travel and tourism competitiveness index 2022

• In the Spotlight
• Strategy Toolbox
• Trends & Insights
• The Place2Place Podcast
• The ultimate guide to place brand rankings
• WEBINAR: Reputation ROI
• City Nation Place Americas
• City Nation Place UK
• City Nation Place Global
• City Nation Place Australasia
• The Place Brand Portfolio

WEF Travel & Tourism Development Index (TTDI) published today

by  Bloom Consulting 

The findings of the  Travel & Tourism Development Index (TTDI)  which measures 117 economies on a range of tourism and travel related indicators and policies, has been published by the World Economic Forum today. The WEF describes this addition as an “evolution” to the previous  Tourism and Travel Competitiveness Index .   

Every two years, the WEF publishes in-depth analysis as well as an index that gives countries a score and overall ranking and serves as a critical benchmarking tool for the T&T sector. Japan topped the ranking this year, with the United States coming in second; Spain third, and the United Kingdom in eighth position.    

Bloom Consulting ,  a member of an advisory  and data partner for the report, believes the outcomes will be highly anticipated by the industry because tourism and travel has changed in unprecedented ways since the publication of the TTDI’s predecessor, the Travel & Tourism Competitiveness Index (TTCI) 2019.   

“The climate crisis, the pandemic and now the outbreak of war in Europe continues to disrupt and impact the recovery of the industry. What made an economy competitive in the past, isn’t necessarily what will make it competitive and sustainable for the future. As such, policies and strategies need to evolve and this report provides much needed direction,” said Jose Torres, CEO of Bloom Consulting.  

See which countries scored in the top 10 of the WEF TTDI.   

New criteria for a new era  

Economies are measured using five subindexes and 112 individual indicators which covers a range of criteria that takes into account “business, safety and health conditions, infrastructure and natural resources as well as, environmental, socioeconomic and demand pressures”.    

This year, criteria used to measure the long-term development drivers of tourism and travel in economies has been updated to reflect the changing market conditions and include a greater focus on sustainability and resilience. SOCIOECONOMIC RESILIENCE AND CONDITIONS, NON-LEISURE RESOURCES AND T&T DEMAND PRESSURE & IMPACT pillars are all new to the 2021 edition of the TTDI.  

“The need for T&T development has never been greater as it plays a critical role in helping the global economic recovery by supporting the livelihoods of some of the populations hardest hit by the pandemic.”

Key outcomes 

Uneven t&t recovery  .

With the accessibility of vaccines in Western countries, strong government spending and an easing of travel restrictions amongst many Western countries, the T&T industry in the developed world is on the road to recovery.  

Whilst international arrivals were still 67 per cent below pre-pandemic levels according to the latest outlook from the United Nations World Tourism Organization (UNWTO), tourist arrivals increased by 4.0 percent in 2021 and numbers in January 2022 rose even further.   

While momentum is gathering pace with tourists eager to travel once again, experts say that international arrivals may not return to pre-pandemic levels until 2024 at the earliest. Countries, for instance, like Australia and New Zealand announced only recently that borders were reopening to international travellers. Further, the unprecedented war in Ukraine has led to increased uncertainty and a rise in the cost of living which may lead to less demand for the travel sector.   

On a global perspective the recovery of the T&T sector “remains slow, uneven and fragile” due to many factors including the limited access to vaccines in emerging and developing economies and an inclination by some tourists to be more sensitive to health and safety conditions, sticking with destinations with widespread vaccination rates and better healthcare services. In many parts of the world, many Covid-related travel restrictions and mask mandates are still in place.  

The World Bank predicts that emerging markets and developing economies (EMDEs) will not return to pre-pandemic tourism activity until after 2023, and 80 percent of tourism reliant EMDEs were below their 2019 economic output at the end of 2021.  

Tourism reliant EMDEs face a significant and urgent need to close this gap because their entire economy depends on it. “Investing in T&T could not only mitigate the impact of the pandemic but also improve socioeconomic progress and resilience,” the report said.   

Going forward, the T&T sector must work to improve “ the distribution and promotion of natural, cultural and non-leisure assets and activities; the availability of quality transport and tourist service   infrastructure; the degree of international openness; and favourable factors such as (increasingly   important) ICT readiness and health and hygiene,” the report stressed.   

As a result of changing market conditions and increased uncertainty, the public and private sectors are “continuously reviewing their tourism strategies and policies to bolster recovery” whilst the industry overall remains “vulnerable to socioeconomic conditions and global risks”.    

Sustainability and resilience

Climate and other environmental issues are a growing risk for many T&T economies with the report warning the sector is “increasingly tied to their ability to manage and operate under ever greater ecological and environmental threats.” Further, results captured from The World Economic Forum’s Global Risks Report 2022 survey confirmed environmental risks represent half of the top 10 global risks.  

Even before the pandemic, sustainability issues such as overcrowding, environmental degradation caused by mass tourism, and the liveability for residents in highly visited cities like Amsterdam or Barcelona were mounting.   

Whilst the initial lockdowns in 2020 forced a complete stop to the actions causing environmental pressures, new smarter ways of approaching sustainable growth are needed as the global industry works to recover.   

“Economies that have sustainable tourism strategies such as the protection of natural assets, longer stays for tourists and the promotion of less populated areas will have long-term competitive advantages because the whole world is moving, slowly but surely, into more sustainable, less carbon intensive ways of operating. Some of the trends we saw during the pandemic, like the shift to nature trips over cities, we think will stay for the long-term,” Torres said.    

Changing dynamics of tourism

The pandemic has created new opportunities and shifted consumer preferences - many  economies will notice and adapt accordingly. According to the UNWTO Panel of Experts, the major trends driving the T&T recovery include “domestic tourism, travel close to home, open-air activities, nature-based products and rural tourism”.   

Over the last two years, nature-related segments saw a 20.8 percent average growth in Digital Demand as people flocked to nature destinations, such as parks and mountains, that were safer and more accessible during the pandemic. In fact, some countries in nature-based and rural tourism markets have been able to grow beyond pre-pandemic levels.    

Influenced by the restrictions and travel policies during the pandemic domestic travel rose which saw an average spending increase from 50.8 per cent in 2019 to 62.6 per cent in 2020 among economies ranked in the TTDI. Conversely, the business travel segment declined as people moved to zoom and remote work.  

“Reading this report demonstrates that the world has changed and what consumers wanted in the past, will not be what they want in the future. It has been a hugely unsettling time but the industry can also look to the opportunities and the new sources of growth that are starting to take shape as outlined in this report. Nation Brands need to focus on these opportunities whilst realising that uncertainty right now is the norm,” Torres said.   

To read the full report, visit  www.weforum.org .   

Sign up to receive our newsletter for the latest data, insights, and inspiration from City Nation Place

The Place Brand Portfolio is City Nation Place's searchable portfolio of Awards case studies from the past five years.

Related content

Six places that are living their sustainable values

How can DMOs drive social & community wellbeing?

Learning Places: How cities are collaborating more effectively with their universities

Your rebrand is complete when it’s complete

Eight tips for balancing growth and community inclusion

Navigating Climate Action: Examples and case studies for destination managers

The power of ‘we’: Building a place brand strategy with your community

Confirm cancellation.

An error occurred trying to play the stream. Please reload the page and try again.

You can unsubscribe from these emails at any time. For more information on how we process and secure your data, view our   privacy policy

By submitting your details you consent to us processing the personal data you have provided in accordance with our   privacy policy   and   terms and conditions

• Press Releases
• Press Enquiries
• Travel Hub / Blog
• Brand Resources
• Newsletter Sign Up
• Global Summit
• Hosting a Summit
• Upcoming Events
• Previous Events
• Event Photography
• Event Enquiries
• Our Members
• Our Associates Community
• Membership Benefits
• Enquire About Membership
• Sponsors & Partners
• Insights & Publications
• WTTC Research Hub
• Economic Impact
• Knowledge Partners
• Data Enquiries
• Hotel Sustainability Basics
• Community Conscious Travel
• SafeTravels Stamp Application
• SafeTravels: Global Protocols & Stamp
• Security & Travel Facilitation
• Sustainable Growth
• Women Empowerment
• Destination Spotlight - SLO CAL
• Vision For Nature Positive Travel and Tourism
• Governments
• Consumer Travel Blog
• ONEin330Million Campaign
• Reunite Campaign

Economic Impact Research

• In 2023, the Travel & Tourism sector contributed 9.1% to the global GDP; an increase of 23.2% from 2022 and only 4.1% below the 2019 level.
• In 2023, there were 27 million new jobs, representing a 9.1% increase compared to 2022, and only 1.4% below the 2019 level.
• Domestic visitor spending rose by 18.1% in 2023, surpassing the 2019 level.
• International visitor spending registered a 33.1% jump in 2023 but remained 14.4% below the 2019 total.

Click here for links to the different economy/country and regional reports

Why conduct research?

From the outset, our Members realised that hard economic facts were needed to help governments and policymakers truly understand the potential of Travel & Tourism. Measuring the size and growth of Travel & Tourism and its contribution to society, therefore, plays a vital part in underpinning WTTC’s work.

What research does WTTC carry out?

Each year, WTTC and Oxford Economics produce reports covering the economic contribution of our sector in 185 countries, for 26 economic and geographic regions, and for more than 70 cities. We also benchmark Travel & Tourism against other economic sectors and analyse the impact of government policies affecting the sector such as jobs and visa facilitation.

Visit our Research Hub via the button below to find all our Economic Impact Reports, as well as other reports on Travel and Tourism. 

Travel, Tourism & Hospitality

Industry-specific and extensively researched technical data (partially from exclusive partnerships). A paid subscription is required for full access.

Leading global countries in the Travel & Tourism Development Index 2023

In 2023, the United States recorded the highest score in the Travel & Tourism Development Index (TTDI), with 5.24 points out of seven. That year, Spain and Japan followed behind, with a TTDI score of 5.18 and 5.09, respectively. The Travel & Tourism Development Index analyzes a range of factors and policies supporting the development of the travel and tourism sector in a sustainable and resilient way. It covers 119 countries and is made up of five sub-indexes, addressing a series of relevant topics for the sector, such as safety and security, prioritization of travel and tourism, infrastructure, environmental sustainability, and more.

