tourism infrastructure examples

The future of tourism: Bridging the labor gap, enhancing customer experience

As travel resumes and builds momentum, it’s becoming clear that tourism is resilient—there is an enduring desire to travel. Against all odds, international tourism rebounded in 2022: visitor numbers to Europe and the Middle East climbed to around 80 percent of 2019 levels, and the Americas recovered about 65 percent of prepandemic visitors 1 “Tourism set to return to pre-pandemic levels in some regions in 2023,” United Nations World Tourism Organization (UNWTO), January 17, 2023. —a number made more significant because it was reached without travelers from China, which had the world’s largest outbound travel market before the pandemic. 2 “ Outlook for China tourism 2023: Light at the end of the tunnel ,” McKinsey, May 9, 2023.

Recovery and growth are likely to continue. According to estimates from the World Tourism Organization (UNWTO) for 2023, international tourist arrivals could reach 80 to 95 percent of prepandemic levels depending on the extent of the economic slowdown, travel recovery in Asia–Pacific, and geopolitical tensions, among other factors. 3 “Tourism set to return to pre-pandemic levels in some regions in 2023,” United Nations World Tourism Organization (UNWTO), January 17, 2023. Similarly, the World Travel & Tourism Council (WTTC) forecasts that by the end of 2023, nearly half of the 185 countries in which the organization conducts research will have either recovered to prepandemic levels or be within 95 percent of full recovery. 4 “Global travel and tourism catapults into 2023 says WTTC,” World Travel & Tourism Council (WTTC), April 26, 2023.

Longer-term forecasts also point to optimism for the decade ahead. Travel and tourism GDP is predicted to grow, on average, at 5.8 percent a year between 2022 and 2032, outpacing the growth of the overall economy at an expected 2.7 percent a year. 5 Travel & Tourism economic impact 2022 , WTTC, August 2022.

So, is it all systems go for travel and tourism? Not really. The industry continues to face a prolonged and widespread labor shortage. After losing 62 million travel and tourism jobs in 2020, labor supply and demand remain out of balance. 6 “WTTC research reveals Travel & Tourism’s slow recovery is hitting jobs and growth worldwide,” World Travel & Tourism Council, October 6, 2021. Today, in the European Union, 11 percent of tourism jobs are likely to go unfilled; in the United States, that figure is 7 percent. 7 Travel & Tourism economic impact 2022 : Staff shortages, WTTC, August 2022.

There has been an exodus of tourism staff, particularly from customer-facing roles, to other sectors, and there is no sign that the industry will be able to bring all these people back. 8 Travel & Tourism economic impact 2022 : Staff shortages, WTTC, August 2022. Hotels, restaurants, cruises, airports, and airlines face staff shortages that can translate into operational, reputational, and financial difficulties. If unaddressed, these shortages may constrain the industry’s growth trajectory.

The current labor shortage may have its roots in factors related to the nature of work in the industry. Chronic workplace challenges, coupled with the effects of COVID-19, have culminated in an industry struggling to rebuild its workforce. Generally, tourism-related jobs are largely informal, partly due to high seasonality and weak regulation. And conditions such as excessively long working hours, low wages, a high turnover rate, and a lack of social protection tend to be most pronounced in an informal economy. Additionally, shift work, night work, and temporary or part-time employment are common in tourism.

The industry may need to revisit some fundamentals to build a far more sustainable future: either make the industry more attractive to talent (and put conditions in place to retain staff for longer periods) or improve products, services, and processes so that they complement existing staffing needs or solve existing pain points.

One solution could be to build a workforce with the mix of digital and interpersonal skills needed to keep up with travelers’ fast-changing requirements. The industry could make the most of available technology to provide customers with a digitally enhanced experience, resolve staff shortages, and improve working conditions.

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Complementing concierges with chatbots.

The pace of technological change has redefined customer expectations. Technology-driven services are often at customers’ fingertips, with no queues or waiting times. By contrast, the airport and airline disruption widely reported in the press over the summer of 2022 points to customers not receiving this same level of digital innovation when traveling.

Imagine the following travel experience: it’s 2035 and you start your long-awaited honeymoon to a tropical island. A virtual tour operator and a destination travel specialist booked your trip for you; you connected via videoconference to make your plans. Your itinerary was chosen with the support of generative AI , which analyzed your preferences, recommended personalized travel packages, and made real-time adjustments based on your feedback.

Before leaving home, you check in online and QR code your luggage. You travel to the airport by self-driving cab. After dropping off your luggage at the self-service counter, you pass through security and the biometric check. You access the premier lounge with the QR code on the airline’s loyalty card and help yourself to a glass of wine and a sandwich. After your flight, a prebooked, self-driving cab takes you to the resort. No need to check in—that was completed online ahead of time (including picking your room and making sure that the hotel’s virtual concierge arranged for red roses and a bottle of champagne to be delivered).

While your luggage is brought to the room by a baggage robot, your personal digital concierge presents the honeymoon itinerary with all the requested bookings. For the romantic dinner on the first night, you order your food via the restaurant app on the table and settle the bill likewise. So far, you’ve had very little human interaction. But at dinner, the sommelier chats with you in person about the wine. The next day, your sightseeing is made easier by the hotel app and digital guide—and you don’t get lost! With the aid of holographic technology, the virtual tour guide brings historical figures to life and takes your sightseeing experience to a whole new level. Then, as arranged, a local citizen meets you and takes you to their home to enjoy a local family dinner. The trip is seamless, there are no holdups or snags.

This scenario features less human interaction than a traditional trip—but it flows smoothly due to the underlying technology. The human interactions that do take place are authentic, meaningful, and add a special touch to the experience. This may be a far-fetched example, but the essence of the scenario is clear: use technology to ease typical travel pain points such as queues, misunderstandings, or misinformation, and elevate the quality of human interaction.

Travel with less human interaction may be considered a disruptive idea, as many travelers rely on and enjoy the human connection, the “service with a smile.” This will always be the case, but perhaps the time is right to think about bringing a digital experience into the mix. The industry may not need to depend exclusively on human beings to serve its customers. Perhaps the future of travel is physical, but digitally enhanced (and with a smile!).

Digital solutions are on the rise and can help bridge the labor gap

Digital innovation is improving customer experience across multiple industries. Car-sharing apps have overcome service-counter waiting times and endless paperwork that travelers traditionally had to cope with when renting a car. The same applies to time-consuming hotel check-in, check-out, and payment processes that can annoy weary customers. These pain points can be removed. For instance, in China, the Huazhu Hotels Group installed self-check-in kiosks that enable guests to check in or out in under 30 seconds. 9 “Huazhu Group targets lifestyle market opportunities,” ChinaTravelNews, May 27, 2021.

Technology meets hospitality

In 2019, Alibaba opened its FlyZoo Hotel in Huangzhou, described as a “290-room ultra-modern boutique, where technology meets hospitality.” 1 “Chinese e-commerce giant Alibaba has a hotel run almost entirely by robots that can serve food and fetch toiletries—take a look inside,” Business Insider, October 21, 2019; “FlyZoo Hotel: The hotel of the future or just more technology hype?,” Hotel Technology News, March 2019. The hotel was the first of its kind that instead of relying on traditional check-in and key card processes, allowed guests to manage reservations and make payments entirely from a mobile app, to check-in using self-service kiosks, and enter their rooms using facial-recognition technology.

The hotel is run almost entirely by robots that serve food and fetch toiletries and other sundries as needed. Each guest room has a voice-activated smart assistant to help guests with a variety of tasks, from adjusting the temperature, lights, curtains, and the TV to playing music and answering simple questions about the hotel and surroundings.

The hotel was developed by the company’s online travel platform, Fliggy, in tandem with Alibaba’s AI Labs and Alibaba Cloud technology with the goal of “leveraging cutting-edge tech to help transform the hospitality industry, one that keeps the sector current with the digital era we’re living in,” according to the company.

Adoption of some digitally enhanced services was accelerated during the pandemic in the quest for safer, contactless solutions. During the Winter Olympics in Beijing, a restaurant designed to keep physical contact to a minimum used a track system on the ceiling to deliver meals directly from the kitchen to the table. 10 “This Beijing Winter Games restaurant uses ceiling-based tracks,” Trendhunter, January 26, 2022. Customers around the world have become familiar with restaurants using apps to display menus, take orders, and accept payment, as well as hotels using robots to deliver luggage and room service (see sidebar “Technology meets hospitality”). Similarly, theme parks, cinemas, stadiums, and concert halls are deploying digital solutions such as facial recognition to optimize entrance control. Shanghai Disneyland, for example, offers annual pass holders the option to choose facial recognition to facilitate park entry. 11 “Facial recognition park entry,” Shanghai Disney Resort website.

Automation and digitization can also free up staff from attending to repetitive functions that could be handled more efficiently via an app and instead reserve the human touch for roles where staff can add the most value. For instance, technology can help customer-facing staff to provide a more personalized service. By accessing data analytics, frontline staff can have guests’ details and preferences at their fingertips. A trainee can become an experienced concierge in a short time, with the help of technology.

Apps and in-room tech: Unused market potential

According to Skift Research calculations, total revenue generated by guest apps and in-room technology in 2019 was approximately $293 million, including proprietary apps by hotel brands as well as third-party vendors. 1 “Hotel tech benchmark: Guest-facing technology 2022,” Skift Research, November 2022. The relatively low market penetration rate of this kind of tech points to around $2.4 billion in untapped revenue potential (exhibit).

Even though guest-facing technology is available—the kind that can facilitate contactless interactions and offer travelers convenience and personalized service—the industry is only beginning to explore its potential. A report by Skift Research shows that the hotel industry, in particular, has not tapped into tech’s potential. Only 11 percent of hotels and 25 percent of hotel rooms worldwide are supported by a hotel app or use in-room technology, and only 3 percent of hotels offer keyless entry. 12 “Hotel tech benchmark: Guest-facing technology 2022,” Skift Research, November 2022. Of the five types of technology examined (guest apps and in-room tech; virtual concierge; guest messaging and chatbots; digital check-in and kiosks; and keyless entry), all have relatively low market-penetration rates (see sidebar “Apps and in-room tech: Unused market potential”).

While apps, digitization, and new technology may be the answer to offering better customer experience, there is also the possibility that tourism may face competition from technological advances, particularly virtual experiences. Museums, attractions, and historical sites can be made interactive and, in some cases, more lifelike, through AR/VR technology that can enhance the physical travel experience by reconstructing historical places or events.

Up until now, tourism, arguably, was one of a few sectors that could not easily be replaced by tech. It was not possible to replicate the physical experience of traveling to another place. With the emerging metaverse , this might change. Travelers could potentially enjoy an event or experience from their sofa without any logistical snags, and without the commitment to traveling to another country for any length of time. For example, Google offers virtual tours of the Pyramids of Meroë in Sudan via an immersive online experience available in a range of languages. 13 Mariam Khaled Dabboussi, “Step into the Meroë pyramids with Google,” Google, May 17, 2022. And a crypto banking group, The BCB Group, has created a metaverse city that includes representations of some of the most visited destinations in the world, such as the Great Wall of China and the Statue of Liberty. According to BCB, the total cost of flights, transfers, and entry for all these landmarks would come to $7,600—while a virtual trip would cost just over $2. 14 “What impact can the Metaverse have on the travel industry?,” Middle East Economy, July 29, 2022.

The metaverse holds potential for business travel, too—the meeting, incentives, conferences, and exhibitions (MICE) sector in particular. Participants could take part in activities in the same immersive space while connecting from anywhere, dramatically reducing travel, venue, catering, and other costs. 15 “ Tourism in the metaverse: Can travel go virtual? ,” McKinsey, May 4, 2023.

The allure and convenience of such digital experiences make offering seamless, customer-centric travel and tourism in the real world all the more pressing.

Three innovations to solve hotel staffing shortages

Is the future contactless.

Given the advances in technology, and the many digital innovations and applications that already exist, there is potential for businesses across the travel and tourism spectrum to cope with labor shortages while improving customer experience. Process automation and digitization can also add to process efficiency. Taken together, a combination of outsourcing, remote work, and digital solutions can help to retain existing staff and reduce dependency on roles that employers are struggling to fill (exhibit).

Depending on the customer service approach and direct contact need, we estimate that the travel and tourism industry would be able to cope with a structural labor shortage of around 10 to 15 percent in the long run by operating more flexibly and increasing digital and automated efficiency—while offering the remaining staff an improved total work package.

Outsourcing and remote work could also help resolve the labor shortage

While COVID-19 pushed organizations in a wide variety of sectors to embrace remote work, there are many hospitality roles that rely on direct physical services that cannot be performed remotely, such as laundry, cleaning, maintenance, and facility management. If faced with staff shortages, these roles could be outsourced to third-party professional service providers, and existing staff could be reskilled to take up new positions.

In McKinsey’s experience, the total service cost of this type of work in a typical hotel can make up 10 percent of total operating costs. Most often, these roles are not guest facing. A professional and digital-based solution might become an integrated part of a third-party service for hotels looking to outsource this type of work.

One of the lessons learned in the aftermath of COVID-19 is that many tourism employees moved to similar positions in other sectors because they were disillusioned by working conditions in the industry . Specialist multisector companies have been able to shuffle their staff away from tourism to other sectors that offer steady employment or more regular working hours compared with the long hours and seasonal nature of work in tourism.

The remaining travel and tourism staff may be looking for more flexibility or the option to work from home. This can be an effective solution for retaining employees. For example, a travel agent with specific destination expertise could work from home or be consulted on an needs basis.

In instances where remote work or outsourcing is not viable, there are other solutions that the hospitality industry can explore to improve operational effectiveness as well as employee satisfaction. A more agile staffing model  can better match available labor with peaks and troughs in daily, or even hourly, demand. This could involve combining similar roles or cross-training staff so that they can switch roles. Redesigned roles could potentially improve employee satisfaction by empowering staff to explore new career paths within the hotel’s operations. Combined roles build skills across disciplines—for example, supporting a housekeeper to train and become proficient in other maintenance areas, or a front-desk associate to build managerial skills.

Where management or ownership is shared across properties, roles could be staffed to cover a network of sites, rather than individual hotels. By applying a combination of these approaches, hotels could reduce the number of staff hours needed to keep operations running at the same standard. 16 “ Three innovations to solve hotel staffing shortages ,” McKinsey, April 3, 2023.

Taken together, operational adjustments combined with greater use of technology could provide the tourism industry with a way of overcoming staffing challenges and giving customers the seamless digitally enhanced experiences they expect in other aspects of daily life.

In an industry facing a labor shortage, there are opportunities for tech innovations that can help travel and tourism businesses do more with less, while ensuring that remaining staff are engaged and motivated to stay in the industry. For travelers, this could mean fewer friendly faces, but more meaningful experiences and interactions.

Urs Binggeli is a senior expert in McKinsey’s Zurich office, Zi Chen is a capabilities and insights specialist in the Shanghai office, Steffen Köpke is a capabilities and insights expert in the Düsseldorf office, and Jackey Yu is a partner in the Hong Kong office.

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Tourism infrastructure: what is it and how is it made?

The touristic infrastructure it is a set of facilities and institutions that constitute the material and organizational base for the development of tourism. It consists of basic services, road system, transport, accommodation, gastronomy, services for cultural and leisure activities, network of shops, tourist protection services and others.

Tourism has become a booming industry worldwide. Annually more than a billion people move outside their usual place to visit places of great attractiveness, in order to spend their vacations, entertain themselves, or perform other leisure activities.

