse organizations, but they complement each other. Moreover, transportation is considered as a determinant of destination attractiveness (Robinson, 1976 ; Chew, 1987 ; Gunn, 1988 ; Martin & Witt, 1988 ; Inskeep, 1991 ; Chon et al., 1991 ; Hu & Ritchie, 1993 ; Gallarza et. al., 2002 ; Naudee and Saayman, 2004 ). It is the most necessary component, and basically, it offers accessibility to tourism. A country attracts more tourists with a good infrastructure of road and transport (Virkar & Mallya, 2018 ). Numerous scholars have suggested a positive link between road infrastructure and tourism activities (Khadaroo and Seetanah, 2007 ; Liu and Shi, 2019 ) which directly impacted the development of tourism development (Nazneen et al., 2019 ; Kanwal et al., 2020 ).

Moreover, tourism infrastructure positively impacts residents’ quality of life (Mamirkulova et al., 2020 ). Certain studies have mentioned that if the destination is deprived of accessibility, tourism cannot occur (Chew, 1987 ; Prideaux, 2000 ). Accessibility constitutes one important consideration to tourism development in the drive for innovation and new recommendations (Gillovic & McIntosh, 2020 ). It is a major component and offers driving pleasure to the tourists. It refers to the ease of reaching goods, services, activities, and destinations. If it is uncomfortable, tourists will seek another destination. The topography of an area plays a significant role in implementing strategies/policies. It is difficult to have a good transportation network in mountain areas compared to plain and plateau areas. (Dhali & Dar, 2019 ). Transportation seems to be the lifeline of the trade, tourism, and commerce industry (Summers, 2000 ), and it provides certification for the development of connectivity (Grydehoj & Casagrande, 2019 ). The development of transportation at natural destinations may also negatively impact, i.e., air pollution, noise pollution, traffic congestion, overcrowding, and destruction of natural attractiveness (Kanwal et al., 2019 ; Nunkoo & Ramkissoon, 2011 ; Park et al., 2015 ).

Therefore, more researchers emphasize green transport or sharing transport mobility. Recently, the introduction of electric vehicles by many companies such as Tata Nexon and Tigor, Mahindra E Verito, Hyundai Kona, MG ZS, Nissan Leaf, Mercedes-Benz EQC, Audi e-Tron, electric scooters, and many more has been instrumental in significantly reducing the environmental impacts of transport. But the technologies are in the initial phase of operation in India and are costly compared to combustion engine vehicles. Besides, taxi operators like Ola, Uber, and Bla-Bla carpooling systems are rapidly increasing in metro cities and inter-city travelling. The government of India has planned to shift different types of vehicles to the electric mode by 2030. In the state of Himachal Pradesh, the government has already started electric buses on an experimental basis in the Kullu–Manali area, a prominent tourist place in the mountain environment adjoining the study area ( https://timesofindia.indiatimes.com/auto/news/manufacture-himachal-specific-prototype-electric-buses-state-chief-minister-tells-ev-makers/articleshow/98381562.cms?from=mdr ). So far, the experiences are encouraging, and it is expected that such an environment-friendly mode of transport will be introduced in the entire state in due course of time after laying down the re-charging infrastructure. This would certainly be a great step for sustainable tourism in the study area and relieve the environmental stress put on by the use of combustion engines by the vehicles entering the study area. In that context, accessibility requires a more comprehensive analysis in the recent planning paradigm.

Tourism has been recognized as one of the most important sectors of the economy for generating the livelihood of the local folks in Kinnaur. It is also a vehicle for sustainable poverty reduction and a major growth factor for the future. National Highway-22 is a major road in the cold desert destination of Kinnaur district in Satluj, Baspa, and Spiti valley. However, this road has been considered one of the “Deadliest Roads” globally. It has played a very important role in socio-economic development, trade and travel, defense, and tourism. Improved road connectivity in the freezing desert alleviates transportation constraints and attracts more tourists, boosting local inhabitants’ social and economic advantages. Hence, the article recognizes the role of transport accessibility to increase tourist footfall in Kinnaur. It evaluates their current status as well as associated problems. Transport is recognized as a potential basis for attracting tourism to such terrain, so it is necessary to evaluate transport infrastructure to regulate tourism activities in this ecologically fragile area.

Previous research

From the early times, the transport system has had a strong effect on tourism development (Kaul, 1985 ). Although several researchers acknowledge the necessity for effective transport in the successful program of tourism development but very little attention to the significance of transportation in destination development. Some researchers admit that there has a great link between tourism and transport but fail to identify any specific causal relationships between them (Gilbert, 1939 ; Gunn, 1994 ; Hall, 1991 ; Inskeep, 1991 ; Page, 1994 , 1999 ; Robinson, 1976 ; Thurot, 1980 ). Several models are proposed by academics (Barrett, 1958 ; Barbaza, 1970 ; Butler, 1980 ; Lavery, 1974 ; Smith, 1992 ; Soane, 1993 ; Young, 1983 ) to describe the progress in tourism destinations; they recognized transport as a major factor in the growth process. The economic role of transport is as a bridge between the tourist (buyer) and the tourism services (seller).

Numerous studies have been done in different physiographic regions of the world. Gearing et al. ( 1974 ) reveal the attractiveness of Turkey’s tourist destination, and he stressed the development of transport infrastructure at the destination. Kaul ( 1985 ) has identified the significance of transport setup as a vital element in the effective progress of tourism, especially the creation of new attractions and the development of existing ones. Furthermore, Gunn ( 1988 ) states that tourists use multiple services, especially transportation. In other studies, in the case of Victoria (Canada) (Murphy et al., 2000 ), the case of Australia, the case of Turkey (Kozak & Rimmington, 1999 ) case of Sun Lost City, South Africa (Kim et al., 2000 ) and the case of 51 islands (McElroy, 2003 ) also emphasized the significance of transport infrastructure for a destination success. It is not possible to grow tourism without roads and other infrastructure (Crouch & Ritchie, 1999 ).

Most tourists of developed countries are familiar with modern and efficient transport infrastructure, and they also expect that experience to be in the destination country (Prideaux, 2000 ). Moreover, the various infrastructure elements interact with the tourists (Murphy et al., 2000 ). Australia’s Tourism Task Force ( 2003 ) states that transport infrastructure is a big part of linking tourism-generating regions to destinations. Naude and Saayman ( 2004 ) emphasize the importance of infrastructure in tourism development. The local government established an electric bus line around the Khon Kaen to respond to the citizens and tourism needs (Sorupia, 2005 ).

Similarly, automobile transportation makes tourism stress-free to perceive local culture and nations (Oter, 2007 ). On Phuket Island, the transportation system resolves the problems such as public transportation and improving the Island’s road network (Sakolnakorn et al., 2013 ). The Government of Thailand collaborates with a local organization in all the areas of north-eastern Thailand. In European countries included in the Organization for Economic Co-operation and Development (OECD), progress in road infrastructure development could be associated with increased tourism spending.

The preceding review suggests that enormous work has been done in the field of transport’s role in the development of tourism. However, no study related to transportation has been reported on the cold desert, especially the Himachal Pradesh part of Himalayas, India. According to the Road Accident Data Management System (RADMS), of the state, there were 213 road accidents in Kinnaur during the year 2015–20 and consequently, occurred 205 fatalities and 353 persons became injured. To regulate and develop tourism as a sustainable livelihood option at high altitude destinations, robust transportation development is necessitated as a threshold benchmark to attract prospective travelers. It is in this context that the present study is an attempt to fill this gap.

The article consists of five sections; the introduction section presents the background of the study and a review of literature that focuses on transportation’s role in tourism development. Section  2 examines the study area characteristics briefly. Further, Sect.  3 analyzes the database and methodology to identify the actual condition of transport infrastructure. Section  4 discusses the results after applying the proposed methodology. Finally, Sect.  5 reveals the conclusions and recommendations.