The economic contribution of travel and tourism

In 2023, the total contribution of travel and tourism to the global gross domestic product (GDP) was forecast to exceed nine trillion U.S. dollars, nearly catching up with the figure recorded in 2019, the year before the onset of the COVID-19 pandemic. Meanwhile, the  number of travel and tourism jobs worldwide was expected to surpass 300 million in 2023, also remaining slightly below pre-pandemic levels.

What is the number of international tourist arrivals worldwide?

In 2023, the  number of international tourist arrivals worldwide reached almost 1.3 billion. While this figure denoted a sharp annual increase, it did not catch up yet with the peak reported in 2019, when global inbound tourist arrivals totaled approximately 1.46 billion. Overall, both before and after the impact of the health crisis, Europe was the global region with the highest number of inbound tourist arrivals .

Leading countries in the Travel & Tourism Development Index (TTDI) worldwide in 2023

To access all Premium Statistics, you need a paid Statista Account

• Immediate access to all statistics
• Incl. source references
• Download as PDF, XLS, PNG and PPT

Additional Information

Show sources information Show publisher information Use Ask Statista Research Service

The Travel & Tourism Development Index (TTDI) is an evolution of the Travel and Tourism Competitiveness Index (TTCI). It measures factors and policies that contribute to the development of the travel and tourism sector, which in turn contributes to the development of a country. The index covers 119 countries and is made up of five sub-indexes: Enabling Environment, Travel and Tourism Policy and Enabling Conditions, Infrastructure and Services, Travel and Tourism Resources, and Travel and Tourism Sustainability. The index scores range from one to seven, with one being the worst rating and seven the best rating.

Other statistics on the topic Travel and tourism in Malta

Leisure Travel

• Best-rated countries in the Gay Travel Index 2023
• Number of inbound tourists in Malta 2001-2023
• Travel and tourism's total contribution to GDP in Malta 2019-2022

Accommodation

• Number of hotel rooms in Malta 2013-2022

To download this statistic in XLS format you need a Statista Account

To download this statistic in PNG format you need a Statista Account

To download this statistic in PDF format you need a Statista Account

To download this statistic in PPT format you need a Statista Account

As a Premium user you get access to the detailed source references and background information about this statistic.

As a Premium user you get access to background information and details about the release of this statistic.

As soon as this statistic is updated, you will immediately be notified via e-mail.

… to incorporate the statistic into your presentation at any time.

You need at least a Starter Account to use this feature.

• Immediate access to statistics, forecasts & reports
• Usage and publication rights
• Download in various formats

You only have access to basic statistics. This statistic is not included in your account.

• Instant access  to 1m statistics
• Download  in XLS, PDF & PNG format
• Detailed  references

Business Solutions including all features.

Statistics on " Travel and tourism in Malta "

• Distribution of travel and tourism expenditure in Malta 2019-2022, by type
• Distribution of travel and tourism expenditure in Malta 2019-2022, by tourist type
• Travel and tourism revenue in Malta 2019-2028, by segment
• Travel and tourism's total contribution to employment in Malta 2019-2022
• Leading global countries in the Travel & Tourism Development Index 2023
• Key data on travel agencies in Malta 2024
• Number of inbound tourists in Malta 2010-2023, by travel mode
• Number of inbound tourists in Malta 2019-2023, by age group
• Leading inbound travel markets in Malta 2019-2023, by number of nights
• Inbound tourist expenditure in Malta 2010-2023
• Leading inbound travel markets in Malta 2019-2023, by tourist expenditure
• Inbound tourism spending as a share of total exports in Malta 2010-2022
• Leading inbound travel markets in Malta 2024, by Google travel demand growth
• Number of outbound tourists from Malta 2010-2023
• Number of nights spent by outbound tourists from Malta 2019-2023, by country
• Spending of outbound tourists from Malta 2010-2023
• Expenditure of outbound tourists from Malta 2019-2023, by destination
• Leading outbound travel markets in Malta 2024, by Google travel demand growth
• Number of domestic tourists in Malta 2016-2022
• Number of domestic tourists in Malta 2022, by age
• Number of domestic tourists in Malta 2015-2022, by destination
• Domestic tourism spending in Malta 2016-2022
• Cruise passenger movements in Mediterranean ports 2019-2023, by country
• Number of cruise passengers arriving in Malta 2011-2023
• Number of cruise passengers arriving in Malta 2019-2023, by age
• Cruise calls at Mediterranean ports 2019-2023, by country
• Number of cruise liner calls in Malta 2008-2023
• Number of tourist accommodation establishments in Malta 2022, by type
• Key data on the hotel industry in Malta 2022
• Number of hotels and similar accommodation in Malta 2013-2022
• Hotel bedroom occupancy rate in Malta 2013-2022

Other statistics that may interest you Travel and tourism in Malta

• Basic Statistic Travel and tourism's total contribution to GDP in Malta 2019-2022
• Basic Statistic Distribution of travel and tourism expenditure in Malta 2019-2022, by type
• Basic Statistic Distribution of travel and tourism expenditure in Malta 2019-2022, by tourist type
• Premium Statistic Travel and tourism revenue in Malta 2019-2028, by segment
• Basic Statistic Travel and tourism's total contribution to employment in Malta 2019-2022
• Premium Statistic Leading global countries in the Travel & Tourism Development Index 2023
• Basic Statistic Best-rated countries in the Gay Travel Index 2023
• Premium Statistic Key data on travel agencies in Malta 2024

Inbound tourism

• Premium Statistic Number of inbound tourists in Malta 2001-2023
• Premium Statistic Number of inbound tourists in Malta 2010-2023, by travel mode
• Premium Statistic Number of inbound tourists in Malta 2019-2023, by age group
• Premium Statistic Leading inbound travel markets in Malta 2019-2023, by number of nights
• Premium Statistic Inbound tourist expenditure in Malta 2010-2023
• Premium Statistic Leading inbound travel markets in Malta 2019-2023, by tourist expenditure
• Premium Statistic Inbound tourism spending as a share of total exports in Malta 2010-2022
• Premium Statistic Leading inbound travel markets in Malta 2024, by Google travel demand growth

Outbound tourism

• Premium Statistic Number of outbound tourists from Malta 2010-2023
• Premium Statistic Number of nights spent by outbound tourists from Malta 2019-2023, by country
• Premium Statistic Spending of outbound tourists from Malta 2010-2023
• Basic Statistic Expenditure of outbound tourists from Malta 2019-2023, by destination
• Premium Statistic Leading outbound travel markets in Malta 2024, by Google travel demand growth

Domestic tourism

• Premium Statistic Number of domestic tourists in Malta 2016-2022
• Premium Statistic Number of domestic tourists in Malta 2022, by age
• Premium Statistic Number of domestic tourists in Malta 2015-2022, by destination
• Premium Statistic Domestic tourism spending in Malta 2016-2022

Cruise tourism

• Premium Statistic Cruise passenger movements in Mediterranean ports 2019-2023, by country
• Premium Statistic Number of cruise passengers arriving in Malta 2011-2023
• Premium Statistic Number of cruise passengers arriving in Malta 2019-2023, by age
• Premium Statistic Cruise calls at Mediterranean ports 2019-2023, by country
• Premium Statistic Number of cruise liner calls in Malta 2008-2023
• Basic Statistic Number of tourist accommodation establishments in Malta 2022, by type
• Premium Statistic Key data on the hotel industry in Malta 2022
• Premium Statistic Number of hotels and similar accommodation in Malta 2013-2022
• Premium Statistic Number of hotel rooms in Malta 2013-2022
• Premium Statistic Hotel bedroom occupancy rate in Malta 2013-2022

Further related statistics

• Premium Statistic Leading European countries in the Travel & Tourism Development Index 2023
• Premium Statistic Leading countries in the MEA in the Travel & Tourism Competitiveness Index 2018
• Premium Statistic Sub-Saharan African countries in the Travel & Tourism Competitiveness Index 2019
• Premium Statistic Inbound tourism of visitors from India to the Netherlands 2012-2017
• Basic Statistic Forecast: economic contribution of travel and tourism to GDP worldwide 2020-2029
• Basic Statistic Global travel and tourism expenditure 2019-2022, by type
• Premium Statistic Travel and tourism competitiveness index score APAC 2019 by segment
• Premium Statistic Overnight guests from Canada in the Netherlands 2018, by city
• Premium Statistic Inbound tourism of visitors from Denmark to the Netherlands 2013-2019
• Premium Statistic Number of hotel bed-places in Saudi Arabia 2008-2022
• Premium Statistic Tourism establishments Saudi Arabia 2007-2020
• Premium Statistic Average length of hotel stay Saudi Arabia 2004-2022
• Basic Statistic Flyer usage before travel and tourism shopping in Canada 2014
• Basic Statistic Opinions on EU Digital COVID certificates aiding travel planning in Europe 2021

Further Content: You might find this interesting as well

• Leading European countries in the Travel & Tourism Development Index 2023
• Leading countries in the MEA in the Travel & Tourism Competitiveness Index 2018
• Sub-Saharan African countries in the Travel & Tourism Competitiveness Index 2019
• Inbound tourism of visitors from India to the Netherlands 2012-2017
• Forecast: economic contribution of travel and tourism to GDP worldwide 2020-2029
• Global travel and tourism expenditure 2019-2022, by type
• Travel and tourism competitiveness index score APAC 2019 by segment
• Overnight guests from Canada in the Netherlands 2018, by city
• Inbound tourism of visitors from Denmark to the Netherlands 2013-2019
• Number of hotel bed-places in Saudi Arabia 2008-2022
• Tourism establishments Saudi Arabia 2007-2020
• Average length of hotel stay Saudi Arabia 2004-2022
• Flyer usage before travel and tourism shopping in Canada 2014
• Opinions on EU Digital COVID certificates aiding travel planning in Europe 2021

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

U.S. Department of Commerce

• Fact Sheets

Was this page helpful?