According to the World Tourism Organization, tourism ranks third in exports of services and goods worldwide, with a greater growth in the last five years than international trade.

The tourist attractions form the primary base to attract tourists, giving them a space-time itinerary. However, actions aimed at protecting and adapting to these attractions are necessary in order to generate the tourist movement.

The complementary tourist resources that serve for this purpose are defined as tourist infrastructure.

• 1 How is the tourist infrastructure of a country?
• 2.1 One of the most visited countries
• 2.2 Need for development
• 2.3 The coastal destination stands out
• 2.4 Cultural richness
• 3 References

How is the tourist infrastructure of a country?

The economic apogee has made tourism become for any country an obvious trigger of infrastructure creation, causing an excellent synergy between public and private investment.

The government when it makes investments in tourist infrastructure is creating a beneficial circle with which it encourages private investment and its economic profit, and on the other hand, private investment leads to the social profit sought with government investment.

The tourism infrastructure makes it possible for tourism to develop, so there must be both a strategic plan and good management so that each tourist destination can give an effective maintenance to said infrastructure, in such a way that the tourist feels satisfied and comfortable. with the facilities as with the required services.

The tourist infrastructure of a country is made up of interconnected elements that allow tourists to arrive, stay and enjoy the tourist attraction of their destination, making their trip a pleasure, among which are:

• Basic services: water supply, electricity, telecommunications, waste collection, health and hygiene, security and protection.
• Road system: highways, roads, roads and trails.
• Transportation: airports, seaports, river boats, rail networks, buses, taxis.
• Accommodation: hotels, inns, apartments, camps.
• Gastronomy: restaurants, fast food establishments, taverns, cafes.
• Services for cultural activities: art and entertainment, museums, nature reserves, zoos.
• Services for sports and recreational activities: rental of sports and recreational items, gambling and betting rooms, amusement parks, golf courses, sports courts, diving, skiing.
• Other services: tourist information, equipment and vehicle rental, banking services.
• Network of stores and shops in general.
• Security services / tourist protection.

Commercial entities, such as hotels or restaurants, create and operate infrastructures to serve their customers (tourists). Public entities develop infrastructure not only for the service of tourists but, mainly, for the creation of conditions for the development of the region, serving the whole society (including tourists) and the economy.

Characteristics of the tourist infrastructure in Mexico

An interesting country to know the characteristics of its tourist infrastructure is Mexico. He Mexican tourism represents an immense industry.

One of the most visited countries

According to the World Tourism Organization, Mexico is among the ten most visited countries in the world and is the second most visited country in the Americas, behind the United States.

Mexico has a significant number of sites cataloged by UNESCO as World Heritage Sites, which include ancient ruins, colonial cities and nature reserves.

In the report"Travel and Tourism Competitiveness Index"of 2017, which measures the factors to do business in the tourist industry of each country, Mexico ranked 22nd in the world ranking, its tourist service infrastructure ranked 43 , health and hygiene in 72, and safety and protection in 113.

Need for development

According to recent statements by the president of the Mexican Association of Hotels and Motels, Mexico needs more infrastructure to attract European tourists and thus depend less on the United States, where 60% of tourists who enter the country come from.

Greater air connectivity is needed, as well as more and better roads and trains to attract tourists from Europe and elsewhere.

Although there are more than 35 international airports in the country, there are major airports saturated, such as Mexico City, and there is a lack of greater internal connectivity that allows other tourist destinations, such as Cancún, to be exploited.

The coastal destination stands out

The coasts of Mexico harbor beaches with an excellent tourist infrastructure. In the Yucatan peninsula, the most popular beach destination is the tourist city of Cancun. South of Cancun is the coastal strip called Riviera Maya.

On the Pacific coast, the most notable tourist destination is Acapulco, famous as the ancient destination of the rich and famous.

To the south of Acapulco are the surf beaches of Puerto Escondido. North of Acapulco is the tourist city of Ixtapa.

Cultural richness

The abundant culture and natural beauty existing in the states of the Mexican southeast allows us to devise an exceptionally competitive tourist destination.

In order for tourists to reach destinations farther away from the main cities, work has been carried out on development plans for tourism infrastructure, such as the planned centers project in Chichén Itza, Calakmul and Palenque, or the trans-peninsular train, the extension of the Cancun airport, as well as the construction of a Convention Center in the city of Mérida, the construction of hospitals or the increase of roads.

Thus, when a tourist arrives at the Cancun airport, apart from enjoying the modern tourist welcome offered by the Riviera Maya and its beautiful beaches, you can also penetrate other places in the area; know for example the historic center of Campeche, the route of the cenotes, archaeological sites revealing the great Mayan culture, or delight in jungle tourism.

In the same way you can make a guest at a congress in Merida, which will surely expand your visit depending on the formidable and varied local offer.

All this will produce a significant economic income, since during your stay that tourist will taste the cuisine of the region, buy handicrafts and souvenirs, will stay in different accommodations and hire tourist guides or means of transport in the same region.

• International Recommendations for Tourism Statistics 2008 New York, 2010. United Nations. Department of Economic and Social Affairs Statistics Division. Studies in Methods Series M No. 83 / Rev.1. Available in: unstats.un.org
• OMT panorama of international tourism. Edition 2017. World Tourism Organization. October 2017. eISBN: 978-92-844-1904-3 ISBN: 978-92-844-1903-6. Available at e-unwto.org.
• Tourism Infrastructure as a determinant of regional development. Panasiuk, Aleksander. University of Szczecin. ISSN 1648-9098. Ekonomika go vadiba: Actualijos go perspectyvos. 2007
• Tourism in Mexico. From Wikipedia, the free encyclopedia. Taken from en.wikipedia.org
• Infrastructure for tourism. Secretariat of Tourism of Mexico. May 2015. Available at sectur.gob.mx.
• More infrastructure, key to attract European tourism. The Universal newspaper of Mexico. 01/20/2018 Available at eluniversal.com.mx.

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Turning tourism into development: Mitigating risks and leveraging heritage assets

If done right, tourism can actually bolster and preserve cultural heritage, while also helping to develop economies. Image:  REUTERS/David Loh

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Maimunah mohd sharif.

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Stay up to date:, travel and tourism.

• Cultural and historical travel accounts for 40% of all tourism globally.
• 73% of millennials report being interested in cultural and historic places.
• Protecting local culture and heritage requires a robust plan to mitigate negative impacts and policies to ensure prosperity is shared.

Culture and heritage tourism has the potential to create significant employment opportunities and stimulate economic transformation.

However, communities worldwide often grapple with the challenges posed by the magnetic appeal of heritage sites and the promise of economic prosperity. Property values can increase, displacing local residents and permanently altering the character of their neighbourhoods.

But capitalizing on tourism's potential while preserving and enhancing history and culture is possible — and it is already being done in sites around the world. From Malaysia to Saudi Arabia, many are already demonstrating the ability to balance economic development with socially and environmentally sustainable transformations.

Below are five common features that those sustainable approaches embrace.

Have you read?

This is how to leverage community-led sustainable tourism for people and biodiversity, are we finally turning the tide towards sustainable tourism, how the middle east is striving to lead the way in sustainable tourism, translating a vision into an area-based plan.

Urban planning and regeneration require a holistic approach, coordinating interventions across various sectors and providing guidance for investments. A holistic plan would include spatial and policy measures that are supported by regulatory measures, particularly those focusing on affordability and social cohesion. UN-Habitat prioritizes measures which promote mixed-use and social-economically diverse development to mitigate gentrification.

In George Town, Malaysia, the Special Area Plan and its Comprehensive Management Plan function as the key reference for inclusive strategic policies, regulations and guidelines for conservation, economic activities and intangible heritage. The plan, which balances economic development and conservation, included affordability measures such as supporting local owners restoring their houses, enabling adaptive reuse for small businesses, and supporting renters, thus protecting a share of historic buildings from tourism-induced redevelopment.

In Saudi Arabia’s AlUla, home to 40,000 residents and leading cultural assets including Hegra and Jabal Ikma — which was recently added to UNESCO’s Memory of the World International Register — a similar vision is unfolding. The Path to Prosperity masterplan makes provisions for new housing, creates new economic opportunities and establishes new schools, mosques and healthcare facilities for the community with affordability as the guiding principle. An expanded public realm will create district and neighbourhood parks with green spaces, playgrounds, outdoor gyms and bicycle trails. A network of scenic routes, low-impact public transportation and non-vehicular options will facilitate mobility.

A diversified economic base

To avoid over-reliance on a single economic driver, planners must make space for a range of alternative livelihoods. In AlUla, The Royal Commission for AlUla (RCU), which is responsible for the city’s development into a tourism hub, is drawing on its rich local heritage to create a global destination while diversifying the local economy. Investment in native industries such as agriculture has resulted in a revived high-yielding and higher-value farming sector, while new sectors such as the creation of film and logistics industries are creating new jobs and providing increased revenue for residents.

The UN-Habitat Parya Sampada project in the Kathmandu Valley undertook earthquake reconstruction of the heritage settlements in urban areas using a holistic approach of physical reconstruction and economic recovery. It focused on the reconstruction of public heritage infrastructure supported by tourism enterprises run by women and youth.

Nurturing living heritage and local knowledge

Maintaining the character of a place is critical to its future and creates valuable economic assets. Maintenance and preservation animate the built environment, while the recovery of building techniques and crafts of traditional cultural activities creates jobs and maintains skills.

UN-Habitat’s work in Beirut demonstrates this approach, supporting several hundred jobs. Through the Beirut Housing Rehabilitation and Cultural and Creative Industries project, led by UN-Habitat, UNESCO supervises the allocation of small grants to local artisans. The regeneration of the historical train station in Mar Mikhael and adjacent areas will focus on traditional building techniques to reactivate cultural markets and businesses.

In AlUla, the Hammayah training programme is empowering thousands to work as guardians of natural heritage and culture. In Myanmar the nationwide Community-Based Tourism initiative is operated and managed by local vulnerable communities to provide genuine experiences to world travelers.

Share the value created by tourism

Addressing the negative externalities of tourism requires the assessment and compensation of its real impacts, which can be done through sustainable tourism planning and community participation. The pressure on services, increased congestion and the cost of living need to be addressed through specific investments, funded through the taxation of tourism-related revenues redirected towards the local community, especially for the most vulnerable groups.

Examples include the Balearic Island of Mallorca, which has introduced a sustainable tourism tax to support conservation of the island. Meanwhile Kyoto, Japan has implemented several measures to control the number of tourists at popular sites and establish visitor codes of conduct.

Human-centered local development

Empowering the local community to actively engage with its rich culture while minimizing conflict with the natural environment can increase the resilience of residents and reduce the pressures of gentrification. Participation in decision-making is critical to shape visions and plans that achieve these goals.

The UN-Habitat Participatory Strategy in Mexico’s San Nicolas de los Garza showcases how collaboration with the local community throughout the design and implementation process can ensure solutions capture the culture, skills and needs of the neighborhoods. The 2030 City Vision provides a participatory action plan for the integration of culture, heritage and tourism within the currently prevalent urban economic sectors.

In Saudi Arabia such approaches are embedded in Vision 2030, a blueprint for economic diversification. RCU deploys short- and long-term support to the community through scholarship, upskilling and support for SMEs to enhance access to jobs and entrepreneurship in hospitality and tourism.

While development always introduces complex dynamics and transformations, mitigating gentrification in tourist areas is crucial to achieving sustainable local development for the benefit of all and preserving the unique character of these places.

These measures advocate a proactive approach to ensure that economic growth remains inclusive for the entire community, and that tourism is promoted for the benefit of local residents as well as visitors.

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• 1) World Bank Group, 2005. Multilateral Investment Guarantee Agency (MIGA): Tanzania's Investor Outreach Program. 2) De Chazal Du M (DCDM), 2011. Tourism in Tanzania: Investment in Tourism in Tanzania. 3) The World Bank, 2015. Diagnostic Trade Integration Study (DTIS) for Tanzania. 4) United Republic of Tanzania, 2021. Third National Five-Year Plan (FYDP 3). 5) United Republic of Tanzania, 2021. Tanzania Tourism Policy – Under Review. 6) Operations Research Society of Eastern Africa, Journal Vol. 7 (2), 2017. Gender and Women Entrepreneurs’ Strategies in Tourism Markets: A Comparison between Tanzania and Sweden. 7) United Republic of Tanzania, 2002, Tourism Master Plan. 8) Word Bank Group, 2021. Tanzanian Economic Update, Transforming Tourisms Sector, Toward a Sustainable, Resilient, and Inclusive Sector. 9) Oxford Business Group, 2017. https://oxfordbusinessgroup.com/analysis. 10) HAL Open Science, 2021. Economic impacts of COVID-19 on the tourism sector in Tanzania.
• 11) Tanzania Invest, 2022. https://www.tanzaniainvest.com/tourism. 12) Jadian Company Limited, 2021. Feasibility Study Report. 13) Word Bank Group, 2021. Tanzanian Economic Update, Transforming Tourisms Sector, Toward a Sustainable, Resilient, and Inclusive Sector. 14) PricewaterhouseCoopers, 2019. Hospitality Outlook. 15) Aman Raphael, 2013. Career Development of Women in Hospitality Industry: Insights From Double Tree By Hilton Hotel, Tanzania. 16) African Journal of Hospitality, Tourism and Leisure, Volume 7, 2018. 17) Tanzanian Ministry of Education and Vocational Training, 2015. Human Resource Needs and Skill Gaps in the Tourism and Hospitality Sector in Tanzania. 18) United Republic of Tanzania, 2008. Tourism Act. 19) United Republic of Tanzania, 2009. Wildlife Conservation Act (No. 5). 20) United Republic of Tanzania, 2013. The Wildlife Conservation Act. 21) The World Bank, 2017. New Opportunities for Development in Southern Tanzania Through Nature-Based Tourism. 22) United Republic of Tanzania, 2022. Standard Incentives for Investors. https://investment-guide.eac.int. 23) Ministry of Natural Resources and Tourism of United Republic of Tanzania, 2022. https://www.maliasili.go.tz/attractions/tanzania-tourist-attractions. 24) World Travel & Tourism Council (WTTC), 2022. https://wttc.org. 25) World Bank Group, 2015. Tanzania Economic Update. 26) Adumua Safaris, 2022. https://adumusafaris.com/destinations/tanzania-southern-circuit. 27) Inter-American Development Bank, 2011. Public-Private Partnerships (PPPs) for Sustainable Tourism in Tanzania. 28) United Republic of Tanzania, 2019. Tourism Investment Guide. 29) United Republic of Tanzania, 1997. Tanzania Investment Act, No. 26. 30) Nature Conservation, 2013. Emerging issues and challenges in conservation of biodiversity in the rangelands of Tanzania. 31) Journal of Development Studies, 2015. Gender and Livelihood Diversification: Maasai Women’s Market Activities in Northern Tanzania. 32) Happiness Kiami, 2018. Effects of Tourism Activities on The Livelihoods of Local Communities In The Eastern Arc Mountains. 33) World Economic Forum, 2019. Travel and Tourism Competitiveness Index Report. 34) Oxford Business Group, 2018 Addressing infrastructure challenges in southern Tanzania to drive tourism growth.