Study area description

Kinnaur is a well-known tourist destination at high altitudes in the Himalayan region. It is situated in the northeastern part of Himachal Pradesh near the Indo-China border with 6401 sq. km (Fig.  1 ). With an average of 1,11,393 tourist arrivals per year from 1990 to 2020, Kinnaur gives huge exposure to tourism worldwide. The study area offers an extensive range of products, i.e., natural attractions, hiking, skiing, bungee jumping, rock climbing, mountain biking, paragliding, etc., that attract mass tourism. For the present research, three major tourist destinations, Chitkul, Kalpa, and Nako, have been selected. Spread on both sides of the Great Himalaya range that traverses through the state, well-connected by an all-weather motorable National Highway-22, popularly known as Hindustan-Tibet Road. After the construction of NH-22 along the river, many new riverside villages Bhabanagar, Wangtu, Tapri, Powari have spilled over closer to the river. Besides, many traditional villages Spillo, Kanan, Poo, Maling, etc., have also extended downwards to the road edge. There are several places, namely, Nichar, Kothi, Ribba, Moorang, Sangla, Rakcham, Chitkul, Namgya, Pooh, Chango, Nako, Leo, and Lippa that attract lots of tourists.

Map of the study area

Database and methodology

The study requires the collection of a wide range of primary and secondary data to comprehensively examine. Adequate care has been taken to ensure that all the data meet the research objectives. The data have been collected from government organizations and downloaded from the official websites of different departments (Table 1 ). The primary data required for achieving the stated goal were collected from selected destinations of Chitkul, Kalpa, and Nako in the Kinnaur district through a well-structured questionnaire. To analyze the road condition of different travel routes in Kinnaur, GARMIN hand GPS was used to point out the damaged road from its start to end portions. Data about bus services have been recorded from the schedule chart available at the Reckong-Peo bus stand, while a focused group discussion (FGD) was accomplished with employees of fuel stations.

Secondary data and their sources

Sample procedure

A simple random sampling method was applied to collect data from the tourists. A total of 280 samples were collected over the two weeks in June 2016. About 80 tourist samples have been collected from three selected tourist destinations. Moreover, 40 additional samples have been collected from Reckong-Peo, the new district headquarters of Kinnaur (about 13 km. from Kalpa), which is the center of whole tourism activities. In Nako and Chitkul, only 10 to 30 tourists visited per day during the survey period. In addition, there is only one daily bus service between headquarters and some destinations like Nako in Kinnaur and Kaza in Lahaul and Spiti. Therefore, collecting such a large number of samples had consumed a lot of time, and a further increase in samples was not possible because the next month was July, which falls in the rainy monsoon season. There is a sharp decline in travelers during the rainy season because of frequent landslides and shooting stones. The emphasis has always been on collecting representative samples for good research results. Hence, 280 interviews of tourists were conducted in 2016 during the peak tourist month, June, before the rainy period to avoid extreme weather conditions and incidences of landslides. It remains cut off from the rest of the country during the winter season due to climate constraints and poor transportation infrastructure.

Survey instrument

A questionnaire is the most commonly used instrument to gather survey data. The questionnaire is comprised of three sections. The first section was structured to characterize a tourist’s gender, age, education level, occupation, place of residence, etc. The second section includes questions related to the visitor’s journey to the destination. The last section is concerned with respondents’ perceptions about the destination (Questionnaire attached in appendix). Tourist perceptions were recorded on a five-point Likert scale, from score 1 (negative) to score 5 (positive).

Data analysis

In a comparative analysis, the quality of particular characteristics allows us to compare that with others. After reviewing the literature, the tourism infrastructure has been classified into four broad categories. The study focused on accessibility and transport services that are major physical infrastructure components. Table 2  shows the types of facilities characterized by the author that fit in the study area. In the context of road conditions of major travel routes, the Global Positioning System (GPS) was used to determine the coordinates of road conditions. The road conditions have been classified as:

• Good condition—road with a good surface layer or without damage
• Partially damaged road—road having upper layer washed out or under maintenance
• Fully damaged road—a road that completely or mostly washed away

Types of facilities characterized by the author

The Google earth image was digitized through the ArcGIS 10.4 software for the mapping. Besides, a traffic flow diagram was prepared for understanding the routes from and to the destination. In addition, skewness and kurtosis have been examined to measure the data symmetry. If the distribution is close to 0 value, it is probably close to normal. The correlation coefficient has been calculated to identify any relationship between the destinations. After that, the ANOVA test and Tukey test were conducted to examine the differences in tourist perceptions as well as to compare all the possible pairs of means at the statistical significance level of 0.05 (Haralambopoulos & Pizam, 1996 ; Tosun, 2002 ; Singla, 2014 ; Hritz & Ross, 2010 ; Turker and Ozturk, 2013 ; Karnchanan, 2011 and Brida et al., 2012 ). Lastly, a radar diagram has been used for the graphical representation of differences between items at the selected destinations.

Results and discussion

Tourist profile.

The socio-demographic variables include individual and personal characteristics such as nationality, gender, age, religion, marital status, education level, language known, and occupation. Researchers have identified the differences in the perception depending on these variables (Beerli and Mart, 2004 ; Baloglu & McCleary, 1999 ). These characteristics influence tourists’ attitudes toward tourism development (Harrill, 2004 ). The ratio between male and female tourists was 73.50 to 26.50. The share of international tourists was about 26 percent coming from 19 countries. The majority of the tourists were aged 21 to 40 years, whereas those aged less than 15 or older than 60 were insignificant, suggesting that the harsh terrain conditions are challenging to some age groups and females. The analysis reveals that the largest proportion (65.3 percent) of tourists were Hindu, followed by Jewish (11.8 percent), Sikhs, Christians, and Buddhists. Approximately 60 percent of tourists completed graduation and university degrees. About half of the tourists were married. Tourists engaged in their own business (36.0 percent) constituted the largest share, followed by students (24.9 percent). Most of the tourists were comfortable speaking Hindi and/or English; however, many were well versed in multiple languages (Table ​ (Table3 3 ).

Demographic profile of surveyed tourist’s ( N  =  280 ) Source: Field survey, 2016

Seasonality in tourism

The seasonal pattern of tourist arrivals has been detected in two peak seasons in Kinnaur, which are ‘May–June’ and ‘September–October’ (Fig.  2 ). The May–June season is the most favorable tourist season in Kinnaur and entire hilly areas due to the scorching summer season added by the vacation period in surrounding plain areas. While September–October months are considered as mini peak season in Kinnaur. Though upper Kinnaur is a cold desert area, heavy rainfall occurred during the month of July–August in lower Kinnaur. Besides, the winter months (December, January, February, and March) are marked as lean months due to extremely low temperatures and heavy snowfall in Kinnaur. Both climatic phenomena trigger landslides and worked as constraints in the development of tourism in the study area. There is a need to develop good road infrastructure like in Europe and Russia that minimize the impact of natural constraints at high altitudes.

Seasonal index of tourist arrivals in Kinnaur, 1991–2020

Accessibility to the Kinnaur

There are no direct rail services to Kinnaur, and the nearest railway station is at Shimla. It is a narrow-gauge railway line (Kalka–Shimla railway line distance of 96 km.) and is around 226 km. from Kinnaur (Fig.  3 ). It is one of the UNESCO world heritage sites.

Major travel routes to Kinnaur

Similarly, there is no direct flight to Kinnaur from any airport like rail networks. The nearest airport at Shimla is around 226 km. from Reckong-Peo, the district headquarter. Shimla airport is also connected to Delhi and Kullu, which provides air accessibility to the destination. Moreover, 11 helipads have been constructed in Kinnaur for strategic reasons and natural disaster rescue operations (Himachal Pradesh State Disaster Management Authority, 2018 ). In the context of selected destinations, Nako and Reckong-Peo, ITBP has helipads that can be used by Indian defense forces for tourists in adverse situations (Fig.  3 ).