Fact sheet: 2022 national travel and tourism strategy, office of public affairs.

The 2022 National Travel and Tourism Strategy was released on June 6, 2022, by U.S. Secretary of Commerce Gina M. Raimondo on behalf of the Tourism Policy Council (TPC). The new strategy focuses the full efforts of the federal government to promote the United States as a premier destination grounded in the breadth and diversity of our communities, and to foster a sector that drives economic growth, creates good jobs, and bolsters conservation and sustainability. Drawing on engagement and capabilities from across the federal government, the strategy aims to support broad-based economic growth in travel and tourism across the United States, its territories, and the District of Columbia.

The federal government will work to implement the strategy under the leadership of the TPC and in partnership with the private sector, aiming toward an ambitious five-year goal of increasing American jobs by attracting and welcoming 90 million international visitors, who we estimate will spend $279 billion, annually by 2027.

The new National Travel and Tourism Strategy supports growth and competitiveness for an industry that, prior to the COVID-19 pandemic, generated $1.9 trillion in economic output and supported 9.5 million American jobs. Also, in 2019, nearly 80 million international travelers visited the United States and contributed nearly $240 billion to the U.S. economy, making the United States the global leader in revenue from international travel and tourism. As the top services export for the United States that year, travel and tourism generated a $53.4 billion trade surplus and supported 1 million jobs in the United States.

The strategy follows a four-point approach:

• Promoting the United States as a Travel Destination Goal : Leverage existing programs and assets to promote the United States to international visitors and broaden marketing efforts to encourage visitation to underserved communities.
• Facilitating Travel to and Within the United States Goal : Reduce barriers to trade in travel services and make it safer and more efficient for visitors to enter and travel within the United States.
• Ensuring Diverse, Inclusive, and Accessible Tourism Experiences Goal : Extend the benefits of travel and tourism by supporting the development of diverse tourism products, focusing on under-served communities and populations. Address the financial and workplace needs of travel and tourism businesses, supporting destination communities as they grow their tourism economies. Deliver world-class experiences and customer service at federal lands and waters that showcase the nation’s assets while protecting them for future generations.
• Fostering Resilient and Sustainable Travel and Tourism Goal : Reduce travel and tourism’s contributions to climate change and build a travel and tourism sector that is resilient to natural disasters, public health threats, and the impacts of climate change. Build a sustainable sector that integrates protecting natural resources, supporting the tourism economy, and ensuring equitable development.

Travel and Tourism Fast Facts

• The travel and tourism industry supported 9.5 million American jobs through $1.9 trillion of economic activity in 2019. In fact, 1 in every 20 jobs in the United States was either directly or indirectly supported by travel and tourism. These jobs can be found in industries like lodging, food services, arts, entertainment, recreation, transportation, and education.
• Travel and tourism was the top services export for the United States in 2019, generating a $53.4 billion trade surplus.
• The travel and tourism industry was one of the U.S. business sectors hardest hit by the COVID-19 pandemic and subsequent health and travel restrictions, with travel exports decreasing nearly 65% from 2019 to 2020. 
• The decline in travel and tourism contributed heavily to unemployment; leisure and hospitality lost 8.2 million jobs between February and April 2020 alone, accounting for 37% of the decline in overall nonfarm employment during that time. 
• By 2021, the rollout of vaccines and lifting of international and domestic restrictions allowed travel and tourism to begin its recovery. International arrivals to the United States grew to 22.1 million in 2021, up from 19.2 million in 2020. Spending by international visitors also grew, reaching $81.0 billion, or 34 percent of 2019’s total.

More about the Tourism Policy Council and the 2022 National Travel and Tourism Strategy

Created by Congress and chaired by Secretary Raimondo, the Tourism Policy Council (TPC) is the interagency council charged with coordinating national policies and programs relating to travel and tourism. At the direction of Secretary Raimondo, the TPC created a new five-year strategy to focus U.S. government efforts in support of the travel and tourism sector which has been deeply and disproportionately affected by the COVID-19 pandemic.

Read the full strategy here

• Place Branding
• Placemaking
• Digital Identity
• Experts Opinion
• Keynotes & Presentations
• Publications
• Day at Work

• About Bloom Consulting
• Portuguese (Portugal)

Nation Branding vs. Nation Marketing: What’s the Difference?  

What is nation branding, branding a place and a nation: challenges of defining the central idea, three branding initiatives to close the perception-reality gap of a country or place brand, placemaking and the quality of “public places”, what is placemaking, cities and the new zeitgeist: understanding the dematerialized world, key elements to successful placemaking in the new decade, swedish case study: what search data reveals about the impact of the pandemic on tourism, who’s managing the digital identity of nations and places, d2 digital demand ©, results may surprise you., here’s why tourism destinations can’t afford to ignore search-engine results, bloom consulting continues its official partnership with the wef travel and tourism development index, what constitutes people’s perceptions of global cities, what constitutes perceptions about countries, laying the foundations of a new place brand for the great southern region in australia  , finland – playing the long game, how a place brand strategy revived one of london’s historic areas, australia: a new approach to nation branding, first ai-driven nation branding strategy, a masters degree in place branding: a transformative academic partnership, the bloom place academy, interview with philippe, tourism development global director at bloom consulting, interview with filipe, nation and place branding director at bloom consulting, interview with philippe and jose on attract and bloom consulting merger, laying the foundation, fundamentals of managing nation and place brands during times of crisis, how the digital identity of nations and places reflects on place brands, highlights of the country brand ranking 2022 | 2023 trade edition, brand south africa: converting a complicated legacy into an advantage, future risks for place branding, day at work with jolanta, cmo, nation and place branding consultant at bloom consulting.

World Economic Forum – The Travel & Tourism Competitiveness Report

Published every two years by the World Economic Forum, the Travel & Tourism Competitiveness Report and Index compares the competitiveness of 140 economies and measures the set of factors and policies that enable the sustainable development of the Travel & Tourism (T&T) sector, which in turn contributes to the development and competitiveness of a Country.

Bloom Consulting is an official data partner of the World Economic Forum.

See the Travel & Tourism Competitiveness Report and Index

POPULAR POSTS

Key Benefits of Nation and Place Branding Strategies

Stockholm: the Smart City Brand of Scandinavia

Costa Rica, how a small Country can have a great vision

Popular category.

• Highlights 100
• Research 29
• Our Work 28
• Experts Opinion 24
• Place Branding 16
• Interesting Facts 16
• Behind the Scenes 15

Tourism competitiveness measurement. A perspective from Central America and Caribbean destinations

Tourism Review

ISSN : 1660-5373

Article publication date: 20 July 2022

Issue publication date: 27 October 2022

This study aims to present diverse proposals for the measurement of tourism destination competitiveness that serve as alternatives to the travel and tourism competitiveness index (TTCI).

Design/methodology/approach

The proposal includes principal component analysis, the DP2-distance method, goal programming, data envelopment analysis and the Borda count. The study evaluates 17 destinations from Central America and the Caribbean.

These include the feasibility that the methodologies provide reliable competitiveness rankings and the possibility of using less information due to the strength of the statistical methodologies. International tourist arrivals, income from international tourism and travel and tourism contribution to the gross domestic product could be used as approximations of tourism destination competitiveness.

Research limitations/implications

The main limitation is the absence of major destinations from the region that constitutes fierce competitors.

Practical implications

New aggregation methods can build composite indicators for competitiveness measurement and their presentation in a more comprehensible way.

Social implications

The results serve as an alternative for countries that have yet to be considered in international tourism competitiveness comparisons.

Originality/value

A better explanatory power of the proposed index is given, thanks to their decomposition capacity and the reduction of the limitations of the original TTCI. Moreover, the proposals facilitate the inclusion of external information or the execution of a completely objective methodology.

本研究旨在为衡量旅游目的地竞争力提出多样化的建议, 并作为旅行和旅游竞争力指数的替代方案。

该提案包括主成分分析、DP 2 距离方法、目标规划、数据包络分析和 Borda 计数。 该研究评估了中美洲和加勒比地区的 17 个目的地。

结果包括这些方法提供可靠的竞争力排名的可行性, 以及由于统计方法的优势而使用较少信息的可能性。 国际旅游人数、国际旅游收入以及旅行和旅游对 GDP 的贡献可以用作旅游目的地竞争力的近似值。

主要局限是该地区没有竞争激烈的主要目的地。

新的聚合方法可以为竞争力测量建立综合指标, 并以更易于理解的方式呈现。

结果可作为国际旅游竞争力比较中, 衡量尚未考虑国家的替代方案。

由于其分解能力和原始 TTCI 限制的减少, 所提出的指数具有更好的解释力。 此外, 这些建议有助于纳入外部信息及执行完全客观的方法。

El presente estudio busca presentar diversas metodologías para medir la competitividad de los destinos turísticos, de modo que sirvan como alternativa al Índice de Competitividad de Viajes y Turismo.

Diseño/metodología/enfoque

La propuesta incluye Análisis de Componentes Principales, el método de distancia DP 2 , Programación por Metas, Análisis Envolvente de Datos y el Recuento de Borda. Se analizan 17 destinos de Centro América y el Caribe.

Estos incluyen la validez de las metodologías para obtener rankings de competitividad fiables y la posibilidad de emplear menor cantidad de información, dadas las fortalezas de los procedimientos estadísticos propuestos. Las Llegadas de Turistas Internacionales, los Ingresos por Turismo Internacional, y la Contribución del Turismo al PIB podrían ser buenas aproximaciones para medir competitividad turística

Limitaciones/implicaciones

La principal limitación es la ausencia de destinos importantes de la región, que se consideran importantes competidores.

Implicaciones prácticas

Novedosos procedimientos de agregación para crear indicadores sintéticos para medir la competitividad turística y su presentación de un modo más comprensible.

Implicaciones sociales

Los resultados sirven como alternativa para otros destinos que aún no han sido considerados en comparaciones internacionales de competitividad turística.

Originalidad

Un mejor poder explicativo de los índices propuestos, gracias a su capacidad de descomposición, y la reducción de las limitaciones del índice del WEF. Además, las propuestas facilitan la inclusión de información externa o la ejecución de un método completamente objetivo.