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Open Access

Peer-reviewed

Research Article

How does new infrastructure impact the competitiveness of the tourism industry?——Evidence from China

Roles Writing – review & editing

Affiliation Institute of Management, Shanghai University of Engineering Science, Shanghai, China

Roles Writing – original draft

* E-mail: [email protected]

Affiliation Institute of Geography, Heidelberg University, Heidelberg, Germany

Roles Methodology

Roles Data curation

• Guodong Yan, 
• Lin Zou, 
• Yunan Liu, 

• Published: December 1, 2022
• https://doi.org/10.1371/journal.pone.0278274
• Reader Comments

Infrastructure construction related to the new generation of information technology and 5G technology is an important measure taken by the Chinese government to promote regional economic development. Large-scale infrastructure investment is being carried out simultaneously in China’s core and peripheral regions. The COVID-19 pandemic has dealt a severe blow to China’s tourism industry, and the application of new technologies seems to blur the spatial boundaries of the tourism industry. Therefore, it is debatable whether the zealous development of large investment projects can really improve the competitiveness of the regional tourism industry. This paper discusses this topical issue by empirically analyzing data from 31 Chinese provinces and cities from 2008–2019 and draws the following conclusions (1) The continuous expansion of new infrastructure investment in China indeed has a positive effect on improving China’s overall tourism competitiveness. However, the inverted U-shaped relationship between the two shows that China should not blindly expand the scale of infrastructure construction and make appropriate investment according to the regional industrial development level. (2) Although convergent infrastructure plays an important role in regional industrial competitiveness, the marginal effect has begun to weaken, so the problem of scale inefficiency needs to be addressed. In contrast, the input of innovation infrastructure is insufficient to enhance industrial competitiveness and can be moderately increased to achieve better results. (3) China’s core economic areas have a good driving effect on new infrastructure investment, but the original technological innovation and transformation-type facilities are still the key to limiting the improvement of industrial competitiveness. Peripheral areas are more passive recipients with strong demand. Therefore, investment in various types of infrastructure can drive regional development.

Citation: Yan G, Zou L, Liu Y, Ji R (2022) How does new infrastructure impact the competitiveness of the tourism industry?——Evidence from China. PLoS ONE 17(12): e0278274. https://doi.org/10.1371/journal.pone.0278274

Editor: Hironori Kato, The University of Tokyo, JAPAN

Received: June 7, 2022; Accepted: November 13, 2022; Published: December 1, 2022

Copyright: © 2022 Yan 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: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported by National Social Science Foundation of China (17BJY148); National Natural Science Foundation of China (42101175); China Scholarship Council Postdoctoral Foundation (Grant: 202008310025).

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

1 Introduction

Since 2018, China has defined the construction of 5G, artificial intelligence, industrial internet and the internet of things as "new infrastructure construction". New infrastructure construction focuses on the industrial internet to provide infrastructure for the digital transformation of industry, with investment in fixed assets, advanced infrastructure and digital platforms. The development of the new infrastructure has accelerated the deployment and application of cutting-edge digital technologies in China.

In 2020, China has defined that the scope of new infrastructure construction mainly includes information infrastructure, converged infrastructure and innovation infrastructure. Information infrastructure refers to the infrastructure developed and created based on the new generation of information technology, such as the infrastructure of communication networks represented by 5G, the Internet of Things, the Industrial Internet and satellite Internet, the infrastructure of new technologies represented by artificial intelligence, cloud computing and blockchain, and the infrastructure represented by data centers and intelligent data centers. Converged infrastructure refers to the transformed and modernized infrastructure through the deep application of internet, Big Data, artificial intelligence and other technologies, such as smart transport infrastructure and smart energy infrastructure. Innovation infrastructure refers to the non-profit infrastructure supporting scientific research, technological development and product research and development, such as large-scale science and technology infrastructure, science and education infrastructure and industrial technology innovation infrastructure.

The COVID-19 pandemic in 2020 has brought major changes to the tourism industry. The traditional tourism industry, which used to rely on crowd consumption, has almost come to a standstill, while emerging industries such as cloud display art and cloud tourism based on digital technology are developing rapidly in China.For the digital tourism industry, building new infrastructures does not only mean satisfying the growing demand for information processing, information transmission and storage capacity. The digital tourism industry relies on digital technology for the production, distribution and management of tourism-related content, providing digital cultural services with smarter connectivity, deeper interaction, more comprehensive integration and higher quality content, creating a variety of new platforms and formats for the digital tourism industry and opening up new development opportunities. China’s tourism industry is showing an increasingly clear digital development trend. The construction of new infrastructure has become the key promoter of the transformation and development of the digital tourism industry, which plays a key role in improving the competitiveness of regional and national tourism in the Special Period. Therefore, from the perspective of the joint development of new infrastructure construction and the tourism industry, this paper analyses the general issue of how new infrastructure construction affects the competitiveness of China’s tourism industry, and specifically attempts to discuss the diversity of the role of new infrastructure development on tourism in terms of the types of facilities, regional differences and investment differences.

This paper is organized as follows. The next section reviews the relevant literature and proposes the analytical framework for the relationship between the construction of new infrastructure and the competitiveness of the tourism industry; the third section describes the data and methodology of our study; the fourth and fifth sections discuss the impact of new infrastructure on the tourism industry in China; finally, the last section provides some concluding remarks.

2 Analytical frameworks

New infrastructure construction has become a necessary condition for China’s economic development and has a catalytic effect on the high-quality development of the regional economy and the transformation and upgrading of the industrial structure [ 1 , 2 ]. The construction of new infrastructure is closely related to tourism development and can improve the quality of tourism development [ 3 – 5 ]. Tourism is associated with multiple industries and has strong industrial penetration [ 6 , 7 ]. Digitization and connectivity of new infrastructure reduces the negative benefits of travel time and provides potential economic benefits based on improving the value of travel time [ 8 ]. The construction of new infrastructure enables investment of China’s public finance, government debt funds, private investment and other funds into the internal economic circulation system [ 9 ], which plays a role in improving the income level and tourism consumption capacity of urban and rural residents and boosts demand in China’s domestic tourism market. It helps China to mitigate the economic impact of the COVID -19 pandemic to a certain extent, which is the internal economic cycle that the Chinese government has emphasized. It is worth highlighting that the Chinese government’s new infrastructure construction policy stimulates private investment in the tourism industry, creating economic spillover effects [ 10 ]. This leads to an increase in private investment and consumption, much of which comes from foreign FDI investment, linking the two-way interaction between China’s internal and international capital [ 5 ].

Infrastructure investment can promote economic cycles and growth and is subject to the law of diminishing marginal returns [ 11 ]. The marginal benefit of improving the competitiveness of the tourism industry gradually increases as the scale of investment in new infrastructure increases, but above a certain level of investment, the marginal benefit of the competitiveness of the tourism industry decreases. This is because too much investment in tourism industry infrastructure leads to conflicts in capital utilization and neglect of other development needs of the tourism industry. Therefore, the level of investment also has an impact on the competitiveness of the tourism industry.

Although infrastructure encompasses the key fields of the scientific and technological revolution and industrial change, different categories of infrastructure have different emphases in the tourism industry chain, and there are differences in how the tourism industry uses and depends on different types of new infrastructure. Information infrastructure is mainly based on a cloud computing platform to support the construction of an urban tourism database, accurately analyses tourists’ preferences, realize personalized recommendations for tourism strategies, and innovate the market segmentation and positioning of the tourism industry [ 12 ]. Convergent infrastructures focus on implementing digital transformation, such as improving tourism accessibility through smart transport infrastructures that help tourists plan the shortest travel time. Innovation infrastructure helps to integrate knowledge elements into the development and construction of tourism destinations. Due to the different ways in which new infrastructure is built, there are large differences in their impact on improving the competitiveness of the tourism industry.

The development of tourism based on information and network infrastructures can better reflect the diversity of regional conditions, and the construction of new infrastructures makes knowledge and information the main production factors of the digital economy [ 13 ]. With the development of the digital economy, tourism culture, landscape, folk customs and other local cultures become more diverse and can be more easily integrated through digitalization. With the help of smart infrastructure of destinations, digital tourist attractions can be gradually built, and the temporal and spatial boundaries of tourism culture production can be broken. There is obvious heterogeneity in the role of new infrastructure construction, especially in terms of heterogeneity in the types of infrastructure construction and regional contextual differences [ 14 ].

The regional conditions in East China, Central China and West China are very different, and the regional tourism industry is developed by different economic policies, economic levels and urban cultures in each region. There are also differences in the stage of investment, scale and impact of new infrastructure construction, leading to large differences in the dependence of the regional tourism industry on new infrastructure. For example, the five major urban agglomerations in the coastal areas of East China are the core areas of economic and social development, with advantages in policy implementation, rich tourism resources and perfect tourism infrastructure [ 15 ]; the new infrastructure can quickly interact with the development of the tourism industry. There is still a gap between investment in new urban infrastructure in central China and eastern China, as central China does not have the obvious relative advantage of political support. Western China is the peripheral area of China’s economic development. Although it is considered by planners as the most important planning area for tourism, it has weak capacity to distribute resources to the market. Due to institutional backwardness, western China has an urgent need for new infrastructure. Therefore, the impact of new infrastructure on the competitiveness of the tourism industry varies greatly due to different regional conditions. There are studies on the relationship between new infrastructure development and tourism development in China, but are the massive government investments in new infrastructure development in China really able to increase the competitiveness of the industry? Does blind expansion lead to scale economies, or how does it play in different regions? To answer this general question, a research framework for new infrastructure and the tourism industry was established ( Fig 1 ).

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https://doi.org/10.1371/journal.pone.0278274.g001

3 Methodology

3.1 data collection.

The panel data of tourism industry and new infrastructure investment from 2008 to 2019 of 31 provinces and municipalities in China (excluding Taiwan, Macau and Hong Kong) were selected as the research dataset for this paper. Data such as New infrastructure construction, Market potential, Destination accessibility, Sustainable development, Health care construction, Accommodation and catering performance, Culture construction are from China Statistical Yearbook (2009–2020) and Statistical Bulletin of National Economic and Social Development (2009–2020). Data of market performance is from China Tourism Statistical Yearbook (2009–2020). The control variables are industrial structure, population size and degree of openness. The proportion of tertiary industry represents the rationalization of the industrial structure; we use the proportion of tertiary industry in the total value of all industries to measure the regional industrial structure; Since population affects regional tourism consumption and population size has a positive effect on local tourism consumption and tourist flows [ 16 , 17 ], regional population is selected to measure population size; regional openness is of great importance in attracting international tourists and developing the international market; import and export status of each region is used to measure the openness of each region. Data of control variable is also from China Statistical Yearbook (2009–2020).

3.1.1 Infrastructure indicators.

According to existing research and the Chinese government’s policy definition [ 18 ], new infrastructure consists of information infrastructure (Infra1), convergent infrastructure (Infra2), innovation infrastructure (Infra3) and its total infrastructure ( Table 1 ), each represented by the level of investment capital of the corresponding infrastructure sectors from 2008 to 2019.

https://doi.org/10.1371/journal.pone.0278274.t001

Referring to the existing researches, the competitiveness index of the tourism industry mainly includes two parts: the development potential and the performance of the tourism industry [ 3 , 19 , 20 ] ( Table 2 ). Referring to the 2021 travel and tourism development index and existing researches, this paper constructs the competitiveness index of the tourism industry. The factor analysis method was used to calculate the tourism industry competitiveness of 31 provinces and cities in China from 2008 to 2019.According to the Travel and Tourism Development Index (TTDI) and existing research [ 19 , 21 – 24 ], the development potential of the tourism industry mainly includes market potential, destination accessibility, sustainable development, and health care construction. Tourism industry performance mainly includes market performance, accommodation and catering performance, culture construction, etc.

https://doi.org/10.1371/journal.pone.0278274.t002

On the one hand, for indicator of tourism industry potential, tourism consumption has a long-term equilibrium relationship with residents’ income or consumption level, these factors have a certain role in promoting tourism consumption, which can represent regional market potential [ 25 ]. To realize the tourism market expansion, especially in central or western economic peripheral regions, a combination of tourism and postal service is one of the most important ways [ 26 ]. Travel destination accessibility is normally related to air transport, ground and port transport service capabilities, etc., which will influence traveler mobility and attraction accessibility [ 22 ]. Environmental sustainability is an important factor in the long-term profitability of national or regional tourism destinations [ 19 ]. Moreover, health care construction is an important condition to ensure travel safety, and China’s rapidly developing tourism industry urgently needs medical construction in high-level tourist destinations [ 21 ], which has been pronounced during the covid-19 pandemic. Based on the existing research, consider the particularity of China’s tourism industry, while ensuring the integrity of the data available. This research takes the income or consumption level of regional residents and regional post-service income as the main indicators of regional market potential. Passenger traffic volume of railways and highways, and the number of employees in railway, highway, aviation, and water transportation are considered the main indicators of regional transportation construction level. The sustainability of regional tourism is measured in terms of forest coverage rate, harmless treatment rate of domestic waste, oxygen demand in wastewater, ammonia nitrogen emission in wastewater, and sulfur dioxide emission. Indicators such as the number of health institutions, the number of health technicians per 1,000 people, and the number of hospital beds per 1,000 people are used to characterize health care construction.

On the other hand, for tourism industry performance, indicators related to domestic and foreign tourism revenue, and the number of inbound and domestic tourists can characterize the regional market performance. Tourist reception capacity, accommodation and catering-related consumption and infrastructure construction are the main indicators to measure the competitiveness of the industry. Tourist reception capacity, accommodation, catering-related consumption, and infrastructure construction level are the main aspects to measure the industry competitiveness, which are measured by indicators such as the number of accommodation and catering firms, the number of employed employees, and labor remuneration. Cultural tourism is an important part of international tourism consumption, which is usually measured by the number of libraries and museums, the number of audiences of performance groups, the number of cultural and entertainment employees, and labor remuneration [ 24 ].

Based on the regional statistical data of tourism in 31 provinces and cities in China from 2008 to 2019, the original data were tailed by 1%, and the data were normalized to eliminate the influence of extreme values. This study determines the weights through factor analysis, calculates the competitiveness of the tourism industry and provides a statistical description of the data ( Table 3 and S1 Appendix ).

https://doi.org/10.1371/journal.pone.0278274.t003

3.2 Fixed effects regression model

In terms of data selection, panel data combines the advantages of time series and cross-section data. It has the advantages of controlling temporal and spatial heterogeneity, reducing multicollinearity and reducing data bias, and has been widely used in existing causal relationship studies [ 27 – 29 ].

Fixed effect model focuses on the change of a single object over time and can eliminate the interference of multiple fixed factors without considering the variation among different individuals. Random effect model can make better use of data information by weighted average of variation within and among individuals. However, due to the consideration of individual variation, it must be assumed that the residuals are not related to the independent variables, which is relatively inaccurate.

To clarify the model, the Hausmann test was performed in this research ( Table 4 ). In model1, the P value of the F test is 0, the null hypothesis is rejected at the 1% significance level, indicating that the fixed model is better than the mixed model; The P value of the LM test is 0 in model2, and the hypothesis of "there is no individual random effect" is rejected at the 1% significance level, indicating that the random effect model is better than the mixed model; Model 3 is the Hausman test result, p value = 0, the null hypothesis is strongly rejected, and the fixed effects model is considered significantly better than the random model, so here the fixed-effects model should be used.

https://doi.org/10.1371/journal.pone.0278274.t004

Eq ( 2 ) verifies the differences of impact of different types of infrastructure on the competitiveness of tourism industry, where X*it denotes information infrastructure, converged infrastructure and innovation infrastructure respectively; Eq ( 3 ) verifies the effect differences in different regions, where dummy1 and dummy 2 are dummy variables which shall be 1 when the region is located in Central and West China or 01 when the region is not located in Central and West China; Eq ( 4 ) verifies the effect differences of different investment scales, where dummy 3 is a dummy variable which shall be 1 when the investment scale is greater than China’s average or 0 when not; the remaining variables have the same meaning as in Eq ( 1 ).