Road transport is a major mode of access to Kinnaur through which tourists can reach the destination directly. Also, most tourists preferred road mode to explore the Himalayas, which makes them understand the geomorphology and geology of the Himalayas more closely and instills self-confidence and energy. Moreover, the Trans-Himalayan rough terrain acts as a barrier to constructing railway lines and developing airports. The frequent trip circle used by tourists is Chandigarh–Shimla–Narkanda–Reckong-Peo–Kalpa–Nako–Kaza–Rohtang–Manali–Chandigarh, mainly covered by NH-21 and NH-22. Some of the tourists preferred Shimla–Narkanda–Reckong-Peo–Kalpa–Nako–Kaza–Rohtang Pass–Keylong–Leh Ladakh and the back trip circle. The major route to reach the destination is NH-22 which directly connects it with Shimla (state capital), Chandigarh (state capital of both Haryana and Punjab), and Delhi (national capital).

National highway -22

The National Highway -22 is an important route for socio-economic sectors, trade and travel, defense, and tourism. The route is the lifeline of Solan, Shimla, and Kinnaur districts connecting the new townships Reckong-Peo, Bhavanagar, Jhakri, and Jari. The old recognize towns are Theog, Narkanda, Ani, Nirmand, Rampur, Pooh, and Tapri on this route. This route is known as the ‘Hindustan-Tibet road,’ or ‘Silk route’ that starts from Ambala (Haryana) to Kaurik (Indo-Tibet border) and represents its strategic importance. The route also provides direct connectivity to Shimla, Chandigarh, and the national capital Delhi through National Highway-1 (now re-designated as NH-44). The road was featured on the ‘History Channel’ as one of the “Deadliest Roads” in the world ( https://www.financialexpress.com/auto/car-news/5-most-dangerous-roads-in-india-think-twice-before-taking-these-routes/1539265/ ). On this route, a police post has been put at Sumdo where foreigners have to submit their permits to enter the protected area of Kinnaur.

Other route connectivity with National Highway

Apart from National Highway-22, other state highways and national highways that connect Kinnaur to other states or destinations play an important role in the accessibility to Kinnaur (Table 4 ). The NH-20 provides a comfortable journey for tourists coming from northern Punjab, and its connection with NH-21 moves tourists to Kinnaur from the side of Lahaul and Spiti. Similarly, NH-70 connects it to Punjab state. Mandi district is the joining point of NH-20, NH-70, and NH-21, providing a way to the destination from the Lahaul and Spiti route. Moreover, NH-72 connects Kinnaur with Uttrakhand and Haryana state that joins NH-22 to reach the destination ( Fig.  3 ) . A connection between Kalpa–Reckong Peo–Powari will be provided through NH-5, a newly constructed national highway in Kinnaur.

Major national highways in Himachal Pradesh as on 09.09.2019.

Source: http://hppwd.hp.gov.in/sites/default/files/NH%20Length%209-9-2019.pdf

NHAI : National Highway Authority of India

Other route connectivity with state highway

One of the most traveled routes selected by bikers is Reckong-Peo-Nako-Kaza-Keylong-Leh-Ladakh. The Leh–Keylong highway, which is part of the Leh–Manali highway, connects Jammu and Kashmir with Himachal Pradesh and Kinnaur. It is the major road selected by tourists who come from the side of Jammu and Kashmir. Also, this route has significance for tourists who visit Leh–Ladakh via Kinnaur (Fig.  3 ).

Accessibility within the Kinnaur

The major mode of transport within Kinnaur is a road, and NH-22 is the district’s lifeline. Although it connects the major villages or tourist destinations, it is not maintained adequately and needs to develop the travel route infrastructure of high quality to link tourist spots. The total length of the road, including national highways, was 1134 km. in the year 2018–19. Most of the roads mounted in the district are motorable with a single lane (839 km.). Length of about 45 Kilometers of the motorable road is a double lane that provides comfortable and safe driving in the district. However, due to the tough terrain in a high mountain area, it does not have any motorable four-lane road. (Fig.  4 ).

Road length with conditions in Kinnaur, 2018–19. Note: Road length includes NH and border roads Source: Public Works Department, H.P

Table ​ Table5 statistics 5  statistics represent the total metalled and un-metalled road length in Kinnaur. The roads in the district are under the Public Works Department for construction and maintenance. However, beyond Pooh Tehsil toward the Tibet border, the work of maintenance is regularly done by BRO (Border Road Organization) due to its strategic importance. The destination has an international border with Tibet and China; thus, the location is important for the country. The development of roads is very poor as of December 2020, though it has improved a lot compared to earlier conditions. The result of derived information indicates that the density of the road per 100 sq. km. is very low due to its tough terrain, low density of population, and political apathy. The density of metalled roads is 8.42 km per 100 km2 which is the lowest among all the districts except Lahaul and Spiti (4.97 km. per 100 km2) and far less than the state average (51.92 km. per 100 km2). The destination is located in a cold desert condition where life is too difficult, and due to that situation, the district is very sparsely populated. The area is under the tribal region of the state. Thus, the road density per 1000 persons is much better than the rest of the state of Himachal Pradesh. The climate of high-altitude destinations has many effects on tourist arrivals. The density of un-metalled roads which constitutes 35% of the total road length, per 100 km2 and per1000 persons was 4.50 and 3.42, respectively. Therefore, the seasonality in transport is a challenge for planners because the road is open for transportation during the summers (April to June) and autumn months of September–October. The destinations are not easily accessible for tourists during rainy and winter seasons due to poor maintenance of major and rural roads and highly inadequate resources such as snow removing machines with the PWD and BRO. No doubt, NH-22 provides good connectivity, but it also lacks a desirable level of maintenance, especially in geological weaker sections near Karcham and Powari of Kinnaur district, where landslides and shooting stone is a perennial problem during snowfall and rainfall. These two points are serious threats to tourism as many causalities have happened here in the recent past, which has jolted the confidence of prospective travelers.

Metalled and un-metalled road density as on 31.10.2020

Area of Kinnaur 6401 sq. km. and Himachal Pradesh 55,673 sq. km

Population of Kinnaur 84,121 persons and Himachal Pradesh 6,864,502 persons (Census, 2011)

Source: http://hppwd.hp.gov.in/district-wise-metalled-and-unmetalled-road-density

Public Works Department, H.P

Road condition of major travel route: NH-22

As discussed, NH-22 is the Kinnaur district’s lifeline and the major route that connects Kinnaur with major centers such as Shimla, Chandigarh, and Delhi. The road is four-lane up to Kalka, and further, the construction by NHAI (National Highway Authority, India) is in progress to convert it into a four-lane till Shimla, the capital of the state. Kalka-Solan’s four-lane section has been operationalized, and work is in progress between Solan and Shimla. The expansion of roads would also provide easy accessibility to tourist destinations and increase the bear of vehicles. Moreover, such types of transport deliver a comfortable drive and escape from road accidents. The authors, through field visits, examined that the bad condition of NH-22 starts from Narkanda. About 24.15 km of the road is partially damaged between Narkanda to Jeori (Kinnaur’s entry point). The surveying team has identified 11 big damaged patches in Kinnaur. Thus, the road is not only partially or fully damaged, but also the length of each patch is more than 3 km. which would be problematic and unsafe for travelers in such topography. In Kinnaur, around 43.45 percent of the total road length is not in good condition. The total length surveyed through field visits is 262.1 km. out of which 113.92 km of road length is damaged (Table ​ (Table6). 6 ). The government should take stringent steps to check and ensure that it is well maintained by entrusted agencies, the PWD, and BRO because the road is significant for travel and strategically important. (Fig.  5 ).

Road condition of major travel routes in Kinnaur Source : Area examined by the author through GPS, 2016

Length wise road condition of major travel routes in Kinnaur.