• Competitiveness
• Central America and the Caribbean
• Composite indicators
• Distance-based methods
• Multicriteria
• Data envelopment analysis
• Competitividad
• Centro américa y el caribe
• Indicadores sintéticos
• Métodos basados en distancia
• Multicriterio análisis envolvente de datos

Pérez León, V.E. , Guerrero, F.M. and Caballero, R. (2022), "Tourism competitiveness measurement. A perspective from Central America and Caribbean destinations", Tourism Review , Vol. 77 No. 6, pp. 1401-1417. https://doi.org/10.1108/TR-03-2022-0119

Emerald Publishing Limited

Copyright © 2022, Víctor Pérez León, Flor Mª Guerrero, Rafael Caballero.

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at: http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

To assess destination competitiveness, researchers have diagnosed the competitive positions of a specific destination or groups of destinations using a wide range of approaches, tools and simple and specific indicators ( Abreu-Novais et al. , 2016 ). In addition, the literature reveals the existence of several studies dedicated to this end ( Carayannis et al. , 2018 ; Croes, 2011 ; Croes and Kubickova, 2013 ; Dwyer et al. , 2000 ; Gómez-Vega and Picazo-Tadeo, 2019 ; Kayar and Kozak, 2010 ; Knežević Cvelbar et al. , 2016 ; Kunst and Ivandić, 2021 ; Ritchie and Crouch, 2010 ; Rodríguez-Díaz and Pulido-Fernández, 2021 ; Uyar et al. , 2022 ). Nevertheless, the progress presented to date reveals, among other factors, certain limitations regarding the selection of evaluation variables and the calculation of their respective weights ( Carayannis et al. , 2018 ), the methodology used to aggregate the information and the explanatory power of the results.

Amongst the diverse initiatives developed to measure destination competitiveness, there is the travel and tourism competitiveness index (TTCI) developed by the World Economic Forum (WEF) ( WEF , 2015, 2017 , 2019), which constitutes the most noteworthy contribution. This index has been launched biannually since 2007 and serves as a comprehensive strategic tool to measure the factors and policies that make the development of the tourism sector attractive in various countries, by enabling all stakeholders to work jointly to improve the competitiveness of the tourism industry in their national economies, thereby contributing towards growth and national prosperity ( WEF, 2019 ).

The TTCI is composed of 14 “pillars” comprising a set of qualitative and quantitative variables. Each of the pillars is calculated as an unweighted average of the individual component variables. The sub-indices are then calculated as unweighted averages of the pillars included, and this process has remained invariable since its first publication (WEF, 2019).

This is one of the most commonly used and feasible indices, thanks to its credibility, data accuracy ( Abreu-Novais et al. , 2016 ) and the desirable combination of hard and soft data, which is narrowly limited to a small number of initiatives. The index is a valuable comparability tool for the demonstration of destination strengths ( Pérez León, et al. , 2021a ) and support of their visibility. Consequently, following the findings of Uyar et al. (2022) , diverse studies assess destination competitiveness using the TTCI in global analysis ( Rodríguez-Díaz and Pulido-Fernández, 2021 ; Salinas et al. , 2022 ) and evaluate different destinations according to their general behaviour ( Salinas et al. , 2020 ) or compare regional destinations, including the Mediterranean ( Kunst and Ivandić, 2021 ), Middle Eastern destinations ( Leung and Baloglu, 2013 ), European Union countries ( Kayar and Kozak, 2010 ) and Caribbean destinations ( Pérez León et al. , 2021a ), among others.

This index is one of the most highly criticised initiatives in the measurement of destination competitiveness, due to its intense use. The criticism involves methodological issues ( Croes and Kubickova, 2013 ), the arbitrary weighting of the variables ( Pulido-Fernández and Rodríguez-Díaz, 2016 ; Salinas et al. , 2020 ), the number of indicators within each pillar ( Gómez-Vega and Picazo-Tadeo, 2019 ), the components of the index that most influence destination competitiveness ( Kubickova and Martin, 2020 ; Uyar et al. , 2022 ), its viability as a reliable measure of destination competitiveness ( Kunst and Ivandić, 2021 ) and the amount of information required for its creation ( Mendola and Volo, 2017 ), among other issues.

The latter consideration is the main reason why various countries have been omitted from certain editions, as is the case with several destinations in the Caribbean region. While most developed countries succeed in collecting reliable tourism data, less developed countries struggle to provide accurate and timely statistics ( Mendola and Volo, 2017 ). Alternatives are therefore needed that allow tourism competitiveness to be measured with a smaller number of indicators, whose degree of reliability and understanding is at least as high as that of the TTCI.

Along these lines, this study aims to introduce various proposals for the measurement of tourism destination competitiveness (TDC) that serves as an alternative to the TTCI, which reduces negative aspects such as the amount of information needed for its creation, includes the possibility of introducing external information and provides ease in interpreting the results, thereby revealing the strengths and weaknesses of the destinations analysed, and identifying the contribution of the subindices to the global competitiveness measure. This research includes the achievement of a competitiveness ranking using different methods, such as the DP 2 -distance, principal component analysis, goal programming and data envelopment analysis (DEA), and the study of their differences according to the weights and aggregation processes. Additionally, a meta-index is obtained by means of the Borda count method through allowing decision-makers to achieve a global ranking representative of the overall degree of competitiveness for compared destinations, starting from the results of different aggregation methods.

This is an innovative approach in the achievement of meta-indices as it enables the strengths of the composite indicators to be taken into account while striving to reduce their weaknesses. In contrast to other studies that use similar methods ( Salinas et al. , 2020 , 2022 ), the DP 2 -distance proposes the identification of those indicators that measure tourism competitiveness without having to use all the information required in the TTCI and/or in the distance-principal component (DPC) indicator. Moreover, in contrast to Gómez-Vega and Picazo-Tadeo (2019) , our proposal uses goal programming to create the dimensional indicators with all the information available, together with the consideration of both internal and external information. Furthermore, the use of DEA is proposed to obtain the global competitiveness index so that it could be possible to identify the contribution of each dimension to the global measure. Finally, the use of the Borda count method is proposed to merge the rankings obtained and to solve the problem of their differences. Additionally, the study includes the comparison of the rankings obtained with the rankings from the WEF, both for each sub-index and globally to validate the feasibility of the proposed approaches. Comparison to other indicators related to TDC is also made to evaluate their possibility of being representative of a certain degree of competitiveness.

This research involves the measurement of destination competitiveness in various destinations from Central America and the Caribbean region, using the country level. Notwithstanding, this topic has been addressed at different levels: resorts ( Claver-Cortés et al. , 2007 ), tour operator and hotel companies ( Assaf, 2012 ), cities ( Enright and Newton, 2005 ), regions ( Cracolici and Nijkamp, 2009 ) and countries ( Salinas et al. , 2020 ). The approaches presented in this study are useful for all destination sizes and depend on the scope of the indicators and the information used.

The paper is structured as follows. First, after the presentation of the research gap and of the objectives in the introduction, the proposals for the measurement of destination competitiveness are described in detail. The region case study and the data used in the verification of the suitability of the proposed methods are then presented. The results are given, both per dimension and globally, and include their relationship to other non-previously used indicators. Lastly, the conclusion section reveals the implications and proposes further research.

2.1 The DP 2 -distance indicator

The first method, called the DP 2 -distance indicator, was initially developed to measure the evolution of social welfare ( Pena, 1978 ; Zarzosa and Somarriba, 2013 ). This method is objective and eliminates the problems related to duplicity of information. It has also been used as an alternative in the measurement of TDC by Salinas et al. (2020 , 2022 ) to solve the problems arising from the aggregation of variables with different measurements and the assignation of arbitrary weights.

The DP 2 -distance for a destination is defined as: D P 2 = ∑ i = 1 n d i σ i ( 1 − R i , i − 1 , i − 2 , … , 1 2 ) with   R 1 2 = 0

For i= 1, … , n , d i is the distance between the observed unit and the reference situation for the i th indicator, and σ i is the standard deviation of the i th indicator. The d i dividing the standard deviation of each indicator eliminates the problems associated with the units of measure. R i ,   i − 1 … 1 2 is the determination coefficient, and the term 1 − R i ,   i − 1 … 1 2 is the correction factor that represents the variability percentage of the i th indicator that is not lineally explained for the previous i − 1 indicators. In this way, the problem of information duplicity is solved because this coefficient eliminates the information contained in the i th indicator contributed in the i − 1 previously added indicators.

This procedure contains certain advantages, such as its objectivity, its independence from normalisation processes and the fact that its weights are determined endogenously; therefore, any duplicity of information is eliminated.

2.2 Distance-principal component indicator (DPC)

This indicator combines principal component analysis with the concept of distance to a reference point based on multi-criteria decision-making philosophy and is defined as follows: D P C i = ∑ j = 1 q [ V E j ( ∑ k = 1 p I N i k | C o r r j k | ) ] for i = 1, 2, …, n , where n is the number of observations, p is the number of original indicators, q is the number of components selected, VE j is the variance explained by the j th component and Corr jk is the correlation between the j th component and the k th indicator. IN ik is the normalised value of the i th observation in the k th indicator, which is needed for the normalisation of the data such that the measuring units used for each indicator exert no effect on the final result. This procedure involves dividing the distance to the anti-ideal point by the difference between the maximum and the minimum values: I N i k = I i k − M i n M a x − M i n

where I ik is the value of the i th observation in the k th indicator. The minimum value of each indicator is taken as the reference point while bearing in mind that higher values indicate that the destination is assumed to be more competitive. This approach enjoys certain advantages, such as the ease in interpreting the results, as the values of the initial indicators are defined according to their distance to a fixed reference value such that the synthetic indicator is a linear combination of these distances and not of the principal components. Moreover, weights are determined endogenously.

The DP 2 and the DPC are valid for those analyses in which the destinations have not gathered all the information requested by the WEF for the creation of the TTCI. In this way, the procedure may help to identify the indicators required to measure destination competitiveness. Hence, only the data concerning the indicators resulting from the initial selection process should be collected, i.e. those indicators that contribute with a higher level of information to the competitiveness measure (DP 2 ) or those obtained from the prior application of the principal component analysis (DPC). Furthermore, their use is proposed when there is no information regarding the level of importance of the indicators.