3.3 Regional context and infrastructure foundation

Since China unveiled its new infrastructure construction strategy in 2018, the amount of investment in new infrastructure has steadily increased. By February 2020, the Chinese government had issued RMB 231.9 billion worth of special bonds for new infrastructure-related sectors. Investment in new infrastructure construction accounts for about 20% to 25% of total infrastructure investment. In 2022, China will focus on building 425,000 5G base stations. Although the Chinese government is trying to increase investment in new infrastructure construction, according to the 2019 World Economic Forum statistics, China has an unbalanced ranking in the quality of global infrastructure construction, as the different types of infrastructure construction vary widely. Moreover, infrastructure investment in China varies greatly from region to region. In terms of communication infrastructure development, the penetration rate of mobile phones, fixed broadband and internet in East China in 2018 was 145%, 34.24% and 61.32% respectively, and 18 cities supported 5G network coverage; the penetration rate of mobile phones, fixed broadband and internet in Central China was 93.69%, 25.9% and 45.6% respectively, which is a big difference from East China, and only 6 cities supported 5G network coverage. The penetration rate of mobile phone, fixed broadband and internet in Western China was significantly lower than the Chinese average.

The regional differences in the competitiveness of the tourism industry show a trend of expansion from the core regions of East China to West China from 2008 to 2019. East China is the core region of economic development and has a more solid industrial economic base, better service facilities and higher consumption level of residents than the other regions of China. Therefore, the regional tourism industry demand is huge, the regional tourism base and demand are developed first, and undoubtedly have higher initial competitiveness than other regions. Yunnan and Sichuan in western China are rich in natural resources for tourism, but the initial competitiveness of the industry is not high due to the accessibility and regional socio-economic development level, suggesting low industrial competitiveness. With China’s industrial transfer and infrastructure development, the advantages of tourism resources in western China are gradually becoming apparent.

4.1 Impact of infrastructure on overall tourism competitiveness

According to the regression results ( Table 5 ), China’s investment in new infrastructure construction has a significant positive impact on improving regional competitiveness in tourism. In model 1, regional competitiveness in tourism increases by about 0.6642 per unit investment.

https://doi.org/10.1371/journal.pone.0278274.t005

Adding control variables to model 1 to obtain model 2, the regression coefficient has decreased, but it still plays a positive role at the 1% significance level. In order to eliminate the influence of time and individual differences, this paper further adds time and individual fixed effects to Model 2 and obtains Model 3. The regional tourism competitiveness increased by about 0.1326 with the investment of new infrastructure units. Differences in variables such as industrial structure, population size, and openness, as well as time and individual fixed effects, explain the decline of the regression coefficient to a certain extent. Model 3 still shows the same positive trend under the full introduction of various conditions.

Model 4 introduces the quadratic term of new infrastructure investment into the regression equation. The results show that the effect of new infrastructure investment on increasing the competitiveness of the tourism industry is not linear but has an "inverted U-curve." That is, there is an inflection point between new infrastructure investment and the improvement of tourism industry competitiveness, which shows the law of diminishing marginal effect.

To a certain extent, this is related to the characteristics of the system of assessing local governments with GDP as the main indicator. Furthermore, local governments will encourage enterprises and institutions to participate in key new infrastructure projects by increasing local taxes and other means to achieve the goal of increasing GDP. This can lead to excessive infrastructure construction, overcapacity and industry inefficiency in some regions [ 32 , 33 ]. In addition, expanding infrastructure investment has a certain crowding-out effect on household consumption. The proportion of tourism consumption decreases accordingly, which cannot effectively promote the competitiveness of the tourism industry.

Models 5 and 6 show the impact of a time lag of new infrastructure investment on the competitiveness of the tourism industry. We believe that despite the increase of the coefficient, there is no obvious lag effect of new infrastructure investment and the feedback of the investment effect can be achieved in time. Therefore, the current investment stock is used as an explanatory variable in the following analysis.

4.2 Diverse impact of new infrastructure

4.2.1 impact of infrastructure type..

The analysis of the impact of different types of infrastructures on tourism competitiveness ( Table 6 ) shows that information infrastructure, convergent infrastructure has a significant impact on the competitiveness of the regional tourism industry. The impact of convergent infrastructure on the competitiveness of tourism industry is the most significant (regression coefficient 0.4337), followed by information infrastructure (0.3560).

https://doi.org/10.1371/journal.pone.0278274.t006

The main reason for this phenomenon is that convergent infrastructure is the profound application of new generation information technology through the construction of traditional infrastructure. The convergence of information technology and digital economy has created the development path of intelligent city, intelligent transportation and intelligent sightseeing in China. By building convergent infrastructure, the region can provide efficient basic services in accommodation, transportation, catering and medical care, improve tourists’ leisure experience and enhance the competitiveness of the tourism industry. The information infrastructure is dominated by 5G, the Internet of Things, and the Industrial Internet, such as intelligent sightseeing reservation, information-based tourism platform, and live streaming tours, which have been increasing in recent years, realize the online information collection of tourists and provide marketing, management, and service standards for attractions and hotels. Innovation infrastructure consists of the infrastructure to support technology development, scientific research and product development. It is generally dominated by the Chinese government and scientific research institutions, with relatively little private investment capacity. The development of innovation infrastructure is highly targeted, and it will take a long time for the tourism industry to receive technical support from the funds invested by governments and scientific research institutions in different regions.

It is worth noting that models 4 to 6 introduce the quadratic term of different types of infrastructure investment into the regression model, and the regression coefficient of the first term of convergent infrastructure and innovation infrastructure is positive, while the regression coefficient of the quadratic term is negative, suggesting that the effect of the two on the competitiveness of the tourism industry is an "inverted U curve" with an obvious inflection point. The insignificant effect of information infrastructure on the competitiveness of the tourism industry indicates that the marginal effect of investment in information infrastructure has not decreased significantly and the capital stock of information infrastructure needs to be built up continuously. Therefore, investment in the construction of new infrastructure in China should not be increased blindly, but should be targeted and gradual, to avoid wasting resources through a blanket approach.

4.2.2 Impact of regional context.

Regional site diversity is an important factor affecting regional industrial competitiveness. The results show that ( Table 7 ), after adding the cross term of regional dummy variables and infrastructure investment, the impact of infrastructure investment on the economic peripheral western region is higher than that in the developed east region. In order to clarify the main reasons for this phenomenon, this paper divides infrastructure into three categories to verify their effects on tourism competitiveness in different regions.

https://doi.org/10.1371/journal.pone.0278274.t007

The promotion of information infrastructure to the tourism competitiveness of the western regions of China is higher than that of the eastern regions. Although the investment in information infrastructure in the eastern region is the best in China, it still lags behind the development level of the region’s own tourism industry, which leads to the low empowerment performance of information infrastructure. On the contrary, the level of investment in information infrastructure in the western regions is relatively low, but the marginal effect of these investments on the tourism industry in this region is significant. Convergence infrastructure has a similar effect on the competitiveness of the tourism industry, as the marginal effect of converged infrastructure on economic peripheral is high.

The difference is that the innovation infrastructure has no obvious effect on the eastern and central regions, while the western region has a significant effect.This is because the investment in innovative infrastructure in the eastern and central regions is still unable to meet the needs of tourism, so the role of economies of scale is insufficient. The innovative infrastructure in the western region will help promote regional opening up and gradually realize the transformation of the regional economic growth mode.

4.2.3 Impact of investment scale.

The effect of the differences in investment scale shows that the average capital stock for new infrastructure construction in China is 0.554, and the average capital stock for information infrastructure, convergent infrastructure, and innovation infrastructure is 0.183, 0.236, and 0.134, respectively. The average value of infrastructure investment of different types is taken as the boundary for the construction of dummy variables, and after adding the cross term between the dummy variables of scale and level of infrastructure investment ( Table 8 ), model 1 represents the impact of infrastructure construction of different scales on tourism competitiveness, and models 2 to 4 analyze infrastructure construction in three main categories.

https://doi.org/10.1371/journal.pone.0278274.t008

Overall, adequate new infrastructure investment has a positive effect on improving tourism competitiveness, but it should be emphasized that the marginal benefit actually declines gradually as the size of the investment increases. Combined result of the previous results (Tables 5 – 7 ), the investment effect of innovation infrastructure (model 4) can still be improved significantly. Too low investment in innovative infrastructure cannot enhance the competitiveness of the tourism industry, which needs to be improved by increasing the investment scale. However, it should be pointed out that the implementation effect of innovative infrastructure projects in western China should be emphasized, and the coordination between infrastructure projects and investment in the tourism industry should be planned to avoid the negative impact of infrastructure investment crushing the investment of the tourism industry.

The impact of integrated infrastructure (model 3) is significant, the expansion of investment scale has not brought more enabling effects. Although this is consistent with the law of diminishing marginal utility, we should be wary of the phase shift of regional differentiation. China should moderately reduce investment in traditional infrastructure in China and focus on the efficient integration of new-generation information technology and traditional infrastructure in China.

5 Discussion and conclusion

Since the Fifth Plenary Session of the 19th CPC Central Committee, China has increased its investment in the construction of new technologies and facilities, and the Chinese government has stimulated and vigorously promoted the construction of infrastructure related to the new generation of information technology and 5G technology, hoping to achieve economic impact on a wide range of industries through large-scale investment. Such large-scale investments are taking place not only in China’s economic core cities and eastern coastal areas but also in China’s economically marginal central and western regions. For example, the government of Guizhou Province, China’s least developed province, has launched the "Data Operation in Eastern China and Hash rate Support in Western China" project, investing RMB 17 billion in Big Data by 2022. In addition, 18 major projects related to 5G have been launched in China’s core economic cities such as Shenzhen. We cannot help but wonder if large-scale new infrastructure projects and investments are actually delivering the desired results and helping industries that otherwise lack momentum. The tourism industry was one of the fastest-growing future industries in China, but the COVID -19 pandemic led to a sudden halt in industry development in 2020. How should China seek a new breakthrough to achieve a new round of economic growth? Will government investment in infrastructure play a role in stagnant industrial competitiveness? Based on China’s new economic phenomenon and the practical issues that need to be addressed, we discuss how China’s new infrastructure construction will affect the competitiveness of the regional tourism industry. Below are our thoughts and conclusions:

China’s new infrastructure investment shows a sustained growth trend. Based on the impact model of new infrastructure investment and regional competitiveness in tourism, it is found that there is a positive relationship between the two. Consequently, regional competitiveness in tourism can be improved through the construction of new infrastructure. It should also be noted that the impact relationship is not a continuous linear relationship, but an inverted U-shaped curve and the marginal effect decreases over time. Therefore, the scale of infrastructure construction cannot be expanded blindly. Instead, it is necessary to analyze and understand the status of infrastructure investment and the development of the regional tourism industry to carry out appropriate construction, which requires comprehensive consideration of the region, infrastructure types and investment scale, and other specific factors.

Combined with the different types of infrastructure deployment and the existing level of investment (1) the deployment of convergent infrastructure represented by the Internet, Big Data, artificial intelligence, and other technologies have the greatest impact, as these technologies are widely used to provide basic tourism-related services and leisure experiences, and increased deployment in this segment can quickly improve the industry’s competitiveness. However, the impact of converged infrastructure has reached a tipping point in terms of the scale of investment. Therefore, we must pay attention to the efficiency of continued investment and avoid wasting resources. (2) The construction of innovation infrastructure in the form of scientific research, technological development, and research, and development of new products has little impact, because the research and development of new technologies and new products have a certain technical bottleneck, and the technical innovation cycle is generally long. In addition, such infrastructure investment is usually led by the government or local prestigious scientific research institutions, so the cost of time and human capital is extremely high, and regional industrial development needs more cycles. There is no obvious inflection point in the development of information infrastructure, especially 5G and IoT, which is different from the other two categories, suggesting that information infrastructure does not necessarily have a marginal effect on tourism development. Combined with the investment scale, the promotion effect of investment in innovation infrastructure has not yet been fully realized. Therefore, a moderate increase in investment in innovationinfrastructure within a certain period will have a good marginal effect on improving industrial competitiveness.

Considering the regional heterogeneity, East China has become a core economic region, and the tourism market there is very receptive and responds quickly to the application of new technologies. Therefore, investment in information and convergence infrastructure in the region can significantly improve industrial competitiveness, but there is still an insufficient response to scientific research and innovation infrastructure in the region. This shows that even in the most developed core regions of China, original innovation and technology transformation is still a problem that limits the improvement of industrial competitiveness. Western China is a relatively marginal region in terms of economic development, but precisely because of its weak base, the region is more responsive to the country’s new infrastructure, and an increase in investment and improvement in the tourism environment and conditions can quickly pull up competitiveness. Together with the rich tourism resources, the investment in infrastructure makes up for the region’s original backwardness. Central China has completed China’s industrial transfer, but it is not rich in original resources, and its economic resource advantage is far lower than that of the core economic regions. Therefore, an advantageous path to regional development has yet to be found. Infrastructure investment should not be blindly increased to drive industrial development so that the marginal effect is not extremely small. Therefore, depending on the above-mentioned regional conditions and differences in demand, precise new infrastructure investment can avoid a blanket approach and improve the competitiveness of the regional industry.

Achieving competitiveness in the regional tourism industry is in itself a difficult and complex socioeconomic issue, and the relationship between new infrastructure construction and industrial competitiveness cannot be resolved by a simple index regression. Although this is a clue to solving the problem, in our further research we will describe the mechanism of this impact through in-depth interviews and further discuss the cross-regional synergistic facilitation through inter-regional relationships.

Supporting information

S1 appendix. descriptive statistics for indicators..

https://doi.org/10.1371/journal.pone.0278274.s001

https://doi.org/10.1371/journal.pone.0278274.s002

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Importance of Tourism Infrastructure Development

Visits by a tourist create additional development of the place such as parks, gardens, and museums. Additional facilities include roads, water systems, public toilets, signage, etc. Because all of these infrastructure developments are important for the tourism sector. While there are many programs organized by Government at the top level it is the governance by the local government that supports the system uniform.

Every development with regard to a place is dependent on the need of the visitors. Visitors use a variety of facilities depending upon the priority. By proper analysis of the opportunities plan, necessary facilities that need to be implemented can be identified. Facilities generally include,

• Transportation Facilities
• Healthcare Facilities
• Water Management Facilities
• Waste Management Facilities
• Recreational Facilities

Accessibility to the above facilities is the important thing in creating an impression among tourists. The satisfaction of the tour program is measured by these facilities.

The population of a place and the tourists visiting that place can have a significant effect on infrastructure development (Tourism development) . Foreseeing the demand that may occur during seasons is crucial in determining how much money should be invested in developing the destination.

The development of a place for tourism can also help in boosting the economy of the locality. Even though this kind of development is not noticed easily the factor of contribution by tourism sector on the economy is higher. A well-developed infrastructure not only enhances the overall tourism experience but also contributes significantly to the economic, social, and environmental aspects of a destination.