Source: Area examined by the author through GPS, 2016

Figure  6 shows the major travel routes and their condition in terms of good, partially damaged and fully damaged. Undoubtedly, the vulnerable physiography and hazard susceptibility produce challenges for road construction and its maintenance in highland areas of the state. It is also the main cause of low road density and damaged roads. According to the Geological Survey of India (GSI), the state is broadly divided into 5 physiographic zones—Alluvial plain, Shivalik foothills, Lesser Himalayas, Central, and Trans Himalayas. National Highway-22 passes through four physiographic zones except for Trans Himalayas; in the alluvial plain and Shivalik foothills, roads construction and maintenance are easy, but it becomes harder as altitude increases. Further, NH-22 becomes narrow from the point it enters in lesser and central Himalayas. Tough terrain and snowfall damage the roads frequently. On the route to Reckong-Peo, Jeori to Wangtu, a 12.41 km road was partially damaged.

Condition of major travel routes in Kinnaur

Further, the road was fully damaged with a longer patch of 17.99 km. length between Wangtu to Karcham, the single longest poorly maintained patch in Kinnaur. The road was also not good from Karcham to Reckong-Peo; it had just 9.56 km. in good condition out of the total length of 23 km. The condition of a 20 km road between Sangla to Chitkul was also not good, and it was partially damaged. These parts were damaged either by landslide activities or due to blasting for the construction of hydropower tunnels. Landslides are the main natural hazard faced by Kinnaur, and from 1971 to 2009, the number of such events was 123, constituting 13.38 percent of the total landslides events in Himachal Pradesh (Kahlon et al., 2014 ). The Kalka–Shimla–Kinnaur highway is one of the most vulnerable stretches. Kinnaur has the highest landslide events after Shimla and Solan, with several landslide casualties (Bilham, 2004 ; Chandel, 2015 ; Chandra, 1992 ). The Parwanoo–Solan–Shimla section of NH-22 falls under ‘Zone-A’, which is tectonically active, characterized by steep slopes, high relief, and very immature topography (Sharma & Kumar, 2008 ). The structure of rocks in Kinnaur is the Granitoids group (Granite rocks) which is highly jointed. The rocks have been disintegrated into small or large blocks by the freeze and thaw process of weathering. The Hindustan-Tibet road between Rampur to Khab (Zone-C) traverses through sparsely vegetated steep slopes consisting of highly jointed and weathered rocks belonging to the Wangtu Gneissic Complex, the Vaikrita Group, and the Haimanta Group (Gupta & Shah, 2008 ; Sharma, 1977 ; Tewari et al., 1978 ). Besides the range of lithology, this area has glacial, glacio-fluvial, fluvial, and paleo-slide material of Quaternary origin (Kahlon et al., 2014 ) and several faults such as Karcham and Vaikrita thrusts. Landslide activity in this area results from the weak structure and steep slopes. A Granitoids tectonically occurring as a window have been identified near the Jeori to Wangtu plausible cause of the landslide. Furthermore, hydropower projects are also found as another major cause of NH-22 destruction. NH-22 is buildup along the Satluj River, and there are 9 big hydel projects of more than 7500 MW running in the basin of this river from Khab to Bhakra. The river passes from tunnels for power generation, which caused major two-way impacts on the basin: change in the shape of the river and loosening of the rock strata. There are 13 small and big hydropower plants working in the Kinnaur, and most of them are along the NH-22 on the Sutlej River (Fig.  7 ). Considering the size of the area, it is a quite big number of power plants, and it has been observed during the field visit that the construction process of hydropower projects and tunnels greatly harms the roads. The road from Jeori to Powari, in particular, faces serious issues regarding road conditions created by construction activities. There are two major power projects of Himachal Pradesh working over this damaged patch; Nathpa-Jhakri HEP (1500 MW) and Karcham-Wangtu HEP (1000 MW). A huge landslide near Urni (Fig.  8 ) village was the result of the Karcham-Wangtu project. The rocks near this place were found sliding intermittently for more than 2 years. This landslide has blocked the straight path of 3 km to cross the Karcham-Wangtu project area but was diverted via Urni hill and lengthened to near about 14 km. According to Gupta and Shah ( 2008 ), the increased frequency of landslides is attributed to a shift in climatic pattern and escalating anthropogenic activity, as evident from the growing population, increased road length, and alteration in land use. The road condition between Karcham–Sangla–Chitkul was quite better than Jeori–Karcham. Out of the total stretch of 43 km., about 4.60 km. length of the road was fully damaged, while 21.71 km. was found partially damaged. Chitkul is the last village toward Tibet, and it is the main attraction for tourists. The road is also used by ITBP (India-Tibet Border Police) to reach the Hindustan-Tibet border, but tourists are restricted to Chitkul village. The weathering process has disjointed rocks since the area receives heavy snowfall during the winter season. Large boulders of rocks can be seen on both sides of the road. Highly fractured landscapes offer good tourism products, especially to earth scientists. On the route to Kalpa–ReckongPeo–Powari, the condition of the road was very good due to the construction of a new highway named 505-A, which is a junction with NH-5 at Powari. On the way to Nako–Sumdo, a stretch between Jhakri and Spillow was announced on a 24/7 landslide alert. Furthermore, the damaged patch of around 6.47 km. Nako to Malling was also on landslide alert for more than a year. Landslide in the Malling area (Zone-D) results from highly jointed, fractured, and weathered schist and high water discharge due to snowmelt during summer and alteration of slopes for transport networks (Kahlon et al., 2014 ).

Hydropower projects in Kinnaur.

Source: Field survey, 2016

Landslides on NH-22 (Urni-left side, Malling- right side).

Transport services

The major component of tourist activity must necessarily be an element of transportation. There is a requirement of quick, comfortable, safe and fairly cheap transport facilities at the suitability of tourists. In the hilly areas, buses are the preferred budget mode of transportation to and within destinations. It is considered a lifeline in Kinnaur, where other transportations means are negligible. According to the Registration and Licensing Authorities of Himachal Pradesh, 1136 vehicles were registered in 2018–19.

Figure  9  demonstrates the major route of bus services from Reckong-Peo, the district headquarters, to tourist destinations. The highest bus services operate between Reckong-Peo to Kalpa, a major tourist destination in Kinnaur. It is also the former headquarter of the district that attracts many tourists every year. On the route of Sangla–Chitkul, the ‘last village toward Tibet’, the service is not good, and only 2 buses run directly to Chitkul. Likewise, there are just 2 buses that ply between Nako and district headquarters, Reckong-Peo; one of those goes beyond Nako up to Kaza. To enter the Nako, foreign tourists need an Inner-Line Permit (ILP) from the police check post located at village Poo. Tourists can apply it online and use offline options (SDM office, Reckong-Peo) for the permits. The bus services to Shimla and Chandigarh provide good connectivity, and their number is higher on these routes. However, beyond Shimla toward Reckong-Peo, bus frequency declined significantly. Only 1 bus runs between Reckong-Peo and Delhi, the national capital. One bus connects it with Haridwar, the religious place of Hindus. Although a sharp increase can be observed in the number of personal and hired vehicles, still there is a need for more public transport modes, especially buses because the destination has very rough topography and at such terrain, everyone could not drive safely. Therefore, a lot of accidents have been noticed. Even HRTC also encountered major accidents sometimes. Therefore, improvement of road condition is necessary with proper signboards and deployment of JCB machines, etc., as a precautionary step in landslide zones to escape the mishaps and make road plying worthy 24 × 7. Strengthening public transport also becomes necessary since many prospective visitors may not afford to travel by personnel vehicle or taxi.

Major routes of bus services from and to Kinnaur.