2.3 The goal programming synthetic index (GPSI)

The goal programming synthetic index (GPSI) is encouraged in the procedure of Blancas et al. (2010) , whereby a set of m initial indicators ( I j with j = 1, 2, …, m ) is considered, for n units ( U i , with i = 1, 2, …, n ), where X ij represents the value of the i th unit valued in the j th indicator with 1 ≤  i ≤  n and 1 ≤  j ≤  m . Firstly, it is necessary to differentiate between positive ( I i j + ) and negative ( I i k − ) indicators, depending on the direction of improvement: “more is better” or “less is better”, respectively. In this way, X i j + represents the value for the i th unit in the j th positive indicator, with j ∈ J , ( J , positive indicators) and X i k − is the value of the i th unit in the k th negative indicator, with k ∈ K , ( K , negative indicators). The achievement levels or the target for each indicator can therefore be determined: u j + for the positive and u k − for the negative. Subsequently, goals are created by introducing the deviation variables to measure the difference between the indicator value and the target:

For positive indicators: I i j + + n i j + − p i j + = u j + with n i j + ,   p i j + ≥ 0 ,   n i j + ⋅ p i j + = 0

For negative indicators: I i k − + n i k − − p i k − = u k − with n i k − ,   p i k − ≥ 0 ,     n i k − ⋅ p i k − = 0

where n i j + is the undesirable variable for positive indicators, and p i k − is the undesirable variable for the negative indicators. Values higher than these variables reveal an absence of competitiveness. This procedure enables several indices to be obtained and the net GPSI, GPSI N , is selected for its compensatory character between the strengths and weaknesses for each unit under evaluation. The GPSI N for a unit is defined as: G P S I i N = ∑ j ∈ J w j + ( p i j + − n i j + ) u j + + ∑ k ∈ K w k − ( n i k − − p i k − ) u k − ,   ∀ i   ∈ { 1 ,   2 ,   … , n } where w j + and w k − are the weights for positive and negative indicators, respectively. The first sum shows the difference between the strengths and weaknesses for positive indicators, and similarly, the second sum shows this difference for the negative indicators.

The contribution of this proposal in measuring TDC involves the possibility of establishing a lower bound for the indicators in such a way that a destination could be considered competitive with respect to this target value in comparison with its competitors. Moreover, there is the facility of interpreting the results through the identification of the strengths and weaknesses of the destinations under comparison in a more comprehensible way than when the TTCI is used.

This procedure can be used both for those destinations that hold all the information available and for those that lack some data. This enables the inclusion of weights obtained externally. Once the dimensional indicators are obtained through the proposed methods, the second stage then involves the use of DEA to generate a global index, as described below.

2.4 Data envelopment analysis (DEA)

DEA is a non-parametric technique used for the construction of composite indicators ( Gómez-Vega and Picazo-Tadeo, 2019 ). DEA models possess the advantage of displaying unit invariance, which renders the normalisation stage redundant. For this stage, the initial information was previously obtained from the dimensional indicators for each destination. A single dummy input with value unity for each destination can be used. This model is formally equivalent to the original input-oriented, constant-returns-to-scale DEA model presented ( Charnes et al. , 1978 ). The global synthetic index for the i 0 observation is obtained by solving the following the linear programming problem: D E A i 0 = M a x w ∑ j = 1 d w j i 0 D I i 0 j subject to: ∑ j = 1 d w j i 0 D I i j ≤ 1     ∀ i = 1 , … , n   ( normalisation constraint ) w j i 0 D I i j ≥ ω   ∀ i = 1 , … , n ,   ∀ j = 1 , … , d   ( virtual output constraint )   w j i 0 ≥ 0   ∀ j = 1 , … , d   ( non − negativity constraint ) where w j i 0 are the weights for the observation i 0 , DI represents the j th dimension indicator for the i th observation, which would be the DPC if the global index refers to DEA after distance-principal component (DEAPC) or the GPSI is used if the global measurement represents DEA after goal programming (DEAGP); d is the number of dimensions considered (the sub-indices held in the TTCI) and ω is a real number that represents the minimum value allowed for the j th virtual output for the i th observation. The virtual output constraint involves the implication of all the dimensions in the global composite index.

The objective function chooses the weights that maximise the value of the composite index for observation i 0 . In the best situation, the index takes a value of 1, which implies that the destination has a performance equal to its reference unit. The 0 value represents the worst situation. The [0,1] range is a characteristic of the input-oriented model, which numerically renders results more comprehensible and guarantees results with a higher explanatory power: this is a desirable characteristic of composite indicators.

The virtual output constraint has been introduced to guarantee the presence of all dimensions in the composite index with a minimum value of ω . Its use in the second phase of aggregation enables the identification of the contribution of each dimension towards the global index.

2.5 The Borda count method

The Borda count method uses mapping from a set of individual rankings to create a combined ranking that leads to the most relevant decision ( Lumini and Nanni, 2006 ). In Borda count, a voter ranks all candidates in a strict order by assigning different points according to the ranking ( Vainikainen et al. , 2008 ). This method assigns zero points to a voter’s least preferred option, 1 point for the next option and ( n – 1) points for the most preferred (where n is the number of alternatives). However, this way of assigning zero points to the least preferred candidate is unfavourable for the implementation of the analytical calculation ( Lawrence et al. , 2012 ). The Borda ranking is therefore determined by placing the Borda scores in order. This approach is useful in those cases where the decision-makers have attained different rankings due to the use of diverse aggregation methods.

3. Geographical context and dataset

The study comprises 17 destinations from Central America and the Caribbean, which is the highest number of countries to have been included in an edition of the TTCI. These are underdeveloped countries in close geographical proximity, in the most tourism-dependent region worldwide, according to the World Travel & Tourism Council (WTTC) (2020 , 2021 ). These destinations compete within the same tourist market, and they offer a similar tourist experience: predominantly sun-and-sand tourism, with emphasis on cruise tourism, which has become big business, with the Caribbean accounting for more than 35% of all such vacations globally ( Caribbean Council, 2019 ). Moreover, while the number of countries included in the TTCI had been steadily rising, the number of Caribbean countries included has decreased in the latest editions, thereby passing from 17 to 13 destinations in the space of only four years. Their absence was caused due to difficulties in providing all the information required. Consequently, some of the proposed approaches serve as alternatives to these destinations, due to the possibility of using less information.

The data used correspond to the 2015 edition of the TTCI, which is the year that included the most destinations from the region. It comprises 90 indicators distributed into 14 pillars grouped into four sub-indices ( WEF, 2015 ). Those indicators with more than three missing values are excluded. For those indicators with three or fewer missing values, their scores are substituted with the minimum. This substitution guarantees the presence of those indicators in the composite measure and, therefore, its representativeness. Moreover, the scores are not influenced, thanks to the proposed method. Consequently, the data set comprises 86 indicators: 40, 22, 14 and 10 for sub-indices A, B, C and D, respectively, of which 30 are subjective. In addition, all the pillars are presented in the study as follows: Pillar A.01 (12 indicators), A.02 (5), A.03 (6), A.04 (9), A.05 (8), B.06 (6), B.07 (3), B.08 (4), B.09 (10), C.10 (5), C.11 (4), C.12 (4), D.13 (5) and D.14 (5).

4. Results and discussion

The aggregation process is developed in the same way as that proposed by the WEF to create the TTCI ( Figure 1 ). Firstly, the indicators are grouped into their pillars, and the pillars are then used to create the dimensional indicators. Lastly, a global index is built by grouping the sub-indices. The dimensional indicators are created through the DP 2 , the DPC and the GPSI approaches. The global competitiveness index is subsequently built using the DP 2 and DEA methods. The latter is used for the two global indices proposed:

the DEAPC; and

the DEAGP methods.

As a result, three alternative methods are presented for the creation of the dimensional and global indicators.

Despite the unfeasibility of the DPC and DP 2 indicators embracing more indicators than destinations, the proposed steps allow the inclusion of all the information. Moreover, to attain a process as close as possible to the WEF proposal, the aspiration level used for the GPSI is zero. In this regard, under this approach, all the destinations only evaluate their strengths. The denominators are omitted from the GPSI, and therefore, the weaknesses are not included.

The weights are achieved in a different way for each methodology. The DP 2 and the DPC methods calculate their weights endogenously. On average, the DP 2 assigns the highest weight to Sub-index B “T&T Policy and Enabling Conditions”, and the DPC gives more weight to Sub-index C “Natural and Cultural Resources”, while least importance is assigned to Sub-index A “Enabling Environment”. These assignations are consistent with the conditions of the region, comprised of underdeveloped countries with lower scores on safety, health, information and communication technology (ICT) readiness ( WEF , 2015, 2017 ) and the demonstrated efforts made by the governments towards the development of the tourism sector in the region ( Pérez León et al. , 2021b ), due to their dependence on this activity. For the GPSI, however, weights should be assigned. For the latter procedure (GPSI), the same importance is given to all the indicators contained in each pillar. For sub-dimensional indicators, the same importance is given to each pillar within the sub-indices. Lastly, all sub-indices receive the same importance in order to calculate the global indicator.

4.1 Dimensional results

The results for the pillars appear in Table A1 , while the dimensional results are shown on Table 1 . The dimensional results reveal great stability amongst the rankings, including the comparison with the results attained with those from the WEF. The five most and least competitive destinations coincide in all the rankings created. This is a great achievement because, despite the differences between the procedures, the results seem to present major similarity. The results from the GPSI approach are those more closely related to the other methods and, compared to those of the TTCI, the DP 2 and the GPSI are those closest to the WEF outputs. This is a great advantage for the DP 2 methodology because it reveals its capacity for englobing the relevant information under the statistical methods comprised in the procedure.