For interested people, Westford University College provided a Diploma program in Tourism Management . This program helps students to gain an in-depth understanding of managing projects in the industry and build their strategic planning capabilities internationally.

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Investment in tourism infrastructure includes investment in components such as transport and communications infrastructure, the hotel and restaurant industry, and recreation facilities... Investment in tourism infrastructure development to make destinations and services increasingly attractive is considered a key measure in developing a country’s tourist destinations. It has a strong and positive impact on visitor attraction. 

1. Introduction

2. the role of transport infrastructure and communications infrastructure, 3. the role of the hotel and restaurant industry, 4. the role of recreation facilities, 5. the influence of uncertain factors.

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Tourism infrastructure and the environment: how does environmental regulation affect welfare, tourism industry, and domestic wage inequality?

• Published: 07 January 2022
• Volume 75 , pages 147–179, ( 2024 )

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• Takanori Shimizu   ORCID: orcid.org/0000-0001-7107-1357 1 &
• Hisayuki Okamoto 2  

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This study presents a general equilibrium model of a small open developing economy with pollution generated by the tourism industry. The national government issues emission permits and constructs tourism infrastructure for the tourism sector. We examine the effects of a stricter environmental regulation on welfare, production, and income distribution. If the elasticity of substitution in the tourism sector is sufficiently low, a stricter environmental regulation paradoxically expands the tourism sector and narrows domestic wage inequality, even under constant tourism terms of trade. In this model, in addition to the two traditional channels, there is a new channel through which a stricter environmental regulation affects the tourism terms of trade and domestic welfare. The new channel, which arises from the difference between the marginal value product of tourism infrastructure and its price, improves the tourism terms of trade and domestic welfare if (1) the marginal value product of tourism infrastructure is greater than its price, (2) the output of tourism infrastructure is increased by a stricter environmental regulation, and (3) the excess supply of a tourism service decreases with a stricter environmental regulation.

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1 Introduction

The tourism industry has become an important sector for both developed and developing countries as it creates employment opportunities and attracts foreign currency. The tourism sector requires a large amount of investment, for example, water supply, sewerage systems, ports, airports, parks, highways, and tourism promotion by authorities (e.g., Visit Japan, Incredible India, and Malaysia Truly Asia), which is rather difficult to be financed only by the private sector. Therefore, a national government needs to construct public infrastructure for the tourism industry, hereafter referred to as tourism infrastructure. At the same time, the tourism sector causes environmental damage. For example, the concentration of people degrades the water quality in the local community, and traffic congestion pollutes the air by the emission of fumes. Footnote 1 , Footnote 2 To mitigate these negative effects, the government introduces an environmental regulation by issuing emission permits to control the amount of pollution. The government can use the revenue from selling pollution permits to construct tourism infrastructure. In general, by reducing the number of emission permits, a stricter environmental regulation tends to discourage the tourism sector, while the formation of tourism infrastructure encourages the sector. A stricter environmental regulation directly improves welfare by reducing the disutility of pollution; at the same time, the price of a tourism service affects consumption patterns and income levels. Thus, it is important to consider the welfare effects of a stricter environmental regulation.

According to ILO ( 2018 , pp. 16–17), the extent of wage inequality—measured by the Gini coefficient—is higher in low- and middle-income countries than in high-income countries. Therefore, rising wage inequality is a serious problem for many developing countries. Footnote 3 This gives rise to the need for developing countries to find a policy that mitigates domestic wage inequality. As pointed out by Aynalem et al. ( 2016 , p. 3), “generally speaking, the tourism and hospitality sector is recognized as having low hourly rates of pay, overtime work without extra money, long working hours of 50 h per week, little or no adequate breaks during peak season periods.” Footnote 4 It follows that, in addition to the welfare level, the wage gap between the tourism sector and other sectors is one of the most important concerns for policymakers.

In general, an environmental regulation affects the outputs of industries, including the tourism sector, which in turn causes a change in the factor reward employed in each industry and domestic income inequality. However, there are relatively few theoretical analyses of the effect of environmental policy on domestic wage inequality in the presence of tourism. Exceptions include Chao et al. ( 2012 ) and Nakai et al. ( 2018 ). The present study aims to fill this gap in the literature.

There are many theoretical studies on the analysis of international tourism. In this regard, Copeland ( 1991 ) has made a seminal contribution to the literature. Although he analyzed the effect of a tourism boom (an increase in the demand for tourism services by foreign tourists) on the tourism terms of trade (the price of tourism services exported through international tourism) and welfare, the environmental pollution problem was not considered. In recent years, there have been many studies on tourism and the environment, including Beladi et al. ( 2009 ), Chao et al. ( 2008 , 2012 ), Gupta and Dutta ( 2018 ), Nakai et al. ( 2018 ), Yabuuchi ( 2013 , 2015 , 2018 ), and Yanase ( 2017 ). Beladi et al. ( 2009 ) constructed a two-good (one traded good and one tourism service) model in which pollution is emitted as a by-product of the tourism service and derived the optimal pollution tax rate that maximizes social welfare. They found that the optimal pollution tax rate does not coincide with the Pigouvian level, that is, the marginal environmental damage to domestic residents. Footnote 5 Chao et al. ( 2008 ) considered a three-good (two traded goods and one tourism service) model in which pollution is generated by a manufacturing industry and derived a combination of optimal pollution tax and import tariff. They found that the optimal import tariff rate is positive. By constructing a three-good model in which the tourism sector requires pollution emissions as an input, Yanase ( 2017 ) showed that if the excess supply of tourism services rises with a pollution tax, the optimal pollution tax level is lower than the Pigouvian level. By constructing a two-good model where the manufacturing industry is under perfect competition while the tourism sector is under oligopoly, and both tourism and manufacturing industries require pollution permits as an input, Chao et al. ( 2012 ) found that if the factor cost share of emission permits in the tourism industry is lower than in the manufacturing sector, a stricter environmental regulation narrows wage inequality. They also showed that if the stricter environmental regulation greatly improves the tourism terms of trade, domestic welfare also improves. In a two-good model where both tourism and manufacturing industries are under perfect competition, Nakai et al. ( 2018 ) showed that an improvement in tourism terms of trade narrows domestic wage inequality.

In a three-good Harris–Todaro model where agriculture and tourism industries are located in the rural area and the manufacturing sector is located in the urban area, Yabuuchi ( 2013 ) found that a tourism boom (pollution tax) reduces (increases) the urban unemployment rate, which positively (negatively) affects domestic welfare. By considering the negative production externality of the tourism industry to agriculture, Yabuuchi ( 2015 ) showed that a tourism boom (pollution tax) increases (decreases) the urban unemployment rate. Further, Yabuuchi ( 2018 ) introduced a subsidy to agriculture financed by pollution tax, and showed that a tourism boom can reduce the urban unemployment rate if the subsidy rate is sufficiently high relative to the negative externality of the tourism sector.

In a dynamic model of tourism and the environment, Gupta and Dutta ( 2018 ) showed that a tourism boom expands the tourism sector in the short run but contracts it in the long run. Meanwhile, Yanase ( 2015 ) analyzed the role of infrastructure in the tourism industry in a dynamic setting. He showed that if the economy specializes in the production of tourism services, a tourism boom improves the tourism terms of trade and makes the economy better off. However, he did not consider environmental problems.

The above-mentioned studies on tourism and the environment consider only production-generated pollution. In many studies on trade and the environment, pollution is generated during the production process. For example, Copeland and Taylor ( 2003 ) dealt only with production-generated pollution. However, the pollution emitted during consumption should not be neglected. For the analysis of trade and consumption-generated pollution, see Beghin and Roland-Holst ( 1997 ) and Copeland ( 2011 , Sect. 6.2). On the one hand, Beghin and Roland-Holst ( 1997 ) considered both production-generated and consumption-generated pollution and analyzed the effects of pollution tax (imposed on producers and consumers), trade tax (import tariff), and emission tax on welfare. On the other hand, Copeland ( 2011 , Sect. 6.2) analyzed the welfare effects of emission tax on consumption (differentiated by domestic goods and foreign goods) and trade tax. However, in the above two studies, only traded goods are considered; thus, the good prices are constant under the assumption of a small open economy. In contrast, the price of tourism services (i.e., the tourism terms of trade) is endogenously determined in the domestic market.

In summary, no study has examined the effects of environmental policy on domestic welfare and the wage gap in the presence of tourism infrastructure. The research question in this paper, therefore, is as follows: Does a stricter environmental regulation improve domestic welfare or narrow domestic wage inequality in an economy with tourism infrastructure?

To answer the above question, we present a polluted small open developing economy model with infrastructure in the tourism sector and examine the effects of an environmental regulation on output, income distribution, and welfare. In this study, pollution is an input into tourism service. Footnote 6 The government issues emission permits to the tourism industry and constructs tourism infrastructure. Unskilled labor is a specific input into the tourism service industry. If the elasticity of substitution in the tourism sector is sufficiently low and the tourism terms of trade are constant, a stricter environmental regulation paradoxically expands the tourism industry. In that case, domestic wage inequality narrows as a result of the stricter environmental regulation. The intuitive reason is as follows. If emission permits are hardly substituted by unskilled labor, a stricter environmental regulation—by decreasing the number of emission permits—greatly raises the price of emission permits, leading to an increase in revenue from selling emission permits. Hence, the government can obtain more resources to construct tourism infrastructure, which increases the output of the tourism service and the wage of unskilled labor.

In this study, we provide a two-final-good model with tourism infrastructure that improves the productivity of the tourism sector. To finance the cost of infrastructure, the Lindahl pricing rule (the price of public intermediate goods is equal to its marginal value product) has been traditionally adopted (Okamoto, 1985 ). However, this study does not assume the Lindahl pricing rule. Thus, the tangency property with respect to the production possibility curve, the envelope theorem with respect to the revenue function, and the reciprocity relations (the Stolper–Samuelson effect is equal to the Rybczynski effect) do not necessarily hold. Footnote 7 Resultantly, we obtain an interesting result that a stricter environmental regulation may expand the tourism industry. This result does not appear in the existing literature on tourism and the environment.

This study also examines welfare implications. Regarding the tourism terms of trade and the welfare effects of a stricter environmental regulation, two traditional channels have been pointed out by Beladi et al. ( 2009 ) and Yanase ( 2017 ). Our study includes an additional channel arising from the difference between the marginal value product of tourism infrastructure and its price. It will be shown that the new channel improves the tourism terms of trade and domestic welfare under certain conditions.

The remainder of this paper is organized as follows. In Sect.  2 , we describe the setup of the model. Section  3 conducts a comparative statics analysis of the supply side of the economy. In Sect.  4 , we examine the effects of a stricter environmental regulation by considering both the supply and demand sides of the economy. Section  5 presents the concluding remarks.

2 The model

Consider a small open developing economy that produces a manufacturing good X and a service T. The manufacturing good is traded while the service is non-traded in the absence of foreign tourists. The service is exported through international tourism, and the manufacturing good is imported. We call service T a tourism service, and its price as the tourism terms of trade. Suppose that the production of the manufacturing good requires capital K and skilled labor S, while the production of the tourism service requires unskilled labor L and pollution emission Z. In other words, skilled labor (unskilled labor) is a specific input into the traded good (tourism service) industry. This specification, which is also adopted in Chao et al. ( 2010 , 2012 ), is consistent with the observations of Marjit and Acharyya ( 2003 ) and Chao et al. ( 2010 ). Marjit and Acharyya ( 2003 , p. 117) pointed out that “most of the non-traded production in the LDCs uses unskilled labour intensively”, while Chao et al. ( 2010 , p. 455) stated that “for developing economies, the exportable sector tends to be relatively intensive in unskilled labor”. The domestic government collects revenue from selling emission permits and uses this revenue to construct tourism infrastructure. For simplicity, suppose that the tourism infrastructure requires only capital input, Footnote 8 and further assume that the formation of tourism infrastructure only enhances the productivity of the tourism industry. Footnote 9 Therefore, the cost of tourism infrastructure is financed by the user pay principle. In other words, firms in the tourism sector—the beneficiaries of tourism infrastructure—bear the cost through the payment for pollution permits.

The production function of manufacturing good (or traded good) X is given by

where \({K}_{j}\) denotes the capital input into good \(j\) and \(S\) is the endowment of skilled labor. Function \(X\) is assumed to be the neoclassical type of production function that exhibits homogeneity of degree one, and to be strictly quasi-concave.

The production function of the tourism service is given by

Function \(N\) has the same properties as function \(X\) , that is, the neoclassical properties. Moreover, function \(g\) represents the positive externality of the infrastructure, \(M\) is the amount of tourism infrastructure devoted only to the tourism industry, \(L\) is the endowment of unskilled labor, and \(Z\) is the amount of pollution. Keeping \(M\) unchanged and doubling \(L\) and \(Z\) , the output of tourism service \(T\) doubles. This implies that the tourism infrastructure has no congestion effect. This means that the tourism infrastructure in this study is the creation of the atmosphere type in Meade’s ( 1952 ) terminology. For example, \(M\) is considered a tourism promotion campaign by tourism authorities, which reduces advertisement costs. Given the amount of production input, an increase in \(M\) results in an increase in the output of the tourism service. Therefore, \(g\) is an increasing function of \(M\) . For the positive externality of intermediate goods or infrastructure, see Okamoto ( 1985 ) and Yanase ( 2015 ).

We assume \(g\) is twice continuously differentiable and has the following properties:

The first condition implies that if there is no tourism infrastructure, the productivity of the tourism sector does not change. The second and third conditions mean that tourism infrastructure has a positive and diminishing effect on the productivity of the tourism sector. Finally, the last two conditions are known as the Inada conditions. Yanase ( 2015 ) made a similar assumption.

The production function of tourism infrastructure is

where \({a}_{ij}\) is the amount of factor \(i(=L,S,K,Z)\) to produce one unit of good \(j(=X,T,M)\) . We assume a linear production function for tourism infrastructure and, thus, \({a}_{KM}\) is constant.

We now examine the equilibrium conditions for the supply side of the economy. We assume that perfect competition prevails in the manufacturing and tourism industries. The zero-profit condition (the price of the good is equal to its unit cost) for the traded good industry is

where \({p}_{X}\) is the price of traded goods, \({w}_{S}\) is the wage of skilled labor, and \(q\) is the rental rate of capital. Note that \({p}_{X}\) is constant under the assumption of a small open economy. Applying Shephard’s lemma, we obtain the unit requirement of factor \(i\) in the production of good \(j\) ( \({a}_{ij}\) ) by differentiating the unit cost function with respect to the associated factor price.

The zero-profit condition for the tourism service industry is

where \({p}_{T}\) is the price of the tourism service, \({w}_{L}\) the wage of unskilled labor, and \(r\) the price of emission permits.

The zero-profit condition for tourism infrastructure sector is Footnote 10

where \({p}_{M}\) is the shadow price of tourism infrastructure.

Next, we consider the factor market equilibrium conditions. Factor endowments are provided exogenously. The full employment condition of capital is

The demand–supply equality of skilled labor requires

The market equilibrium condition of unskilled labor requires

The amount of pollution is given by

Finally, the budget constraint of the government is

where the left-hand side (LHS) denotes the revenue from selling emission permits, and the right-hand side (RHS) denotes the cost of constructing tourism infrastructure. Equations ( 1 )–( 8 ) include eight unknowns: \(X\) , \(T\) , \({w}_{L}\) , \({w}_{S}\) , \(q\) , \(r\) , \({p}_{M}\) , and \(M\) . Given \({p}_{T}\) , the above eight equations determine eight unknowns. Footnote 11 Note that the price of tourism infrastructure \({p}_{M}\) is determined to satisfy the government’s budget constraint (8). It follows that the traditional Lindahl pricing rule does not necessarily hold; thus, we obtain different properties from the standard trade theory.