Source: Based on data given by HPRTC, 2016

Mode of travel chosen by tourists

Figure  10  shows that many domestic tourists have used their personnel car/taxi to reach Kinnaur (54.59 percent). The destination offers an ultimate experience of driving, and many national tourists reach in groups here to enjoy the drive-in Himalayan landscape. Besides, mostly foreigners preferred public transport, i.e., buses. Many foreign visitors are from Israel, and they aim to spend minimum currency on travel; hence, they prefer public transport. Tourists from Australia, Russia, and Germany preferred bikes (26.03 percent). They came in groups and enjoyed biking through one of the ‘deadliest roads of the world’. There are a lot of opportunities for adventure tourism in Kinnaur, and the response of foreign tourists is more positive than domestic tourists.

Mode of transport used by tourists to visit Kinnaur.

Fuel station

The modern world runs on wheels; therefore, to develop the tourism industry, there is a need for the good availability of fuel stations. At such tourist destinations, the importance is more significant where sites are so remote. Bikers carry extra fuel in cans or other containers in the study area because filling stations are few and far. In 2018–19, 6 outlets were available; 4 were run by Indian Oil Corporation (IOC) and 2 by Hindustan Petroleum Corporation ( https://himachalservices.nic.in/economics/pdf/StatisticalAbstract_2018_19.pdf ). Most tourists fill the fuel tank of their vehicles at Tapri fuel station because the next filling station is 227 Kilometers away at Kaza on this route. Besides, the district headquarter, Reckong-Peo, has many options. Hence, there is a need to increase the number of fuel outlets. This is one area where urgent improvement is needed for the sustainable growth of tourism. Poo, Nako, and Sangla would be appropriate locations for additional development of filling stations.

Descriptive analysis

Skewness and kurtosis values of items were examined to assess the normality of data distribution. According to the criteria of Chou and Bentler ( 1995 ), the items with skewness and kurtosis greater than 3.0 point would be considered extreme. The skewness and the kurtosis statistics are 0.699 and − 1.265, respectively, which shows that there are no items that fell outside the ranges, implying that all the study items are reasonably free from skewness and kurtosis (Table ​ (Table7 7 ).

Descriptive statistics for transport facilities items

Responses were based on a five-point Likert scale

Tourist’s perception of transport facilities

Many renowned tourist destinations face pollution problems worldwide, and it is challenging to manage them. Moreover, people who live in metro cities explore such destinations which are free from pollution. Further, in the context of the study area, which is free from those kinds of problems (pollution and huge crowd) and offers a serene environment is also reflected in tourists’ agreement which has been positively favorable in concerns to ‘air pollution’ (Mean = 4.19) and ‘noise pollution’ (Mean = 4.05). Considering the observation of tourists, the mean values of items such as ‘road network and linkages’ (Mean = 2.44), ‘auto-mechanic facility’ (Mean = 2.58), ‘filling station’ (Mean = 2.73), and availability of public parking spaces (Mean = 2.99) indicate their poor status ( Table ​ Table8, 8 , Fig.  11 ). Also, tourists were satisfied with the work of BRO (Border Road Organization), acknowledging their good efforts in the tough terrain located in the high Himalayas. Also, tourists suggested that there should be more road signs at destinations, especially in English. Techniques should be improved, and high-tech machines should be used to clear the blockage of roads during snowfall or landslides as traveling through these roads takes a lot of time and usually creates  jams.

Tourist’s perception about transport facilities

Responses were based on a five-point Likert scale, 2016

Difference in tourist’s perception by destinations.

Source: Responses were based on a five-point Likert scale, 2016

Correlation between tourist’s perception by destination

Pearson correlation has been calculated to find out any relation between the destinations. The absolute value of correlation describes the magnitude of the correlation; the greater the value, the stronger the correlation. The correlation coefficients of the selected destinations are found to be very strong. The correlation between the destinations of Kalpa and Nako is 0.787 due to their diverse landscape (Table 9 ). Kalpa is placed among apple orchards and pine greenery at the base of the Kinner Kailash Mountain. The road setup is pretty good compared with other destinations. Whereas, Nako is situated in the cold desert near the Indo-China border in the Trans-Himalayan region. The road mostly remains broken or damaged due to the mountain’s poor structure. It is very difficult to construct a good road network on such terrain; however, the maintenance of the road is lookout by the Border Road Organization (BRO) timely.

Correlation in tourist’s perception by destinations

*Correlation is significant at the 0.05 level (2-tailed)

**Correlation is significant at the 0.01 level (2-tailed)

The difference in tourist’s perception by destination

An analysis of variance (ANOVA) at a significance level of 0.05 has been used to determine the statistically significant differences between selected destinations such as Chitkul, Kalpa, and Nako. The selected destinations are located nearby river banks, i.e., Nako (Spiti River), Kalpa (Sutlej River), and Chitkul (Baspa River), and have almost similar terrain. The significant value ( P value = 0.487) is above the significance level of 0.05, implying that no significant difference has been found in tourist perceptions among the items of transport facilities (Table ​ (Table10 10 ).

Differences in tourist’s perception toward transport facilities (ANOVA)

*Significant at the level of 0.05 level

Tukey test, also called Tukey’s Honest Significant Difference Test, was applied to the tourist’s perception of transport facilities. Tukey test is the preferred test for conducting post hoc tests on a one-way ANOVA. This test compares all the possible pairs of means. From the results of ANOVA, no significant difference has been found in tourist perceptions among the items of transport facilities. Table 11  shows the differences, if any, among different groups. No statistically significant difference has been found between the destinations. However, the results suggest that the tourists who visited Kalpa ( P value = 0.460) perceived significantly higher agreements in selected destinations. The possible reason may be that Kalpa is the major destination and former district headquarters and has many facilities to fulfill the needs of travelers. It is the center of all tourism activities that extend services to the whole of Kinnaur.

Differences in tourist’s perception by destinations (Tukey HSD)

* Mean difference is significant at the level of 0.05 level

Research acknowledges the need for robust transport infrastructure in a successful strategy for developing tourism. The role of transport in tourism development has been analyzed by explaining both exogenous and endogenous parameters of the system. Transport has much strategic importance due to its specific location, i.e., cold desert and the international border with Tibet. The government of Himachal Pradesh needs to make all-weather roads with appropriate maintenance. The roads are narrow at certain locations and need to widen, preferably two or more lanes. However, the physiography of the high altitude and fractured rocks due to weathering and human-induced factors creates many problems that make the connectivity paralyzed during the rainy and winter season. On the positive side, the Government of Himachal Pradesh has pushed for green transportation to conserve the serene environment of the cold desert destinations. More recently, an attempt has been made to shift public transportation from hydrocarbon fuel to electric buses or taxies on experimental bases. Himachal Roads Transport Corporation (HRTC) recently launched an electric bus service on the Mandi Kullu–Manali and Rohtang Pass routes, and the results are quite positive. It was the first hill state in the country that run electric buses in the country ( https://timesofindia.indiatimes.com/city/shimla/himachal-pradesh-first-hill-state-in-country-to-run-electric-buses/articleshow/60787007.cms ). The encouraging results may push for electric vehicles in a couple of years in both public, and private sector provided re-charging infrastructure is strengthened to an appropriate level. Transport connectivity has affected various systems of the cold desert, not only tourism but also health, education, commerce, and trade. Besides, the total number of international tourist arrivals declined almost 83 percent in 2020 compared with the previous year (UNWTO, 2021 ). Coronavirus effects have shattered the tourism economy of the study area. During the pandemic, the hotel association of Kinnaur announced to close its doors to tourists, while the majority of the population engaged in the tourism industry for their livelihood. Because of the fears of the third wave, tourists are not expected to venture out for the hills anytime soon. However, taking advantage of the absence of tourist vehicles, the road infrastructure has been strengthened but needs to do more. It would significantly increase the tourist inflow and economic development at high-altitude destinations of the study area shortly if good road transport infrastructure is put in place.