The similarities can be corroborated statistically. The Pearson correlation between scores demonstrates their similarity with all values higher than 0.871 and significant at the 0.01 level in each comparison, as are Spearman’s rho correlation coefficients, all higher than 0.850 for all the procedures. In general, the high correlation between each pair of scores and rankings in all the sub-indices demonstrates the feasibility of the proposals for reliable competitiveness measurements. An analysis can be made within each dimension, considering the sub-indices comprised, the indicators and their weights.

In the first sub-index, “Enabling Environment”, the five most competitive destinations (first quartile) coincide for all the rankings: Barbados, Costa Rica, Panama, Puerto Rico and Trinidad and Tobago. Only Trinidad and Tobago leaves this group in the DPC ranking, where it worsens and occupies the sixth place, while Suriname shifts to the fifth position, due to the weighting method. Barbados occupies the first position in all the rankings attained. It presents the best scores in pillars A.02 “Safety and Security” and A.05 “ICT Readiness” and remains within the most competitive destinations in the other pillars. Its worst position is that of fifth in Pillar A.01 “Business Environment”.

4.2 Global results

The global index is generated starting from the dimensional indicators. The DP 2 method is applied to the previous indices created with the same methodology. To determine the global DP 2 -distance indicator, the first step involves obtaining the dimensional indicators and taking the maximum score for each indicator as the reference value. For the construction of a global index, a representative group of initial indicators is selected for each dimension. Initial indicators that show a correlation level greater than 0.5 with the dimensional measures are selected. Weights are represented by the variability percentage of the i th indicator, which is not lineally explained by the previous i -1 indicators. This constitutes the amount of new information added for each indicator included in the process.

To create the global competitiveness index with the DPC and the GPSI approaches, DEA is used to identify the contribution of each dimension to the global measure. As a result, the DEAPC and the indices are proposed. In the DEAPC and DEAPG procedures, the minimum admissible value for the virtual outputs that guarantees the feasibility of the linear problem is 0.015; therefore, this constitutes the lower bound established for this constraint w j i 0 D I i j ≥ ω ;   ω ≥ 0.015 . The scores and rankings for all the global indices appear in Table 2 .

The results enable a ranking for these methods to be established. By comparison with the TTCI ranking, the DEAGP is found to be the most similar to the WEF, with an average variation of 0.71 positions (less than one unit) and a variance of 0.471 in contrast to 0.809 for the DP 2 ranking and 4.375 for the DEAPC. A paired comparison of the rankings reveals that the most similar are the DP 2 and the DEAGP, and there is a minor average variation between them of 0.824. Although the DEAPC and DEAGP indices are calculated with the same method in the second stage, these are the indices that differ the most, and even present the greatest contribution to the global index, largely in Sub-indices A and B. The Pearson correlation and Spearman’s rho correlation coefficients support the proximity between the rankings obtained. Both present values higher than 0.831 in all cases, significant at the 0.01 level.

The use of different aggregation processes and weighting methods may cause diverse rankings to be obtained. In the case that the decision-makers would like to use those different procedures, it would not be possible to establish an overall competitiveness ranking, despite the similarity of the indices. To this end, the Borda count approach is applied to the results of the DP 2 , of the DEAPC and to those of the DEAGP. This is considered a suitable approach because it involves all the sub-indices and the outputs of the proposed methods. The final ranking is given in the “Borda Count” column of Table 2 . This method is presented as an alternative for the decision-makers because it enables the results to be built as a single ranking. This is recommended when different aggregation methods are used, and unification of the results is desired for greater understanding and verification of their stability. However, it can be dispensed with in those cases where only one of the proposed methods is used.

4.3 Global programming synthetic index for global aggregation

The GPSI methodology is also used to calculate a global index. To determine the feasibility of the GPSI in replicating the WEF rank, a value of zero is conveniently assigned to all the aspiration levels, and the denominator is assigned a value of one to the GPSI function. This transformation is carried out due to the use of the normalised values provided by the WEF data set. The results are presented in last column of Table 2 .

The main advantage involves the possibility of observing the amount by which a destination surpasses the established goals and the representative quantity of the improvement necessity for each indicator, pillar and sub-index. Additionally, it is possible to increase goal requirements for a more rigorous comparison by means of changing the target values.

4.4 Link to other indicators

The correlation between the scores obtained with the proposed approaches, the TTCI scores and other additional indicators is analysed (international tourist arrivals, income from international tourism, international tourist expenditure and travel and tourism contribution to the gross domestic product (GDP)) ( Table 3 ). Except for international tourism expenditure, the remaining three variables could be used as approximations of tourism destination competitiveness rankings, depending on their relationship with the scores obtained with the TTCI and the proposed methods.

5. Conclusions

This study contributes towards demonstrating the feasibility of various aggregation methods in building composite indicators for the measurement of TDC and the ability of such indicators to propose rankings. These methods are proposed through the combination of a variety of algorithms, each with its own advantages and disadvantages. The procedures explained present differences, such as the variability of the results due to the order of entry of the initial indicators in the measure, the possibility of introducing subjective judgements, which enables not only the necessities of the stakeholders to be taken into consideration, but also the method used to calculate the weights. All these are practical implications that support the decision-making process.

The proposed methods complement each other and, together, contribute towards the decision-making process in measuring tourism competitiveness. They help reduce the weaknesses associated to the previous existent methods, mainly the TTCI, and therefore provide alternatives for the solution of key aspects, such as reducing the amount of information necessary, the weighting and the explanatory power of the results. The proposed methods can be applied separately, thereby taking advantage of each method to distribute information on the process of decision-making. Furthermore, they can be applied in a combined way, as explained in the study, thereby reaching the maximum of all of the positive aspects indicated.

The DP 2 -distance and DPC do not allow all the indicators to be used, although the information selection process does permit the inclusion of a greater amount of information in a smaller set of indicators. This is a great finding for other destinations because it allows their inclusion in a competitiveness ranking with less information.

The GPSI permits the inclusion of all the indicators in the composite measure. This is the most flexible approach because it facilitates the inclusion of external information through the goals and the weights. It has greater explanatory power than the previous indices due to the possibility of directly revealing the strengths and weaknesses of each destination involved by means of the deviation variables. This method also allows various results to be obtained, and therefore, their combination enriches the analysis of the outputs. Furthermore, this methodology contributes towards solving several problems, such as that of the equitable weight distribution across the pillars, the facility to analyse the results, the influence of the size of destinations and the selection of the target values.

The use of DEA in the second step brings flexibility to the procedure and enables the contribution of each dimension to the overall competitiveness value to be identified. The introduction of the virtual output constraint guarantees the inclusion of all the sub-indices in the global measure. Additionally, it is possible that this method identifies those dimensions that represent a strength or a weakness for each destination.

The meta-index created offers the possibility for decision-makers to seek alternatives to obtain diverse competitiveness rankings and merge them into a single ordered list. This aggregation is presented as an alternative to corroborate the stability of the results when different methods are used on the same data set. Moreover, the stability of the results demonstrates the suitability of the proposed methods. The comparative analysis is supported, as it is possible to identify the dimensions, pillars and indicators that contributed the most regarding the competitiveness position of all the destinations analysed.

Furthermore, compared to the TTCI and to the results achieved, the correlation values indicate that the additional variables could be viewed as providing a good explanation of the rankings. Among these, the most representative is the income from international tourism, which is also significantly correlated to the meta-index results. Consequently, it is possible to affirm that these variables may be used to create indices with results almost identical to the outputs of the TTCI.

Future research should contemplate other possible analyses, such as the consideration of a common set of weights for DEA, the restrictive GPSI indicator and the use of participative methods to obtain the weights for the GPSI and/or DPC index. There is also the possibility of including a dynamic measure of the competitiveness with the consideration of information covering different time periods.

Aggregation procedure

Dimensional results

Global rankings

Spearman’s rho correlations (ranking)

Abreu-Novais , M. , Ruhanen , L. and Arcodia , C. ( 2016 ), “ Destination competitiveness: what we know, what we know but shouldn’t and what we don’t know but should ”, Current Issues in Tourism , Vol. 19 No. 6 , pp. 1 - 21 , doi: 10.1080/13683500.2015.1091443 .

Assaf , A. ( 2012 ), “ Benchmarking the Asia pacific tourism industry: a bayesian combination of DEA and stochastic frontier ”, Tourism Management , Vol. 33 No. 5 , pp. 1122 - 1127 , doi: 10.1016/j.tourman.2011.11.021 .

Blancas , F.J. , Caballero , R. , González , M. , Lozano-Oyola , M. and Pérez , F. ( 2010 ), “ Goal programming synthetic indicators: an application for sustainable tourism in Andalusian coastal counties ”, Ecological Economics , Vol. 69 No. 11 , pp. 2158 - 2172 , doi: 10.1016/j.ecolecon.2010.06.016 .

Carayannis , E.G. , Ferreira , F.A.F. , Bento , P. , Ferreira , J.J.M. , Jalali , M.S. and Fernandes , B.M.Q. ( 2018 ), “ Developing a socio-technical evaluation index for tourist destination competitiveness using cognitive mapping and MCDA ”, Technological Forecasting and Social Change , Vol. 131 , pp. 147 - 158 .

Caribbean Council ( 2019 ), “ Reassessing the role of cruise tourism ”, September , available at: www.caribbean-council.org/reassessing-the-role-of-cruise-tourism/ (accessed 18 April 2022 ).

Charnes , A. , Cooper , W.W. and Rhodes , E. ( 1978 ), “ Measuring the efficiency of decision making units ”, European Journal of Operational Research , Vol. 2 No. 6 , pp. 429 - 444 , doi: 10.1016/0377-2217(78)90138-8 .

Claver-Cortés , E. , Molina-Azorín , J.F. and Pereira-Moliner , J. ( 2007 ), “ Competitiveness in mass tourism ”, Annals of Tourism Research , Vol. 34 No. 3 , pp. 727 - 745 .

Cracolici , M.F. and Nijkamp , P. ( 2009 ), “ The attractiveness and competitiveness of tourist destinations: a study of Southern Italian regions ”, Tourism Management , Vol. 30 No. 3 , pp. 336 - 344 , doi: 10.1016/j.tourman.2008.07.006 .