To facilitate the following analysis, we introduce the elasticity of factor substitution. The elasticity of substitution in each sector \({\sigma }_{j}\) is defined as

A hat over a variable implies the rate of change: for example, \({\widehat{w}}_{S}\equiv d{w}_{S}/{w}_{S}\) .

The cost minimization in each sector requires Footnote 12

where \({\theta }_{ij}\) represents the cost share of factor \(i\) in sector \(j\) .

Solving Eqs. ( 9 ) and ( 11 ), we obtain

Similarly, solving Eqs. ( 10 ) and ( 12 ), we have

Differentiating Eq. ( 1 ) totally and taking into account Eq. ( 11 ), we obtain

Differentiating Eq. ( 2 ) totally and considering Eq. ( 12 ), we obtain

where \(\xi \equiv \frac{g^{\prime}M}{g} > 0\) denotes the elasticity of \(g\) with respect to \(M\) , or the productivity improvement rate of the tourism industry by additional tourism infrastructure. By definition, \(\widehat{g}=\xi \widehat{M}\) holds.

Since \({a}_{KM}\) is constant, Eq. ( 3 ) implies

Differentiating Eq. ( 4 ) and substituting Eq. ( 14 ), we obtain

where \({\lambda }_{ij}\) is the share of factor \(i\) in the production of good \(j\) .

Differentiating Eq. ( 5 ) and substituting Eq. ( 13 ), we obtain

Differentiating Eq. ( 6 ) and using Eq. ( 15 ), we have

Differentiating Eq. ( 7 ) and substituting Eq. ( 16 ), we obtain

Differentiating Eq. ( 8 ) and considering Eq. ( 19 ), we obtain

Equations ( 17 ), ( 18 ), and ( 20 )–( 24 ) are expressed in matrix form as follows:

This is the system of the equations describing the supply side of the economy.

3 Comparative statics: supply side analysis

The supply side of the economy (Eqs. ( 1 )–( 8 )) determines the outputs and factor prices (and, therefore, factor demands). In this section, utilizing Eq. ( 25 ), we examine the effects of a stricter environmental regulation and an improvement in the tourism terms of trade on outputs and factor prices.

3.1 Environmental regulation

In this subsection, we consider the effects of a stricter environmental regulation. A stricter environmental regulation means a reduction in emission permits in this regard ( \(dZ<0\) ).

From Eq. ( 17 ), we have

Since \({p}_{X}\) is unchanged, an increase in the skilled wage is balanced by a decrease in the rental rate of capital. Equation ( 26 ) implies \({\widehat{w}}_{S}-\widehat{q}={\widehat{w}}_{S}/{\theta }_{KX}\) . From Eq. ( 21 ), we have

This means that an increase in the output of traded good \(X\) raises the wage of skilled labor, which is a specific input to that sector.

Substituting Eqs. ( 26 ) and ( 27 ) into Eq. ( 20 ), we obtain

which states that an increase in the output of traded good reduces the output of tourism infrastructure by extracting capital input from that industry. Equations ( 26 )–( 28 ) show that \(\widehat{q}\) , \(\widehat{X}\) , and \(\widehat{M}\) are proportional to \({\widehat{w}}_{S}\) .

From Eqs. ( 22 ) and ( 23 ), we have

which implies that if the number of pollution permits is unchanged, \({\widehat{w}}_{L}=\widehat{r}\) holds.

Solving Eq. ( 25 ), we obtain (see Appendix A )

where \(\Delta \equiv {\sigma }_{T}[{\lambda }_{KX}{\sigma }_{X}(1-\xi )+{\lambda }_{KM}{\theta }_{SX}]>0\) . Footnote 13

The qualitative effects of a reduction in emissions are ambiguous and depend on the elasticity of substitution in the tourism sector \({\sigma }_{T}\) . From Eq. ( 30 ), a reduction in emissions decreases the production of the tourism service if and only if

We can immediately show that \(A<{\theta }_{LT}\) .

From Eq. ( 32 ), the necessary and sufficient condition for a reduction in emissions to decrease the wage of unskilled labor is

Using Eq. ( 33 ), a reduction in emission decreases the price of emission permits if and only if

Since \(\xi <1\) , \(C>{\theta }_{LT}\) holds.

It is straightforward to show that \(A>B\) since

where \(m\equiv \frac{{\lambda }_{KM}{\theta }_{SX}{\theta }_{ZT}}{{\lambda }_{KX}{\sigma }_{X}}>0\) .

Therefore, we have the following relationships in magnitude:

The above results are summarized in Table 1 , which shows how the comparative static results with respect to \(Z\) depend on \({\sigma }_{T}\) , with threshold values such as \(B\) , \(A\) , \({\theta }_{LT}\) , and \(C\) .

The intuition for the above results is as follows. When the elasticity of substitution in the tourism sector \({\sigma }_{T}\) is sufficiently low, a decrease in emission permits \(Z\) raises its price \(r\) significantly since pollution is hardly substituted by unskilled labor. Therefore, the revenue from selling emission permits \(rZ\) and the output of tourism infrastructure \(M\) increases (see Eq. ( 8 )). Footnote 14 If an increase in \(M\) is significant, the output of tourism industry \(T\) rises despite the reduction in emission permits \(Z\) . Consequently, the wage of unskilled labor, which is a specific factor into the tourism sector, increases. At the same time, capital flows from the manufacturing sector, leading to a decrease in the output of manufacturing good \(X\) . The decrease in the output of the manufacturing good reduces the wage of skilled labor, which is a specific factor into that industry. Since the price of the manufacturing good is constant, the decrease in the wage of skilled labor is balanced by the increase in the rental rate of capital (see Eq. ( 17 )).

When \({\sigma }_{T}\) is sufficiently high, the stricter environmental regulation decreases permit price \(r\) since the demand for emission permits is largely substituted by unskilled labor. Thus, the revenue from emission permits and the output of tourism infrastructure decrease. It follows that the output of the tourism service falls due to a decrease in both emission permits and positive externality of tourism infrastructure. Additionally, the wage of unskilled labor decreases despite the initial increase in demand. Meanwhile, capital flows from the tourism infrastructure sector to the manufacturing sector, leading to an increase in the output of the manufacturing sector. The increased output of the manufacturing good raises the wage of skilled labor due to an increase in demand.

Thus, we have the following proposition.

Proposition 1

Suppose that the tourism terms of trade \({p}_{T}\) are constant. When the elasticity of substitution in the tourism sector is sufficiently low, a stricter environmental regulation expands the tourism and tourism infrastructure sectors and contracts the manufacturing sector. This narrows the wage inequality between skilled and unskilled labor. The rental rate of capital and the price of emission permits rise. As the elasticity of substitution in the tourism sector increases, all the above results are reversed.

The effects on the output of the tourism service and the unskilled wage rate also depend on the positive externality of tourism infrastructure \(\xi\) . When \({\sigma }_{T}<{\theta }_{LT}\) , a stricter environmental regulation raises the output of tourism infrastructure \(M\) . If \(\xi\) is sufficiently high, the output of the tourism service significantly increases, and the wage of unskilled labor rises. In other words, the higher \(\xi\) is, the higher the possibility of an increase in the output of the tourism service and the unskilled wage rate. When \({\sigma }_{T}>{\theta }_{LT}\) , both the output of the tourism service and the unskilled wage rate unambiguously decrease since \({B<A<\theta }_{LT}<{\sigma }_{T}\) . In this case, the output of the tourism service falls due to a decrease in both pollution permits and positive externality of tourism infrastructure.

Similarly, the effect on the price of emission permits depends on \(\xi\) . When \({\sigma }_{T}>{\theta }_{LT}\) , the output of tourism infrastructure is decreased by the stricter environmental regulation. If \(\xi\) is sufficiently high, the drop in the output of the tourism service increases. Subsequently, the price of emission permits decreases since a decline in demand for emission permits outweighs the decrease in supply. When \({\sigma }_{T}<{\theta }_{LT}\) , the output of tourism infrastructure increases and the price of emission permits unambiguously rises because \({\sigma }_{T}<{\theta }_{LT}<C\) . This means that the decrease in the supply of emission permits increases the price of emission permits even when the output of the tourism service falls.

In particular, the case of \(\widehat{T}/\widehat{Z}<0\) (the stricter environmental regulation increases the output of the tourism service) is paradoxical in the usual sense because pollution emission is a specific input into the tourism industry, and we try to explain this result graphically. For this purpose, we introduce the production possibility curve or the transformation curve. The properties of the production possibility curve in our model are proved in Appendix B and summarized in the following proposition.

Proposition 2

The production possibility curve in our model is flatter (steeper) than the price line if and only if the marginal value product of tourism infrastructure is greater (lesser) than its price. The curve is strictly concave to the origin.

In the absence of the Lindahl pricing rule, the tangency property with respect to the production possibility curve does not necessarily hold. From Eq. ( 8 ), we obtain \({p}_{T}\frac{\partial T}{\partial M}-{p}_{M}={p}_{M}\left(\frac{\xi }{{\theta }_{ZT}}-1\right)\) . It follows that the Lindahl pricing requires \(\xi ={\theta }_{ZT}\) ; otherwise, the marginal value product of tourism infrastructure is greater (lesser) than its price if and only if \(\xi >(<){\theta }_{ZT}\) . In what follows, we focus on the case of \(\xi >{\theta }_{ZT}\) since the national government otherwise has no incentive to construct tourism infrastructure. The initial production possibility curve is depicted by curve ABC. See Figs. 1 and 2 . When \(Z\) is decreased, the curve shifts inward to AB’C’. At the same time, the price line shifts upward if the elasticity of substitution in the tourism sector is sufficiently low; otherwise, it shifts downward. Footnote 15 Accordingly, the production point moves from point B to B’, leading to a decrease in \(X\) and an increase in \(T\) . Figure 1 (Fig. 2 ) corresponds to the case where the stricter environmental regulation decreases (increases) total revenue.

The case of a stricter environmental regulation decreasing the total revenue

The case of a stricter environmental regulation increasing the total revenue

3.2 Improvement in the tourism terms of trade

In this subsection, we investigate the effects of an improvement in the tourism terms of trade \({p}_{T}\) . Note that Eqs. ( 26 ), ( 27 ), and ( 28 ) still hold. Substituting Eqs. ( 26 ) and ( 28 ) into Eq. ( 24 ), we obtain

Equation ( 29 ) implies \({\widehat{w}}_{L}/{\widehat{p}}_{T}=\widehat{r}/{\widehat{p}}_{T}\) .

Substituting Eq. ( 28 ) into Eq. ( 22 ) and considering \({\widehat{w}}_{L}/{\widehat{p}}_{T}-\widehat{r}/{\widehat{p}}_{T}=0\) from Eq. ( 29 ), we have

The effects of improvement in the tourism terms of trade on the price of emission permits and the wage of unskilled labor have magnification effects ( \(\widehat{r}/{\widehat{p}}_{T}={\widehat{w}}_{L}/{\widehat{p}}_{T}>1\) ). This result is different from that of Nakai et al. ( 2018 ), where pollution is a general input to both manufacturing and tourism industries, while unskilled labor is a specific input into the tourism sector. In Nakai et al. ( 2018 ), the effects of improvement in the tourism terms of trade have a magnification effect on the wages of unskilled labor ( \({\widehat{w}}_{L}/{\widehat{p}}_{T}>1\) ), and not on the price of emission permits ( \(\widehat{r}/{\widehat{p}}_{T}<1\) ).

Since we do not assume the Lindahl pricing rule, the traditional reciprocity relationship (i.e., \(\partial T/\partial Z=\partial r/\partial {p}_{T}\) ) does not necessarily hold. Footnote 16 Therefore, we have obtained an interesting result that the stricter environmental regulation may expand the tourism sector.

The effects of an increase in \({p}_{T}\) are summarized in Table 2 and proposition 3.

Proposition 3

An improvement in the tourism terms of trade expands the tourism and tourism infrastructure sectors, while it contracts the manufacturing sector. This narrows the wage inequality between skilled and unskilled labor. The rental rate of capital and the price of emission permits rise.

The intuition is straightforward. The improvement in the tourism terms of trade expands the tourism sector and, thus, the wage of unskilled labor and the price of emission permits rise. The revenue from selling pollution permits \(rZ\) and the output of tourism infrastructure increase at the expense of the manufacturing sector; this leads to a decrease in the wage of skilled labor. Since the price of the manufacturing good is unchanged, the rental rate of capital rises.

4 Total effect of the environmental regulation

4.1 the effects on the tourism terms of trade and welfare.

The previous sections treated the tourism terms of trade \({p}_{T}\) as a constant. However, \({p}_{T}\) is eventually determined by the market equilibrium condition of the domestic tourism service, that is, the supply of and demand for it. In this subsection, we consider the effects of the stricter environmental regulation, considering that \({p}_{T}\) is not a constant.

To determine the price of the tourism service, we need to introduce the demand side of the economy. Suppose that both domestic residents and foreign tourists consume the manufacturing good and the domestic tourism service. The demand side of the economy is represented by the expenditure function of domestic residents and the demand function of foreign tourists. The expenditure function is defined as Footnote 17

where \({C}_{X}\) is the consumption of the manufacturing good by domestic residents, \({C}_{T}\) the consumption of the domestic tourism service by domestic residents, and \(u\) the level of utility. \(a<0\) is a parameter. The utility function has the property that the marginal utility from the tourism service decreases with the amount of pollution. For the marginal utility of pollution to be negative ( \(\frac{\partial u}{\partial Z}<0\) ), we assume \({C}_{T}>1\) . The expenditure function is derived as follows:

Applying the envelope theorem, we obtain the compensated demand for the tourism service: \({E}_{T}\equiv \frac{\partial E}{\partial {p}_{T}}={C}_{T}={\left(\frac{{e}^{u}{Z}^{a}{p}_{X}}{{p}_{T}}\right)}^{\frac{1}{{1+Z}^{a}}}\) . The downward sloping demand function implies \({E}_{TT}\equiv {\partial }^{2}E/\partial {p}_{T}^{2}<0\) . \({E}_{Z}\equiv \frac{\partial E}{\partial Z}=-\frac{a{Z}^{a-1}E}{{(1+{Z}^{a})}^{2}}\text{ln}\left(\frac{{e}^{u}{Z}^{a}{p}_{X}}{{p}_{T}}\right)>0\) denotes the marginal damage to domestic residents caused by pollution. \({E}_{u}\equiv \frac{\partial E}{\partial u}>0\) represents the inverse of the marginal utility of income. The effect of the stricter environmental regulation on the compensated demand for the tourism service is given by

The first term indicates the effect that a decrease in pollution reduces the amount of compensated demand required to offset the disutility from pollution, while the second term indicates that the decrease in pollution raises the attractiveness of the tourism service. If the latter effect outweighs the former, the stricter environmental regulation increases the compensated demand for the tourism service. Footnote 18

Foreign tourists also consume the manufacturing good and the domestic tourism service. Their utility function is given by \({u}^{*}=\text{ln}{D}_{X}+{Z}^{\alpha }\text{ln}{D}_{T}\) , where \({D}_{X}\) is the consumption of the manufacturing good by foreign tourists, and \({D}_{T}\) is the consumption of the domestic tourism service by foreign tourists. \({\alpha }<0\) is a parameter. For the marginal utility of pollution to be negative, we assume \({D}_{T}>1\) . Foreign tourists’ demand for the domestic tourism service is derived as \({D}_{T}=\frac{{Z}^{\alpha }}{1+{Z}^{\alpha }}\frac{{Y}^{*}}{{p}_{T}}\) , where \({Y}^{*}\) is the budget of foreign tourists and is exogenously given. Note that \(\frac{{\partial D}_{T}}{\partial Z}<0\) because a decrease in pollution increases the attractiveness of the tourism service. Footnote 19

The supply side of the economy is characterized by the revenue function:

The usual envelope theorem does not necessarily hold as the Lindahl pricing rule is not assumed. The properties of the revenue function with a positive externality of the tourism infrastructure are given in Appendix C .