Recommendations

• There is a need for proper signboards and reflectors on roadsides showing the way forward of roads to major destinations with proper marking and important information about the place at a regular distance.
• The administration could install boulder binding/catching nets along the hill slopes for those sites which frequently face landslides. The major portion of Jeori–Karcham–Wangtoo–Reckong-Peo road has been found fully damaged, which could be bound by slope infilling and earth embankment along the hill slopes.
• Boulder gathering trenches are used as the static method to reduce the impact of rock falls. The patches between Sangla-Chitkul and near Powari and Malling are affected by rockfalls; accordingly, boulder gathering trenches are efficient in absorbing the kinetic energy of falling rocks, which could be applied to those areas.
• To maintain the road in a highly jointed, fractured, and weathered schist, with high water discharge due to snowmelt during summer (landslide in the Malling area), NHAI can install the prefabricated channels by converting the water flow from cracks to the surface flow. Prefabricated channels not only slow down the process of crack expansion, but it is also helpful to fulfill the need for fresh water for travelers and residents.
• The hydropower projects are also one of the major causes of NH-22 destruction due to blasting during the tunneling process and instilling the rocks leading to rockslides or landslides. The authorities ignored the maintenance of roads destructed by rockfalls caused by the tunneling process. Therefore, NHAI should penalize the builders involved in tunneling for their lack of maintenance around the tunnels.
• To maintain the road for the future, the authorities can use reinforcement measures to overcome the block disintegration in this area. They should use controlled demolition during the tunneling process. The reinforcement measures include joining loose rocks with metal rock nails and anchors. This method can easily control further rock disintegration in the affected area. The concrete insertion also includes reinforcement measures for giving support to the rocks.
• The destination has a highly inadequate frequency of public transport vehicles from Shimla, Chandigarh, and Delhi. Due to rough topography, everyone could not drive safely on such terrain; therefore, more public transport vehicles like mini busses are needed. Moreover, there is a need to promote eco-friendly means of transportation and accessibility to all the tourist destinations, such as electric vehicles; electric charging points can be easily developed in the area by using abundantly available solar and hydro energy.
• There is a lot of demand for bikes and bicycles for rent, encouraging local youths to start providing this facility at prominent destinations; the government should act as a facilitator by arranging low-interest rate loans.
• Tourists traveling through private vehicles felt concerned about the absence of an adequate number of fuel stations and mechanics. Additional fuel stations should be developed at Powari and Nako to fulfill travelers’ needs.

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Sustainable Transport and Tourism Destinations: Volume 13

Table of contents

Introduction.

The relationship between transport and tourism is very complex to analyze due to mutual causality. Nonetheless, it is worthwhile investigating it, especially paying attention to sustainable mobility, due to the need to minimize the externalities of transport, which can otherwise reduce the attractiveness of a tourism destination. To this aim, after a brief overview of different analytical frameworks, this chapter focuses on transport at destination and sustainable mobility options, such as local public transport (PT) and active modes (walking and cycling). In particular, it provides some insights from the literature about both tourists’ mobility patterns — by taking into account both psychological aspects of tourism experience and the localization of the amenities — and specific modal choices, more focused on the impact of transport on the environment. It then concludes by presenting short summaries of each chapter of the book, in order to provide an overview of the investigated topics, which are dealing with both geographical (islands, coastal areas, natural areas) and management/administration (technical solutions, PT provision, transport demand) issues.

Changes of Transport in Cross-Border Tourist Regions in the Polish–Slovak Borderland: An (Un)Sustainable Development?

The Polish–Slovak borderland is a mountainous area with extraordinary natural conditions for tourism development. The main aim of this chapter is to analyze theoretical aspects of a relationship between transport and tourism and to assess selected changes in cross-border transport that have influenced tourism in Polish–Slovak tourism regions. We have tried to answer the questions on changes in transport infrastructure (based on the analysis of the cross-border projects) and public transport (based on the analysis of timetables of the cross-border public transport connections) in the borderland during the last 30 years and to answer the question whether these changes are in accordance with the sustainable development goals. The Polish–Slovak border is seen as a barrier to transport. The increasing cross-border movement of people and goods through Polish–Slovak border after 1989 required the opening of new border crossings and the construction of new cross-border transport infrastructure. Investments to the road infrastructure have led to using of individual automobile transport. Public transport is currently of marginal importance in cross-border transport. The three cross-border rail lines are in poor technical condition, and plans for their modernization are uncertain. Bus transport has been limited on two tourist-oriented lines in the central part of the borderland. In terms of the structure of the use of means of transport, therefore, no change in trends should be expected and most of the incoming people will continue to cross the Polish–Slovak border by their own means of communication. What is worrying, in the future, in the absence of modernization of the railway infrastructure and no organizational measures, there will be a further decline in the importance of public transport in relation to individual road transport.

Tourist Sustainable Mobility at the Destination. A Case Study of a Polish Conurbation

Much attention has of late been paid to the issue of tourist sustainable ­mobility at the destination. This issue takes on particular significance in big cities, which, prior to the outbreak of the pandemic, saw considerable increases in visitor numbers. The aim of this chapter, which draws on the case study method, is to explore the question of how foreign tourists move around in a Polish conurbation, known as Tri-city. Made up of three cities – Gdansk, Sopot, and Gdynia – it is one of Poland’s most popular tourist destinations. Crucially, in Tri-city all major tourist attractions and facilities are dispersed over a wide area, which makes it particularly well suited to researching visitor mobility patterns. The case study that forms the core of this chapter relies mainly on a paper-and-pencil questionnaire survey conducted among foreign tourists visiting Tri-city in January 2020 as well as on direct observation of reality. It turned out that walking was a preferred way of moving around Tri-city for most foreigners. The findings indicate, too, that young female visitors used public transport more often than older women and all men regardless of age. Furthermore, tourists with a university education more often opted for public transport than those without a degree, and visitors who lived in urban areas used public transport more often than those living in the countryside. The chapter concludes by ­summarizing the argument and drawing practical lessons for municipal authorities interested in facilitating tourist sustainability in their cities.

Walking and Sustainable Tourism: “Streetsadvisor.” A Stated Preference GIS-Based Methodology for Estimating Tourist Walking Satisfaction in Rome

This chapter proposes a methodology to develop a tool aimed at helping tourists moving sustainably in Rome, focusing on the “last mile” of their transport experience, that is, walking trips. The methodology consists of the development of a stated preference survey, where tourists’ preferences are elicited with respect to alternative configurations of walking paths. This is performed by taking into consideration path accessibility, interference with other modes of transport, and thermal comfort aspects. Besides, georeferenced data are collected and systematized with the overall aim to create a geographical information system of the first municipality of Rome with useful information to evaluate the status of the walking network. The results of the analysis help to understand the relevant factors affecting tourists’ walking behavior. Additionally, the chapter provides the preliminary considerations needed for the definition of a “tourist walking satisfaction indicator” related to their walking experience with two aims: first, it provides useful information for policy-makers on how to design and manage walking networks; second, it provides a framework for a tourist traveler information system (a “StreetsAdvisor”) that can guide them in the city on the base of their heterogeneous preferences.

Environmental Sustainability of City Sightseeing Cruises: A Case Study on Battery-Powered Electric Boats in Berlin, Germany

With continued growth in tourism, demand for guided local excursions, sightseeing, and entertainment has increased rapidly, particularly in European tourist destinations cities. Many touristic sights can often be viewed best from the water. Operators offer a variety of sightseeing cruises on motor barges along rivers, canals, lakefronts, or ports. In many tourist destination cities and around urban heritage sites, however, increasing boat traffic and the associated air pollution from diesel-powered engines have become a local environmental concern. Based on complaints from residents and visitors, several cities have already announced plans for (mandatory) tourist boat emission reductions. Today, electric mobility offers alternative options for safely and conveniently powering commercial tourist boats, that may contribute to mutually beneficial solutions for local operators, tourist visitors, and residents alike. However, the technology is still expensive and new businesses may also face considerable challenges when entering established local competitive tourism markets. Focusing on the local waterways of the city of Berlin, Germany, the authors have conducted a local case study, including interviews with several operators of (electric) tour boats, as well as an initial empirical survey of their tourist customers. The authors point out the viewpoints of the various stakeholders, identify opportunities, discuss constraints, and offer policy recommendations with a view to enhance the sustainability of waterborne transport in tourist destination cities.