Croes , R. ( 2011 ), “ Measuring and explaining competitiveness in the context of small Island destinations ”, Journal of Travel Research , Vol. 50 No. 4 , pp. 431 - 442 , doi: 10.1177/0047287510368139 .

Croes , R. and Kubickova , M. ( 2013 ), “ From potential to ability to compete: towards a performance-based tourism competitiveness index ”, Journal of Destination Marketing & Management , Vol. 2 No. 3 , pp. 146 - 154 ., doi: 10.1016/j.jdmm.2013.07.002 .

Dwyer , L. , Forsyth , P. and Rao , P. ( 2000 ), “ The price competitiveness of travel and tourism: a comparison of 19 destinations ”, Tourism Management , Vol. 21 No. 1 , pp. 9 - 22 , doi: 10.1016/S0261-5177(99)00081-3 .

Enright , M.J. and Newton , J. ( 2005 ), “ Determinants of tourism destination competitiveness in Asia pacific: comprehensiveness and universality ”, Journal of Travel Research , Vol. 43 No. 4 , pp. 339 - 350 , doi: 10.1177/0047287505274647 .

Gómez-Vega , M. and Picazo-Tadeo , A. ( 2019 ), “ Ranking world tourist destinations with a composite indicator of competitiveness: to weigh or not to weigh? ”, Tourism Management , Vol. 72 , pp. 281 - 291 , doi: 10.1016/j.tourman.2018.11.006 .

Kayar , Ç.H. and Kozak , N. ( 2010 ), “ Measuring destination competitiveness: an application of the travel and tourism competitiveness index (2007) ”, Journal of Hospitality Marketing & Management , Vol. 19 No. 3 , pp. 203 - 216 , doi: 10.1080/19368621003591319 .

Knežević Cvelbar , L. , Dwyer , L. , Koman , M. and Mihalič , T. ( 2016 ), “ Drivers of destination competitiveness in tourism: a global investigation ”, Journal of Travel Research , Vol. 55 No. 8 , pp. 1041 - 1050 , doi: 10.1177/0047287515617299 .

Kubickova , M. and Martin , D. ( 2020 ), “ Exploring the relationship between government and destination competitiveness: the TALC model perspective ”, Tourism Management , Vol. 78 , p. 104040 , doi: 10.1016/j.tourman.2019.104040 .

Kunst , I. and Ivandić , N. ( 2021 ), “ The viability of the travel and tourism competitiveness index as a reliable measure of destination competitiveness: the case of the Mediterranean region ”, European Journal of Tourism Research , Vol. 27 , p. 2704 , doi: 10.54055/ejtr.v27i.2124 .

Lawrence , W. , Wu , W.W. and Lee , Y.T. ( 2012 ), “ Promoting food tourism with Kansei Cuisine design ”, Procedia - Social and Behavioral Sciences , Vol. 40 , pp. 609 - 615 , doi: 10.1016/j.sbspro.2012.03.238 .

Leung , X.Y. and Baloglu , S. ( 2013 ), “ Tourism competitiveness of Asia pacific destinations ”, Tourism Analysis , Vol. 18 No. 4 , pp. 371 - 384 , doi: 10.3727/108354213X13736372325876 .

Lumini , A. and Nanni , L. ( 2006 ), “ Detector of image orientation based on Borda count ”, Pattern Recognition Letters , Vol. 27 No. 3 , pp. 180 - 186 , doi: 10.1016/j.patrec.2005.08.023 .

Mendola , D. and Volo , S. ( 2017 ), “ Building composite indicators in tourism studies: measurements and applications in tourism destination competitiveness ”, Tourism Management , Vol. 59 , pp. 541 - 553 , doi: 10.1016/j.tourman.2016.08.011 .

Pena , J.A. ( 1978 ), “ La distancia P: un método Para la medición del bienestar ”, Revista Española de Economía , Vol. 8 , pp. 49 - 89 .

Pérez León , V.E. , León Sánchez , M.A. and Guerrero , F.M. ( 2021b ), “ Central American and Caribbean tourism destinations’ competitiveness: a temporal approach ”, Plos One , Vol. 16 No. 5 , p. e0252139 , doi: 10.1371/journal.pone.0252139 .

Pérez León , V.E. , Pérez , F. , Contreras Rubio , I. and Guerrero , F.M. ( 2021a ), “ An approach to the travel and tourism competitiveness index in the Caribbean region ”, International Journal of Tourism Research , Vol. 23 No. 3 , pp. 346 - 362 , doi: 10.1002/jtr.2411 .

Pulido-Fernández , J.I. and Rodríguez-Díaz , B. ( 2016 ), “ Reinterpreting the world economic forum’s global tourism competitiveness index ”, Tourism Management Perspectives , Vol. 20 , pp. 131 - 140 , doi: 10.1016/j.tmp.2016.08.001 .

Ritchie , J.B. and Crouch , G. ( 2010 ), “ A model of destination competitiveness/sustainability: Brazilian perspectives ”, Revista de Administração Pública , Vol. 44 No. 5 , pp. 1049 - 1066 , doi: 10.1590/S0034-76122010000500003 .

Rodríguez-Díaz , B. and Pulido-Fernández , J.I. ( 2021 ), “ Analysis of the worth of the weights in a new travel and tourism competitiveness index ”, Journal of Travel Research , Vol. 60 No. 2 , pp. 267 - 280 , doi: 10.1177/0047287519899982 .

Salinas , J.A. , Guaita Martínez , J.M. and Martín Martín , J.M. ( 2022 ), “ An analysis of the competitiveness of the tourism industry in a context of economic recovery following the COVID19 pandemic ”, Technological Forecasting and Social Change , Vol. 174 , p. 121301 , doi: 10.1016/j.techfore.2021.121301 .

Salinas , J.A. , Serdeira Azevedo , P. , Martín Martín , J.M. and Rodríguez Martín , J.A. ( 2020 ), “ Determinants of tourism destination competitiveness in the countries most visited by international tourists: proposal of a synthetic index ”, Tourism Management Perspectives , Vol. 33 , p. 100582 .

Uyar , A. , Kuzey , C. , Koseoglu , M.A. and Karaman , A.S. ( 2022 ), “ Travel and tourism competitiveness index and the tourism sector development ”, Tourism Economics , doi: 10.1177/13548166221080357 .

Vainikainen , N. , Kangas , A. and Kangas , J. ( 2008 ), “ Empirical study on voting power in participatory Forest planning ”, Journal of Environmental Management , Vol. 88 No. 1 , pp. 173 - 180 , doi: 10.1016/j.jenvman.2007.02.004 .

WEF ( 2015 ), “ The travel & tourism competitiveness report 2015 growth through shocks ”, available at: www.weforum.org/reports/travel-and-tourism-competitiveness-report-2015 (accessed 3 April 2021 ).

WEF ( 2017 ), “ The travel & tourism competitiveness report 2017. Paving the way for a more sustainable and inclusive future ”, Geneva , available at: www.weforum.org/reports/the-travel-tourism-competitiveness-report-2017 (accessed 3 April 2021 ).

WEF ( 2019 ), “ The travel and tourism competitiveness report 2019. Travel and tourism at a tipping point ”, edited by Uppink Calderwood , L. and Soshkin , M. , 2019th ed ., World Economic Forum , Geneva , available at: http://reports.weforum.org/travel-and-tourism-competitiveness-report-2019/?doing_wp_cron=1567434061.3136858940124511718750 (accessed 10 March 2021 ).

World Travel & Tourism Council (WTTC) ( 2020 ), “ Travel & tourism economic impact and trends 2020 ”, available at: https://wttc.org/Research/Economic-Impact (accessed 1 April 2021 ).

WTTC ( 2021 ), “ Global economic impact and trends 2021 ”, available at: https://wttc.org/Research/Economic-Impact (accessed 9 March 2022 ).

Zarzosa , P. and Somarriba , N. ( 2013 ), “ An assessment of social welfare in Spain: territorial analysis using a synthetic welfare indicator ”, Social Indicators Research , Vol. 111 No. 1 , pp. 1 - 23 , doi: 10.1007/s11205-012-0005-0 .

Further reading

Tan , Y. and Despotis , D. ( 2021 ), “ Investigation of efficiency in the UK hotel industry: a network data envelopment analysis approach ”, International Journal of Contemporary Hospitality Management , Vol. 33 No. 3 , pp. 1080 - 1104 .

Acknowledgements

This work was supported by the Spanish Ministry of Economics and Competitiveness, Grant Number: PID2019-104263RB-C41/C42. Also, by the Andalusian Agency of Innovation and Development, Grant Number: P18-RT-1566, UMA18-FEDERJA-065.

Corresponding author

About the authors.

Víctor Ernesto Pérez León is based at the Departamento de Economía Aplicada II, Universidad de Sevilla Facultad de Ciencias Economicas y Empresariales, Sevilla, España. He is a Lecturer in Statistics at the Department of Applied Economics II, University of Seville. PhD in Economic Sciences (University of Pinar del Río) and PhD in Business Administration (Pablo de Olavide University). Experience in Operations Research acting on composite indicators applied to tourism sustainability and competitiveness. Contribution to the article: data collection, data analysis and interpretation, drafting the article.

Flor Mª Guerrero is based at the Department of Economics, Quantitative Methods and Economic History, Pablo de Olavide University, Sevilla, Spain. She Casas is a Professor of Mathematics Methods for Economy and Business at Pablo de Olavide University. Her research activity focuses on mathematical applications in Business Administration. In particular, sustainable development, tourist activity, social welfare, through simple and synthetic indicators, multidimensional indicators and multi-objective optimisation. Contribution to the article: conception or design of the work, critical revision of the article, final approval of the version to be published.

Rafael Caballero is based at the Department of Applied Economics (Mathematics), Universidad de Málaga, Málaga, Spain. He is a Professor at Department of Applied Economics (Mathematics), University of Málaga, Spain. He is interested in the field of multiple objective programming. Presently, his research is in metaheuristics methods and application to problems in economy and business and forestry sciences. Contribution to the article: conception or design of the work, critical revision of the article.