Now, we can derive the equilibrium conditions for both the demand and supply sides of the economy. First, the budget constraint of the economy is given by

which states that the total expenditure equals the total revenue.

Second, the market equilibrium condition of the tourism service is

Here, the LHS denotes the demand for the domestic tourism service, while the RHS is its supply.

The above two equations simultaneously determine the tourism terms of trade \({p}_{T}\) and domestic welfare \(u\) . We analyze the effects of the stricter environmental regulation on \({p}_{T}\) and \(u\) . Totally differentiating Eqs. ( 37 ) and ( 38 ), we obtain

where \(\Gamma \equiv {p}_{T}\frac{\partial T}{\partial M}-{p}_{M}\) is the difference between the marginal value product of tourism infrastructure and its shadow price, \({S}_{T}\equiv \partial T/\partial {p}_{T}-{E}_{TT}-\partial {D}_{T}/\partial {p}_{T}>0\) represents the slope of the excess supply function of the tourism service, and subscripts with respect to the expenditure function denote partial derivatives, for example, \({E}_{Tu}\equiv {\partial }^{2}E/\partial u\partial {p}_{T}\) . Note that \({E}_{Tu}>0\) is the income effect on the tourism service. Note also that \(\partial T/\partial {p}_{T}>0\) and \(\partial M/\partial {p}_{T}>0\) from the analysis in Sect.  3.2 . Let \({\Delta }^{*}\) be the determinant of the 2 \(\times\) 2 matrix on the LHS of Eq. ( 39 ). The stability condition then requires \({\Delta }^{*}>0\) . Footnote 20 Solving Eq. ( 39 ), we obtain

Emission reduction affects the tourism terms of trade and domestic welfare through the two conventional channels, as stated by Beladi et al. ( 2009 ) and Yanase ( 2017 ). On the one hand, if a pollution reduction decreases the domestic excess supply of the tourism service \((\frac{\partial }{\partial Z}\left(T-{D}_{T}-{E}_{T}\right)=\frac{\partial T}{\partial Z}-\frac{\partial {D}_{T}}{\partial Z}-{E}_{TZ}>0)\) , the price of the tourism service rises. These positive terms of trade effect improve domestic welfare. On the other hand, if the marginal damage of pollution to domestic residents is greater than the marginal cost of pollution emission ( \({E}_{Z}>r\) ), the pollution reduction increases the real income of domestic residents. This positive income effect improves the tourism terms of trade. In Eq. ( 40 ), an additional effect \((-{E}_{Tu}\Gamma \frac{\partial M}{\partial Z})\) exists. In the explanation below, we assume the following conditions: (1) \(\Gamma >0\) (the marginal value product of tourism infrastructure is greater than its price), (2) \(\partial M/\partial Z<0\) (the output of tourism infrastructure is increased by the stricter environmental regulation), and (3) \(\frac{\partial T}{\partial Z}-{E}_{TZ}-\frac{\partial {D}_{T}}{\partial Z}>0\) (the excess supply of the tourism service decreases with the stricter environmental regulation). The effect of the additional term can be explained as follows: a decrease in pollution raises tourism infrastructure, which in turn increases the total revenue and, thus, demand for the tourism service. Footnote 21

Next, we consider the effect on welfare. In addition to the two aforementioned conventional channels, there are two other effects. First, a decrease in the excess supply of tourism service raises the tourism terms of trade, which in turn increases tourism infrastructure ( \(\partial M/\partial {p}_{T}>0\) ). Thus, the total revenue and welfare of domestic residents increase. This effect is captured by the term \(\Gamma \frac{\partial M}{\partial {P}_{T}}(\frac{\partial T}{\partial Z}-{E}_{TZ}-\frac{\partial {D}_{T}}{\partial Z})\) . Second, an increase in tourism infrastructure directly raises total revenue and welfare. This effect is represented by the term ( \(-{S}_{T}\Gamma \frac{\partial M}{\partial Z}\) ). If conditions (1)–(3) are satisfied, both these effects increase domestic welfare. Note also that both the effects arise from the difference between the marginal value product of tourism infrastructure and its price (i.e., the term \(\Gamma\) ). We call the effects arising from \(\Gamma\) the \(\Gamma\) -channel. If conditions (1)–(3) are satisfied, the \(\Gamma\) -channel improves the tourism terms of trade and domestic welfare.

Thus, we can establish the following proposition.

Proposition 4

In addition to the two traditional channels, there is an additional channel arising from the difference between the marginal value product of tourism infrastructure and its price. The additional channel improves the tourism terms of trade and domestic welfare if the following three conditions are satisfied: (1) the marginal value product of tourism infrastructure is greater than its price, (2) the output of tourism infrastructure is increased by a stricter environmental regulation, and (3) the excess supply of a tourism service decreases with a stricter environmental regulation.

The first condition is likely to hold when the marginal value product of the tourism infrastructure is sufficiently high. The second condition is satisfied if and only if the elasticity of substitution in the tourism industry is sufficiently low to increase revenue from selling pollution permits. The third condition tends to hold when the output of the tourism service is decreased by the stricter environmental regulation, which occurs if the elasticity of substitution in that sector is not very low. Therefore, for both conditions (2) and (3) to hold simultaneously, the elasticity of substitution in the tourism sector must be a moderately small value. By numerical simulations in Appendix D , we show that there exist parameter values that satisfy the conditions from (1) to (3).

4.2 Effects on outputs and factor prices

In this subsection, we examine the effects of the stricter environmental regulation on outputs and factor prices, considering that the tourism terms of trade are endogenous. The total effect (including the change in tourism terms of trade) of the environmental regulation on each endogenous variable is

where \(\Theta =\text{X},T,M,{w}_{S},{w}_{L},q,r\) . The first term on the RHS represents the direct effect of the environmental regulation, while the second term represents the indirect effect that arises from the change in the tourism terms of trade. Since the sign of the direct effect is ambiguous, we consider the necessary and sufficient conditions for the indirect effect to be dominant. The indirect effect is proportional to the change in the tourism terms of trade and, thus, the indirect effect dominates the direct effect if the tourism terms of trade effect \(\left|\frac{Z}{{p}_{T}}\frac{d{p}_{T}}{dZ}\right|\) is sufficiently high.

Substituting Eqs. ( 31 ) and ( 36 ) into Eq. ( 42 ) for \(\Theta ={w}_{S}\) , the stricter environmental regulation decreases the wage of skilled labor if and only if

From Eqs. ( 26 ), ( 27 ), ( 28 ), and ( 42 ) the total effects on \(q\) , \(X\) , and \(M\) are proportional to those on \({w}_{S}\) .

Similarly, substituting Eqs. ( 30 ), ( 35 ), and ( 36 ) into Eq. ( 42 ) for \(\Theta =T\) , a decrease in pollution reduces the production of tourism service \(T\) if and only if

Substituting Eqs. ( 29 ), ( 32 ), ( 34 ), and ( 36 ) into Eq. ( 42 ) for \(\Theta ={w}_{L}\) , the necessary and sufficient condition for a decrease in pollution to reduce the wage of unskilled labor \({w}_{L}\) is

Finally, substituting Eqs. ( 33 ), ( 34 ), and ( 36 ) into Eq. ( 42 ) for \(\Theta =r\) , the amount of pollution and the price of emission permits \(r\) move in the same direction if and only if

By the straightforward calculation, we have \(H-D=\frac{1-\xi +m/{\theta }_{ZT}}{1+m/{\theta }_{ZT}}>0\) and \(H-G=\frac{1-\xi +m/{\theta }_{ZT}}{{\sigma }_{T}(1+m/{\theta }_{ZT})}>0\) . From this, it is easy to show that \(F<D<H\) . It follows that there are three cases to be considered: (1) when \({\sigma }_{T}<\frac{\xi }{{\theta }_{ZT}+m+\xi }\) , \(G<F<D<H\) ; (2) when \(\frac{\xi }{{\theta }_{ZT}+m+\xi }<{\sigma }_{T}<1\) , \(F<G<D<H\) ; and (3) when \({\sigma }_{T}>1\) , \(F<D<G<H\) . The results are summarized in Tables 3 , 4 and 5 . Footnote 22

The above results are summarized by the following proposition.

Proposition 5

When \(\frac{Z}{{p}_{T}}\frac{d{p}_{T}}{dZ}<min (F,G)\) , a stricter environmental regulation expands the tourism and tourism infrastructure sectors while it contracts the manufacturing sector. It narrows the wage inequality between skilled and unskilled labor. The rental rate of capital and the price of emission permits rise. If \(\frac{Z}{{p}_{T}}\frac{d{p}_{T}}{dZ}>H\) , all the above results are reversed.

Focusing on the total effect on domestic wage inequality, we have the following proposition.

Proposition 6

When \(\frac{Z}{{p}_{T}}\frac{d{p}_{T}}{dZ}\le min (D,G)\) , a stricter environmental regulation unambiguously narrows domestic wage inequality. However, if \(\frac{Z}{{p}_{T}}\frac{d{p}_{T}}{dZ}\ge max (D,G)\) , there is a trade-off between reducing pollution and narrowing wage inequality.

If the tourism terms of trade improve significantly, a stricter environmental regulation can provide a further benefit in improving domestic welfare. Footnote 23 This result is consistent with those of Chao et al. ( 2012 ) and Nakai et al. ( 2018 ).

When the production function of the tourism sector is Cobb–Douglas (i.e., \({\sigma }_{T}=1\) ), the above analysis becomes quite simple (see Appendix E ). In this case, at constant tourism terms of trade, the effect of the stricter environmental regulation on the price of pollution permits is ambiguous. However, the revenue from pollution permits \(rZ\) unambiguously declines, leading to a decrease in the output of tourism infrastructure. This results in a decline in the output of the tourism service and the wage of unskilled labor. At the same time, capital flows from the tourism infrastructure sector to the traded good sector. It follows that the output of the traded good and the wage of skilled labor rise.

5 Conclusions

This study sets up a small open developing tourism economy with tourism infrastructure and examines the welfare, production, and income distribution effects of a stricter environmental policy. The tourism sector generates pollution in the sense that it requires pollution as an input. Since the Lindahl pricing rule is not assumed, the usual envelope theorem and reciprocity relationship do not necessarily hold. Thus, we can obtain interesting comparative static results. If the elasticity of substitution in the tourism sector is sufficiently low, a stricter environmental regulation paradoxically expands the tourism sector even under the constant tourism terms of trade. At the same time, the wage inequality between skilled and unskilled labor narrows.

This study provides new insights regarding welfare concerns. In addition to the two conventional channels pointed out by Beladi et al. ( 2009 ) and Yanase ( 2017 ), this study contains an additional channel through which a stricter environmental regulation affects the tourism terms of trade and domestic welfare. Furthermore, the new channel, which arises from the difference between the marginal value product of tourism infrastructure and its price, increases the tourism terms of trade and domestic welfare if (1) the marginal value product of tourism infrastructure is greater than its price, (2) the output of tourism infrastructure is increased by a stricter environmental regulation, and (3) the excess supply of a tourism service decreases with a stricter environmental regulation.

Before closing this paper, we will state some topics for future research. In this study, we have considered that tourism infrastructure only enhances the productivity of the tourism industry and includes no congestion effect. However, some tourism infrastructures such as airports and highways contribute to several industries and include the congestion effect, where an increase in users lowers efficiency. Thus, it is important to consider this type of infrastructure. If the infrastructure contributes to almost all industries in the economy, the national government can finance the cost of infrastructure by taxing the income of residents in the economy.

We assume that the tourism industry is under perfect competition. It will be interesting to consider another market structure, for example, duopoly or monopolistic competition cases. Footnote 24 , Footnote 25

We have considered only environmental regulation as the national government’s policy instrument. It may be possible to consider an optimal policy mix of environmental regulation and import tariff, as in Chao et al. ( 2008 ) and Yanase ( 2017 ).

In this study, pollution is emitted from the production of the tourism service. However, if pollution is generated from consumption, the corresponding environmental policy may be a consumption tax or license fee to enter a tourist spot. The analysis of consumption tax or license fee would yield new results. All these issues are left for future research.

Code availability

Numerical Simulations utilize MATLAB R2021a.

Availability of data and material

The data that support the findings of this study are available on the website of the Japan Fair Trade Commission. See Japan Fair Trade Commission ( 2016 ).

In Japan, especially in Kyoto, an excessive tourism boom has caused over-tourism, bringing serious damages to the local community. However, this congestion phenomenon was suddenly terminated with the outbreak of the COVID-19 pandemic. Since July 2020, the Japanese government started "Go to travel campaign," subsidizing the tourism-related industries. This is expected to increase the number of domestic tourists. In addition, we hope that we will be able to overcome those negative effects of the COVID-19 pandemic after a period of time as in the case of the other pandemics in the past such as Spanish flu and Soviet flu. We also believe that there will be rapid growth in inbound tourism as a repercussion of immigration control. Thus, negative aspects of the tourism boom are still worth considering.

For an analysis of the environmental problem in developing countries where international capital flows from developed countries increase domestic pollution, see Beladi et al. ( 2000 ).

We treat low- and middle-income countries as developing countries.

According to Aynalem et al. ( 2016 , p. 3) and UNWTO ( 2014 , p. 28), the tourism employment is characterized by the following factors: (1) seasonality, (2) part-time and/or excessive hours of work, (3) low-paid (or unpaid) family labor, and (4) informal or sometimes illegal labor where measurement is notably more difficult.

Since the pollution tax provides a double dividend in reducing the amount of pollution and improving the tourism terms of trade, the optimal pollution tax rate exceeds the Pigouvian level in the case of exogenous tourism where the spending of foreign tourists is treated as a constant.

For an approach treating emission as an input into production, see Beladi et al. ( 2013 ), Copeland and Taylor ( 2003 ), Ishikawa and Kiyono ( 2006 ), and Oladi and Beladi ( 2015 ).

Okamoto ( 1985 ) assumed the Lindahl pricing in a general equilibrium model with public intermediate good. In his model, the tangency property holds.

Even if the tourism sector industry requires both capital and skilled labor, the main results do not change as long as the manufacturing sector is skilled labor intensive relative to the tourism infrastructure sector.

Yanase ( 2015 ) made the same assumption.

Since cost minimization is required in tourism infrastructure sector, cost equals revenue.

The price of tourism service p T is to be determined by the demand and supply of domestic tourism service in Sect.  4 . This approach is adopted by Chao et al. ( 2010 ).