Sustainable Tourism Mobility in Malta: Encouraging a Shift in Tourist Travel Behavior Through an Innovative Smartphone App for Trip Planning

Malta has long been a tourist destination with visitors totaling 2.6 million in 2018. A 2013 survey by the Malta Tourism Authority found that 22% of tourists opted for a rental car during their stay, whereas 76% chose public transport to meet their travel needs. In recent years, the modernization of the bus fleet, improved information provision, and the introduction of a ferry service in the Valletta harbors, have contributed to the increased appeal of public transport. However, the increase in independent tourists might give rise to an increase in the rentals of individual cars. This is a concern given Malta’s high car ownership, and its ever-increasing congestion problem. As part of the CIVITAS DESTINATIONS Project, focused on tourist sustainable mobility, the University of Malta developed a Tourist Mobility smartphone application: MyMaltaPlan. The app enables tourists to plan trips and schedule itineraries between touristic sites. The app, which was launched in the summer of 2019, aims to encourage a shift toward greener travel behavior. A survey was conducted with tourists to understand current tourist travel behavior, and tourists’ use of smartphone or web applications for trip planning. The vast majority of visitors own a smartphone and use it on holiday to plan, access, or book transport. To test the app’s functionalities, a focus group was held with a group of volunteers who shared their experiences in a group discussion. Participants appreciated the automatically created itinerary but noted that to truly promote sustainable mobility, the app should be able to provide the full picture of available alternatives.

Tourists, Residents, and Sustainable Mobility in Islands: The Case of Ischia (Italy)

While tourism is mostly considered a crucial driver for local development, its impact in terms of sustainability and attractiveness of local destinations must also be taken into account. This is especially true for small islands, where tourism may determine detrimental effects in the long term to the limited space and resources. The “sustainable tourism” approach considers this phenomenon and proposes possible solutions to problems such as the loss of public space, waste management, energy and water over-consumption, traffic congestion, air, water, and visual pollution. This chapter presents and discusses the results of a survey that has been carried out in Ischia, a small Mediterranean island located in the Gulf of Naples in order to explore the propensity toward sustainable mobility of both tourists and residents. In particular, the mobility patterns of the respondents have been deeply investigated both at home (domestic behavior) and on holiday (tourist behavior). The results suggest that the promotion of a higher level of cooperation among different stakeholders and local governments is of paramount importance in order to achieve sustainable tourism on islands. This may also generate important effects in terms of destination attractiveness.

Sources of Data to Tackle the Challenges of Public Transport Provision in Seasonal Tourist Destinations

Tourism reconfigures the metropolitan dynamics and the patterns of use of the urban systems. The seasonal nature of tourism produces an impact on the urban hierarchies, since it affects the labor, residential, and recreational markets. As a result, people move to and in the destination and it challenges the supply of sustainable modes of transport such as public transport. This research is set within the context of three demanding challenges that tourist destinations need to face-up: to increase environmental sustainability, to enhance destination competitiveness, and finally to assure quality and comfort of public transport services for the local resident population. Camp de Tarragona region, where Costa Daurada (one of the most important Spanish tourist brands) is located, is analyzed to illustrate how different data sources can aid to confront the aforementioned challenges. Given that seasonality is a dynamic phenomenon, suitable data should be flexible in terms of its time framework. To this end data from smart travel cards provided by the consortium that manages the public transport system in the region has been analyzed. Data unveiled the impact of seasonality on the evolution of demand throughout the year, the type of transport tickets used, or changes occurred in the geographical distribution of the mobility Alternative data sources such as surveys and passive mobile positioning data have also been examined, and their pros and cons have been addressed.

Validity of Repeated Applications of TDM Measures Toward Sustainable Development in Tourism Destinations: A Case Study on Managing Peak Hourly Congested Traffic After the Formula 1 World Championship Japanese Grand Prix at Suzuka

The Formula 1 World Championship Japanese Grand Prix (denoted SUZUKA F1) has been held in Suzuka city in the Mie Prefecture of Japan every year since 2009. This event gathers a large number of motor racing fans around the circuit. The total number of attendees over three days amounts to more than 200,000. Reducing the traffic congestion around expressway interchanges (ICs) and decreasing the departure times of return traffic during peak hours are of critical importance not only for short-term transportation demand management (TDM) measures but also for sustainable development ­management in Suzuka city as a tourism destination. The chapter starts a brief review of previous studies on the TDM measures to identify the current trends in both their methodological and problem-oriented approaches and then introduces our approach called the area marketing and management approach (AMMA) relating to an issue on how we can pursue the sustainable development in tourism destinations. Based on the concept of the AMMA, a set of the Smart TDM measures are proposed involving the development of the application software that will be used as an interactive communication tool. The validity of the repeated applications of the Smart TDM measures is empirically examined by assessing the most recent experiences at the SUZUKA F1 until 2017. The limitations to what the current Smart TDM measures can do are finally discussed to improve the smartness of these TDM measures to contribute to the sustainable area development.

Cycle Tourism as a Driver for a Sustainable Local Development. The Case of a Natural Tourist Destination in a North-Western Area of Italy

Cycle tourism is considered as a trendy opportunity of local development that should be taken into consideration by several destinations to (further) increase tourism according to the sustainable development approach. It is a broad and complex phenomenon that involves various social and economic actors. Cycle tourists are looking for new and deep experiences to better benefit from the local identities and the uniqueness of the landscape of a territory. Cycle tourism gives sustainable access to environmental and cultural resources of territories often neglected. Despite its evident potentialities, the lack of studies represents a drawback that could compromise the local development. The aim of this chapter is first to describe the characteristics of this form of tourism both in terms of its contribution to the sustainable development and of demand and supply features. Second, the study focuses on an Italian area that is strongly investing in the development of this form of mobility: an area called “Insubria,” which is located in the Lombardy region, near the Swiss border and includes, as main cities, Varese and Como. The work explores whether the supply of the tourism product in this area is aligned with the current and future demand trends of cycling and tourism. The analysis ends with some suggestions about possible improvements in the area and for the long-term industry competitiveness.

Proposals for Sustainable Transport in Natural Areas: A Case Study of Teide National Park

The exponential growth in the number of visitors and the mass-tourism mobility patterns in natural areas are causing serious issues such as traffic congestion, crowding in car parks, pollution, high noise levels, and traffic accidents. In order to redress this situation, demand management policies that propose more sustainable transportation systems are crucial. In this chapter, the authors summarize extensive research carried out in Teide National Park (Canary Islands, Spain), the most visited national park in Spain, one of the most visited in the world, and a clear example of a natural area under pressure from mass tourism. The authors present the current situation of the natural site and three scientific contributions based on a survey combining revealed and stated preferences that analyzes visitor preferences with regard to the use of sustainable transportation systems. The first study analyzes visitors’ preferences regarding the implementation of a public bicycle-sharing system. The second study explores visitors’ willingness to pay to reduce the environmental impact of their visit and the potential implementation of a shuttle-bus service. The third study investigates the recreational economic value of the site. The chapter provides useful information for decision-makers who need to address problems associated with the unsustainable visitor mobility and reports results that can be extrapolated to other natural parks with similar characteristics and high inflow of tourists.

Conclusions

This concluding chapter is explicitly comparative in orientation. It analytically draws the similarities and the heterogeneities of the themes, frameworks, and policies introduced and discussed in the previous chapters. It also highlights the new contributions that emerge from the chapters for both scholars and practitioners. The main issues that a conjoint perusal of the various contributions to the book allow to highlight are: (a) the role played by public policies in fostering solutions that aim at increasing the sustainability of transport in tourist destinations; (b) the role of collaboration among stakeholders and of networks for the implementation of sustainable transport policies and strategies; (c) the importance of the availability of information both on the supply side and on the demand side of the tourist market; and (d) the importance of considering the trends of transport demand of tourists.