Note that \({w}_{L}d{a}_{LN}+rd{a}_{ZN}=0\) holds by the cost minimization in the tourism sector since each firm in that sector does not take into account the positive externality of tourism infrastructure, where \({a}_{LN}\equiv g\cdot {a}_{LT}\) and \({a}_{ZN}\equiv g\cdot {a}_{ZT}\) are input coefficients of the tourism industry in the absence of tourism infrastructure.

From the assumptions \(g^{\prime\prime} <0\) and \(g(0)=1\) , we have \(\xi <1\) .

From Eq. ( 33 ), we have \(\widehat{r}+\widehat{Z}=\frac{({\sigma }_{T}-{\theta }_{LT})({\lambda }_{KM}{\theta }_{SX}+{\lambda }_{KX}{\sigma }_{X})}{\Delta }\widehat{Z}\) . Therefore, if \({\sigma }_{T}\) is less than \({\theta }_{LT}\) , a reduction in emission raises the revenue from selling emission permits \(rZ\) .

The total revenue \(R\) is given by \(R={p}_{X}X+{p}_{T}T\) , which is depicted by the price line in the ( \(T,X\) ) plane. The slope of the price line is equal to \({-p}_{T}/{p}_{X}\) . An increase (A decrease) in \(R\) shifts upward (downward) the price line. See Figs. 1 and 2 . The change in total revenue due to the stricter environmental regulation is \(\frac{\partial R}{\partial Z}\equiv {R}_{Z}=r+{p}_{M}\left(\frac{\xi }{{\theta }_{ZT}}-1\right)\frac{\partial M}{\partial Z}\) (See Eq. ( C.3 ) of Appendix C ). The first term is positive while the second is negative (recall that \({p}_{M}\left(\frac{\xi }{{\theta }_{ZT}}-1\right)>0\) and \(\frac{\partial M}{\partial Z}<0\) ). Note that the lesser the elasticity of substitution in the tourism sector \({\sigma }_{T}\) , the greater the absolute value of \(\frac{\partial M}{\partial Z}\) .

In the presence of the Lindahl pricing rule ( \({p}_{T}\frac{\partial T}{\partial M}={p}_{M}\) ), the usual tangency property ( \(\frac{\partial R}{\partial {p}_{T}}=T\) and \(\frac{\partial R}{\partial Z}=r\) ) always holds (see Appendix C ), where \(R\) —the total revenue—is also the revenue function (to be defined in Sect.  4.1 ). By applying the Young’s theorem to the revenue function, we obtain the reciprocity relationship: \(\frac{\partial T}{\partial Z}=\frac{{\partial }^{2}R}{\partial Z\partial {p}_{T}}=\frac{{\partial }^{2}R}{\partial {p}_{T}\partial Z}=\frac{\partial r}{\partial {p}_{T}}\) . However, if \({p}_{T}\frac{\partial T}{\partial M}\ne {p}_{M}\) , the reciprocity relationship does not hold except in the special case of \({\sigma }_{T}=1\) (see Appendix E ).

This specification of the utility function is suggested by Noritsugu Nakanishi.

Beladi et al. ( 2009 ) and Chao et al. ( 2008 ) assume a multiplicative utility function \(U({C}_{X},{C}_{T},Z)=v({C}_{X},{C}_{T})/h(Z)\) while Chao et al. ( 2012 ) and Chao and Sgro ( 2013 ) adopt an additively separable utility function \(U\left({C}_{X},{C}_{T},Z\right)=v\left({C}_{X},{C}_{T}\right)-h(Z)\) , where \({h}^{^{\prime}}\left(Z\right)>0\) . If \(v\left({C}_{X},{C}_{T}\right)\) is a Cobb–Douglas function, the compensated demand for the tourism service unambiguously increases with the amount of pollution, that is, \({E}_{TZ}>0\) . See Yanase ( 2017 , note 15).

If the foreign tourists' utility function is a multiplicative form \({U}^{*}({D}_{X},{D}_{T},Z)={v}^{*}({D}_{X},{D}_{T})/{h}^{*}(Z)\) or an additively separable form \({U}^{*}\left({D}_{X},{D}_{T},Z\right)={v}^{*}\left({D}_{X},{D}_{T}\right)-{h}^{*}(Z)\) , where \({v}^{*}({D}_{X},{D}_{T})\) is an increasing and strictly quasi-concave function and \(h^{*\prime } \left( Z \right) > 0\) , the ordinary demand function does not depend on the amount of pollution. This is because the marginal rate of substitution in consumption does not depend on the amount of pollution.

Let \(\Omega \equiv {E}_{T}+{D}_{T}-T\) be the domestic excess demand for tourism service. From Eqs. ( 37 ) and ( 38 ), we have \(d{p}_{T}/d\Omega =-{E}_{u}/{\Delta }^{*}\) . Hence, the stability of tourism service market requires \({\Delta }^{*}>0\) .

Note that the change in total revenue is given by \(dR=Xd{p}_{X}+Td{p}_{T}+{w}_{S}dS+{w}_{L}dL+rdZ+qdK+\Gamma dM\) . See Appendix C .

Straight calculation shows that \(G-F>0\leftrightarrow {\sigma }_{T}>\frac{\xi }{{\theta }_{ZT}+m+\xi }(>A)\) and \(D-G>0\leftrightarrow {\sigma }_{T}<1\) .

Differentiating Eq. ( 37 ) and substituting Eq. ( 38 ), we obtain \({E}_{u}du=\left({D}_{T}+\Gamma \frac{\partial M}{\partial {P}_{T}}\right)\text{d}{p}_{T}-({E}_{Z}-r-\Gamma \frac{\partial M}{\partial Z})\text{d}Z\) . It follows that, ceteris paribus , an improvement in tourism terms of trade raises domestic welfare.

Chao et al. ( 2012 ) assumed that tourism industry is under oligopoly.

Some tourism industries (e.g., hotel and travel agency business) consist of many agents (see Japan Fair Trade Commission ( 2016 )). Therefore, it is reasonable to consider tourism industry to be under perfect competition or monopolistic competition.

We have used Eqs. ( 3 ) and ( 4 ).

We have used Eqs. ( 16 ), ( 17 ), ( 20 ), ( 21 ) and ( 24 ). Also note that \({\widehat{w}}_{L}=\widehat{r}\) from Eq. ( 29 ). Substituting \({\widehat{w}}_{L}=\widehat{r}\) into Eq. ( 22 ) yields \(\widehat{M}=\widehat{T}/\xi\) .

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Acknowledgements

This is a substantially revised and updated version of the paper previously entitled “Tourism infrastructure, the environment, and international trade”. We are grateful to Muneyuki Saito; Shigemi Yabuuchi; and participants at the Kansai branch meeting of the Japan Society of International Economics (JSIE), Kobe International Economic Studies (KIES), and the 78th annual meeting of the JSIE for their helpful comments and suggestions. We also would like to thank Editage (www.editage.com) for English language editing. All remaining errors are ours.

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Appendix A. Derivation of Eqs. ( 30 )–( 33 ), and ( 36 )

Let \(\Delta\) be the determinant of the 7 × 7 matrix on the LHS of Eq. ( 25 ):

Add the fourth column to the sixth column and then the fifth column to the seventh column to obtain

Subtract the fifth row from the sixth row to obtain

Expand by the second column to obtain

Expand by the fifth row to obtain

Add the second row to the fifth row to obtain

Expand by the fifth column to obtain

Multiply the third row by \({\lambda }_{KX}\) and then subtract from the second row to obtain

Expand by the first column to obtain

By the Cramer’s rule, the numerator of \(\widehat{T}/\widehat{Z}\) is

Similarly, the numerator of \({\widehat{w}}_{S}/\widehat{Z}\) is

The numerator of \({\widehat{w}}_{L}/\widehat{Z}\) is

The numerator of \(\widehat{r}/\widehat{Z}\) is

The numerator of \({\widehat{w}}_{S}/{\widehat{p}}_{T}\) is

Appendix B. Shape of the production possibility curve

The first-order conditions for profit maximization in manufacturing sector are

Similarly, the first-order conditions for profit maximization in the tourism sector are

Therefore, we have Footnote 26

where \(\Gamma \equiv {p}_{T}\frac{\partial T}{\partial M}-{p}_{M}\) is the difference between the marginal value product of tourism infrastructure and its price. Using the budget constraint of the government (8), we can rewrite \(\Gamma ={p}_{M}\left(\frac{\xi }{{\theta }_{ZT}}-1\right)\) . Keeping the factor endowments unchanged, the slope of the production possibility curve is given by

where we have used Eq. ( 8 ) and \(\frac{dM}{dT}=\frac{\widehat{M}}{\widehat{T}}\frac{M}{T}=\frac{M}{T\xi }\) from Eqs. ( 22 ) and ( 29 ). Equation ( B.6 ) implies that the production possibility curve is flatter than the price line if and only if \(\Gamma >0\) (i.e., \(\xi >{\theta }_{ZT}\) ).

The absolute value of the slope of the production possibility curve is rewritten as

where the rate of change of the RHS is given by Footnote 27

Therefore, we can conclude that \({\text{d}}^{2}X/\text{d}{T}^{2}<0\) . It follows that the production possibility curve is strictly concave to the origin.

Appendix C. Properties of the revenue function

The total revenue is defined as

Considering Eq. ( B.5 ), the change in the total revenue is given by

The last term in Eq. ( C.1 ) implies that an increase in tourism infrastructure raises the total revenue \(R\) if and only if the marginal value product of tourism infrastructure is larger than its price (i.e., \({p}_{T}\frac{\partial T}{\partial M}>{p}_{M}\) ).

From Eq. ( C.1 ), we obtain

Thus, the envelope theorem holds if \(\Gamma =0\) .

Appendix D. Numerical simulations

Since conditions (2) and (3) in Proposition 4 .1 seem to be inconsistent with each other, we resort to numerical simulations to find out a set of parameter values that satisfy the conditions from (1) to (3). Numerical Simulations utilize MATLAB R2021a.

For this purpose, we specify the production function. Suppose that the production function of the traded good is a Cobb–Douglas function

where \({A}_{X}\) denotes the productivity parameter for the traded good sector, and \(\updelta \in (\text{0,1})\) is the factor cost share of skilled labor. Thus, we have \({\theta }_{SX}=\updelta\) and \({\theta }_{KX}=1-\updelta\) . The associated unit cost is then given by \(\frac{{({w}_{S})}^{\delta }{q}^{1-\delta }}{{{A}_{X}\delta }^{\delta }{(1-\delta )}^{1-\delta }}\) .

The production function of the tourism service is assumed to be the constant elasticity of substitution (CES) function:

where \(\gamma \in (\text{0,1})\) and \(\rho \ge -1\) are parameters. It is well known that the elasticity of substitution is \({\sigma }_{T}=1/(1+\rho )\) . We specify \({g\left(M\right)=M}^{\xi }\) , where \(\xi \in (\text{0,1})\) is a constant.

From Eqs. ( 6 ) and ( 7 ), we obtain

Substituting Eqs. ( 7 ) and ( D.3 ) into Eq. ( D.2 ), we obtain

The cost minimization in the tourism sector yields

Substituting Eqs. ( D.3 )–( D.5 ) into Eq. ( 2 ) yields

Substituting Eq. ( D.4 ) into Eq. ( 7 ), we obtain

Substituting Eq. ( 3 ) into Eq. ( 8 ), we have

The zero-profit condition for the traded good industry (1) is rewritten as

Substituting Eq. ( D.9 ) and taking into account that the factor cost share of capital in the traded good sector is \(1-\delta\) , the full employment condition of capital (4) can be rewritten as

Considering that the factor cost share of skilled labor in the traded good sector is \(\delta\) , the demand–supply equality of skilled labor (5) becomes

The utility function of domestic residents is specified as

For the marginal utility of pollution to be negative, we assumed \({C}_{T}>1\) . The utility maximization yields

Suppose that the utility function of foreign tourists is given by

Similarly, we assumed \({D}_{T}>1\) . The tourists’ ordinary demand function for the tourism service is derived as

The market-clearing condition for the tourism service is given by

The budget constraint of the economy is

Equations ( D.6 )–( D.15 ) determine \(X\) , \(T\) , \({w}_{S}\) , \(q\) , \(r\) , \(M\) , \({p}_{T}\) , \({C}_{X}\) , \({C}_{T}\) , and \({D}_{T}\) . We set the parameter values as follows: \({A}_{X}=1\) , \({p}_{X}=1\) , \(\delta =\gamma =0.6\) , \(\xi =0.8\) , \(\rho =3\) , \(L=3\) , \(S=1\) , \(K=25\) , \({a}_{KM}=2\) , \({Y}^{*}=4\) , and \(Z=2.5\) . The elasticity of substitution in the tourism sector is \({\sigma }_{T}=1/(1+\rho )=0.25\) . The factor cost share of emission permits in the tourism sector is calculated as \({\theta }_{ZT}=\frac{1}{\frac{\gamma }{1-\gamma }{\left(\frac{Z}{L}\right)}^{\rho }+1}=0.5353\) . Since \(\xi >{\theta }_{ZT}\) , condition (1) is satisfied. By the definition, \({\theta }_{LT}=1-{\theta }_{ZT}=0.4647\) . Then, \({\sigma }_{T}<{\theta }_{LT}\) , implying that condition (2) holds. Finally, \(\frac{\partial }{\partial Z}\left(T-{C}_{T}-{D}_{T}\right)=1.343>0\) . Therefore, condition (3) holds.

Appendix E. The case where the production function of tourism sector is Cobb–Douglas

When the production function of the tourism industry is Cobb–Douglas, that is, \({\sigma }_{T}=1\) , the comparative static results in Sect.  3.1 , where the tourism terms of trade are fixed, are simplified as

From Eqs. ( 18 ) and ( 22 ), we obtain \(\frac{\widehat{T}}{\widehat{Z}}=\frac{{\widehat{w}}_{L}}{\widehat{Z}}\) . Note also that from Eqs. ( 34 ), ( 36 ), and ( E.1 ), the reciprocity relationship ( \(\partial T/\partial Z=\partial r/\partial {p}_{T}\) ) holds in this case.

6.1 The total effect

Now, we consider the total effect of a stricter environmental regulation, taking into account the indirect effect induced by the change in the tourism terms of trade. Letting \({\sigma }_{T}\) be unity in Eq. ( 43 ), the necessary and sufficient condition for a reduction in pollution to decrease the wage of skilled labor is

Recall that from Eqs. ( 26 )–( 28 ), the total effects on \(q\) , \(X\) , and \(M\) are proportional to those on \({w}_{S}\) .

Similarly, from Eq. ( 44 ), the stricter environmental regulation contracts the tourism industry if and only if

From Eq. ( 45 ), the necessary and sufficient condition for decreasing pollution to increase the wage of unskilled labor down is

From Eq. ( 46 ), the amount of pollution and the price of emission permits move in the same direction if and only if

It is straightforward to show that \(F^{\prime} < - \theta_{ZT} < H^{\prime}\) .

Therefore, when the production function of the tourism sector is Cobb–Douglas, Tables 3 , 4 and 5 are simplified as shown in Table

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Shimizu, T., Okamoto, H. Tourism infrastructure and the environment: how does environmental regulation affect welfare, tourism industry, and domestic wage inequality?. JER 75 , 147–179 (2024). https://doi.org/10.1007/s42973-021-00109-4

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Received : 11 December 2020

Revised : 27 September 2021

Accepted : 24 November 2021

Published : 07 January 2022

Issue Date : January 2024

DOI : https://doi.org/10.1007/s42973-021-00109-4

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