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Transport and tourism, an inseparable alliance: the importance of the transport sector

• Published: 16 February 2022
• Volume 57 , pages 465–480, ( 2023 )

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• Fabrizio Antolini   ORCID: orcid.org/0000-0002-3112-524X 1  

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Tourism is based on the physical movement of people, and it is therefore of fundamental importance to have a transport system that is functional and that can facilitate travel to tourist destinations. However, transport is also important in itself because it ensures the freedom of movement for people and reduces social distances, in turn promoting growth and economic development. For this reason, it is necessary to design a transport and infrastructure system that is functional and promotes the accessibility of tourist destinations. Nodal analysis, applied to tourism flows in Italy, highlights the contradiction of a transport system which has enforced the policy of large hubs, in a country where there are many tourist destinations. Nevertheless, the nodal analysis methodology applied to the tourist flows show the need to strengthen the connection between the Adriatic and Tyrrhenian coasts, as well as the connection with some regions of the south, such as the Basilicata region. Moreover, it would be appropriate to enhance the railway line on the Adriatic dorsal in order to reach some tourist regions with greater ease. In the same way, the geographical configuration of Italy makes an enhancement of the ports highly desirable, creating an intermodal system.

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Antolini, F. Transport and tourism, an inseparable alliance: the importance of the transport sector. Qual Quant 57 (Suppl 3), 465–480 (2023). https://doi.org/10.1007/s11135-022-01335-7

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Earth Day: What is it, when is it and why is it important?

Planet v plastics ... Earth Day 2024 is calling for a 60% reduction in plastic production by 2040. Image:  Naja Bertolt Jens/Unsplash

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Hanh nguyen.

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• Earth Day takes place every year on 22 April and is one of the biggest environmental protest movements on the planet.
• The theme of Earth Day this year is 'Planet vs. Plastics' - campaigners are calling for a 60% reduction in the production of plastics by 2040.
• The World Economic Forum's Global Risks Report 2024 finds that environmental risks make up half of the top 10 risks over the next 10 years.

“Good evening, a unique day in American history is ending. A day set aside for a nationwide outpouring of mankind seeking its own survival.”

Those were the words of US TV presenter Walter Cronkite as he described the aftermath of the first Earth Day back in 1970.

Here’s what you need to know about Earth Day in 2024.

What is Earth Day and what is the theme in 2024?

Earth Day is an international day devoted to our planet. It draws attention to the environment and promotes conservation and sustainability. Each year on 22 April, around 1 billion people around the world take action to raise awareness of the climate crisis and bring about behavioural change to protect the environment.

Participation in Earth Day can take many forms, including small home or classroom projects like planting a herb garden or picking up litter. People also volunteer to plant trees, join other ecological initiatives or take part in street protests about climate change and environmental degradation.

Official Earth Day campaigns and projects aim to increase environmental literacy and bring together like-minded people or groups to address issues such as deforestation, biodiversity loss and other challenges .

The global theme for this year's Earth Day is ' Planet vs. Plastics ', which recognizes the threat plastics pose to human health and with campaigners demanding a 60% reduction in the production of plastics by 2040.

From 23 to 29 April 2024, governments and NGOs from around the world will gather in Ottawa to continue negotiating the terms of the United Nations Global Plastic Treaty .

How did Earth Day begin?

Millions of people took to the streets of US cities and towns on 22 April 1970 in mass protests over the damage being done to the planet and its resources. Amid the demonstrations, protesters brought New York City’s usually bustling Fifth Avenue to a halt, while students in Boston held a “die-in” at Logan Airport. The environmental impact of the post-war consumer boom was beginning to be felt at that time. Oil spills, factory pollution and other ecological threats were on the rise, with little if any legislation in place to prevent them.

The protests brought together people from all walks of American life – accounting for about 10% of the US population – to demonstrate and voice their demands for sustainable change. The Earth Day website calls it the birth of the modern environmental movement.

What led to the street protests in 1970?

Concerned about increasing levels of unchecked environmental destruction, Junior Senator Gaylord Nelson of Wisconsin suggested a series of “teach-ins” on university campuses across the US in 1969 to raise awareness of environmental threats. Nelson was joined by Congressman Pete McCloskey and activist Denis Hayes to organize the teach-ins, but the group soon recognized an opportunity to broaden the event’s appeal beyond student populations.

The newly named Earth Day protest events attracted national media attention and support from around 20 million Americans across age and political spectrums, occupations and income groups.

What did the protests achieve?

The Earth Day demonstrations left an indelible mark on US policy. By the end of 1970, the US Environmental Protection Agency came into being and a stream of laws followed to help protect the environment . These included the National Environmental Education Act, the Occupational Safety and Health Act and the Clean Air Act. Further legislation was soon introduced to protect water quality and endangered species, and to control the use of harmful chemicals and pesticides.

When did Earth Day go global?

Earth Day went beyond the US in 1990. Around 200 million people from 141 countries joined efforts to boost recycling around the world that year, paving the way for the 1992 United Nations Conference on Environment and Development in Rio de Janeiro, Brazil.

Climate change poses an urgent threat demanding decisive action. Communities around the world are already experiencing increased climate impacts, from droughts to floods to rising seas. The World Economic Forum's Global Risks Report continues to rank these environmental threats at the top of the list.

To limit global temperature rise to well below 2°C and as close as possible to 1.5°C above pre-industrial levels, it is essential that businesses, policy-makers, and civil society advance comprehensive near- and long-term climate actions in line with the goals of the Paris Agreement on climate change.

The World Economic Forum's Climate Initiative supports the scaling and acceleration of global climate action through public and private-sector collaboration. The Initiative works across several workstreams to develop and implement inclusive and ambitious solutions.

This includes the Alliance of CEO Climate Leaders, a global network of business leaders from various industries developing cost-effective solutions to transitioning to a low-carbon, climate-resilient economy. CEOs use their position and influence with policy-makers and corporate partners to accelerate the transition and realize the economic benefits of delivering a safer climate.

Contact us to get involved.

This “Earth Summit”, as it became known, led to the formation of the UN Convention on Climate Change and the UN Convention on Biological Diversity , along with the Commission on Sustainable Development to monitor and report on the implementation of Earth Summit agreements.

And as citizens were increasingly concerned with corporate impacts on the natural environment, big and small businesses started to feel the pressure to consider sustainability in their practice.

Have you read?

Is climate inaction a human rights violation, how earth observation from space helps advance climate change research, why is earth day important today.

As the millennium loomed, the Earth Day movement turned its attention to the growing reality of the impending climate crisis with a clear message for world leaders and business: urgent action is needed to address global warming.

It’s a message that is even more relevant today. The latest report from the Intergovernmental Panel on Climate Change states that without further immediate action to curb greenhouse gas emissions, the world is on course for temperatures 3.2°C above pre-industrial levels by 2100. This level of warming would be catastrophic for the planet and all life on it, including humans.

The year 2023 was the hottest ever recorded .

The World Economic Forum's Global Risks Report 2024 finds that environmental risks make up half of the top 10 risks over the next 10 years, with extreme weather events, critical change to Earth's systems, biodiversity loss and ecosystem collapse being the top three.

Nature is our biggest ally in fighting the climate crisis and has slowed global warming by absorbing 54% of human-related carbon dioxide emissions over the past 10 years. Yet, we are losing animals, marine species, plants, and insects at an unprecedented rate, not seen in 10 million years . Threats from human activity for food production and ocean use, infrastructure, energy and mining endanger around 80% of all threatened or near-threatened species .

Earth Day has become a leading light in the fight to combat climate change and nature loss. As we celebrate its 54th anniversary, we must make use of this truly global movement to act, as citizens and governments, as consumers and businesses, and as individuals and communities. Our survival could well depend on it.

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