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

WEF’s Travel and Tourism Competitiveness Index 2021: India Ranks 54

The World Economic Forum’s (WEF) ranked India 54th position (down from 46th in 2019) with a score of 4.1 in its Travel and Tourism Development Index 2021.

The World Economic Forum’s (WEF) ranked India 54th position (down from 46th in 2019) with a score of 4.1 in its Travel and Tourism Development Index 2021, but still, India remains the top performer in South Asia. Japan has topped (1) the global chart and the bottom position (117) is occupied by the country Chad.

Buy Prime Test Series for all Banking, SSC, Insurance & other exams

About the WEF’s Travel and Tourism Development Index 2021 

The WEF’s Travel and Tourism Development Index 2021 is a direct evolution of the Travel & Tourism Competitiveness Index, which has been published biennially for the past 15 years. The Index assesses 117 economies, based on 5 subindexes, 17 pillars and 112 individual indicators, distributed among the different pillars.

The five Sub indexes:

• Enabling Environment
• Travel and Tourism Policy and Enabling Conditions
• Infrastructure
• Travel and Tourism Demand Drivers
• Travel and Tourism Sustainability

Countries in the Global Ranking:

Important takeaways for all competitive exams:

• World Economic Forum (WEF) Founder and Executive Chairman: Klaus Schwab;
• World Economic Forum (WEF) Headquarters: Cologny, canton of Geneva, Switzerland;
• World Economic Forum (WEF) was Established: 1971.

Find More Ranks and Reports Here

Leave a comment

Your email address will not be published. Required fields are marked *

Most Trending

• IPL Winners List 2008 to 2024
• Purple Cap Winners Till 2024
• Orange Cap Winners Till 2024

Monthly Current Affairs

Recent posts, important exams.

• International
• Appointments
• Books and Authors
• Importants Days
• Ranks and Reports
• Science and Tech
• Miscellaneous

Our Other Websites

• Teachers Adda
• Current Affairs
• Defence Adda
• Engineers Adda
• Adda School

Welcome to the Current Affairs Section of Adda247. If you are preparing for Government Job Exams, then it is very important for you to read the Daily Current Affairs. All the important updates based on current affairs are included in this Daily Current Affairs 2024 article.

Download Adda247 App

Follow us on

• Responsible Disclosure Program
• Cancellation & Refunds
• Terms & Conditions
• Privacy Policy

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Search ITA Search

Explore the top markets for travel and travel and tourism, ranked in order of most likely for export success potential. Created by ITA experts in travel and tourism, the tool combines trade data, market research, and exporting tips all in one place!

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 07 June 2024

Coupling and interaction mechanism between green urbanization and tourism competitiveness based an empirical study in the Yellow River Basin of China

• Wei Shen 1 , 3 , 4 ,
• Yanli Chen 2 ,
• Weiwei Cao 1 ,
• Ruyi Yu 1 &
• Jinlong Cheng 1  

Scientific Reports volume  14 , Article number:  13167 ( 2024 ) Cite this article

Metrics details

• Environmental sciences
• Environmental social sciences

Exploring the spatial coupling relationship and interaction mechanism between green urbanization (GU) and tourism competitiveness (TC) is of great significance for promoting urban sustainable development. However, the lack of research on the interaction mechanism between GU and TC limits the formulation of effective environmental management policy and urban planning. Taking 734 counties in the Yellow River Basin (YRB) as the study area, this paper analyzes the spatial coupling relationship between GU and TC on the basis of comprehensive evaluation of GU and TC. Then, the interactive mechanism between GU and TC is systematically discussed, and the synergistic development strategy of the two is proposed. The results show that the GU level presents a multicore circle structure, with provincial capitals, prefecture-level urban districts and economically developed counties in east-central regions as high-value centers. The TC at county scale presents a multi-center spatial structure. Additionally, there is a significant positive spatial coupling between GU and TC in the YRB. The analysis further reveals that green urbanization level, social progress, population development, infrastructure construction, economic development quality, and eco-environmental protection has a observably influence on TC. Tourism competitiveness, service competitiveness, location competitiveness, resource competitiveness, market competitiveness, environmental influence, and talent competitiveness has a observably influence on GU. TC can promote GU, and the improvement of green urbanization level can support the development of tourism competitiveness. According to the spatial zoning method, 734 counties are divided into 6 categories, and the coordinated development strategy of GU and TC for each type of district is proposed.

Similar content being viewed by others

Spatiotemporal interaction characteristics and transition mechanism of tourism environmental efficiency in China

Spatial–temporal evolution and peer effects of urban green development efficiency in China

Spatial and temporal evolution and influencing factors of tourism eco-efficiency in Fujian province under the target of carbon peak

Introduction.

Since the reform and opening up, China's urbanization has developed rapidly. However, the extensive urbanization model characterized by high expansion, high consumption and high emissions has brought about a series of problems, such as environmental pollution, ecological destruction, disorderly expansion of urban space, unbalanced urban–rural and regional development, and unreasonable industrial structure 1 , 2 . Green urbanization emphasizes the transformation from rural to urban areas in terms of industrial support, living environment, social security and lifestyle, as well as the improvement of the quality of economic development, ecological environmental protection and social progress 3 , 4 . This makes city managers gradually realize that the implementation and promotion of green urbanization can help solve the negative problems caused by traditional urbanization. At the same time, with the advent of the era of mass leisure, tourism has become the pillar industry of county social and economic development and the key to urban green and low-carbon development and industrial transformation. Tourism competitiveness is the reflection of the overall development strength of regional tourism, and is also the core driving force for the development of tourism industry 5 , 6 . Therefore, green urbanization and tourism competitiveness are consistent in many aspects such as development concepts and goals. On the one hand, the improvement of tourism competitiveness not only provides a major opportunity for the sustainable development of regional tourism, but also releases a strong driving force for the promotion of green urbanization. On the other hand, green urbanization provides support for the improvement of tourism competitiveness. Green urbanization and tourism competitiveness complement each other and can jointly promote regional sustainable development. Therefore, in-depth research on the spatial coupling relationship and interaction mechanism between green urbanization and tourism competitiveness has important theoretical significance, and also has important practical significance for promoting urban sustainable development.

With the deepening of urbanization research, " green urbanization", which pays attention to the improvement of green development and urbanization quality, has gradually become the focus of academic research. At present, the research on green urbanization mainly focuses on five aspects: theoretical research 4 , 6 , evaluation method 7 , 8 , 9 , 10 , influencing factor 4 , 8 , 9 , 10 , dynamic mechanism 11 , and development path 12 , 13 , 14 , 15 . In terms of the research on the tourism competitiveness, the term "tourism competitiveness" began to appear in the 1990s 16 , 17 , 18 . Subsequently, after nearly 20 years of development, the theoretical research results of tourism competitiveness have been gradually enriched and a theoretical framework for the study of tourism competitiveness has been initially formed, which has laid a certain foundation for the subsequent exploration of quantitative research methods of tourism competitiveness. In terms of the assessment methods of tourism competitiveness, the index system evaluation method has been adopted in most of the existing studies 19 , 20 , 21 , and the evaluation indicators are mainly selected from five aspects: social and economic development 20 , 21 , 22 , tourism market development 6 , 20 , 21 , 22 , traffic and location conditions 21 , 22 , ecological environment 20 , 21 , 22 , and tourism resources 17 , 18 , 20 . In terms of research scale, existing research mainly carries out tourism competitiveness evaluation at provincial, city, and county scale based on social and economic statistical data 16 , 22 . In terms of the coupling relationship between green urbanization and tourism competitiveness, most studies have used bivariate spatial autocorrelation model and coupling coordination degree model to analyze the coupling relationship between green urbanization and tourism resource conversion efficiency 23 , the coupling relationship between green urbanization and tourism eco-efficiency 24 , the coupling coordination relationship between low-carbon cities and tourism development 25 , the coupling relationship between urbanization and tourism development 26 , the coupling relationship between tourism urbanization and eco-environmental quality 27 , 28 , and the coupling relationship between tourism development, urbanization and eco-environment 29 . For example, Hao et al. (2022) used SBM model, coupled coordination degree model, Tobit regression and other methods to explore the spatio-temporal evolution characteristics and influencing factors of coupled coordination degree (CCD) between new urbanization and tourism resource conversion efficiency in the Yellow River Basin 23 . Yang et al. (2022) evaluated the coordination relationship between green urbanization and sustainable tourism development from the perspective of decoupling coordination, and analyzed the spatio-temporal characteristics of the two 24 . Wang et al. (2019) used the Coupled coordination degree model (CCDM) to conduct an empirical study on the coupled coordination between low-carbon cities and tourism industry 25 . However, few studies have focused on the spatial coupling relationship between county green urbanization and tourism competitiveness. In terms of the interaction mechanism between green urbanization and tourism competitiveness, there have been more studies on the driving mechanism of green urbanization, and the driving mechanism of tourism competitiveness, but few studies have in-depth analysis of the interaction mechanism between green urbanization and tourism competitiveness. In terms of coordinated development strategy, a few scholars have proposed a coordinated development strategy between tourism competitiveness and green urbanization based on their personal research experience 30 , 31 , but less proposed differentiated regulation strategies for different types of cities.

Based on the existing research results, we can find that there are still deficiencies in the following four aspects: (1) In terms of the evaluation index of tourism competitiveness, due to the limitation of county statistical data, previous studies mainly selected five statistical indicators including socioeconomic development, tourism economic development, transportation and location conditions, and tourism resources, and rarely involves three evaluation indexes including market competitiveness, cultural resources competitiveness, environmental influence, and talent competitiveness. (2) The green urbanization system in the new era is more complex and comprehensive, but the index system constructed by existing studies seldom considers the indicators of social progress, urban–rural integration development, and the eco-environment protection indicators are not comprehensive enough. In addition, the research scale is mostly provincial and municipal, and the comprehensive evaluation of green urbanization level at county level is rarely carried out. (3) Existing studies pay little attention to the spatial coupling relationship between tourism competitiveness and green urbanization and the interaction mechanism between them, which seriously hinders the theoretical development of the interaction mechanism between tourism competitiveness and green urbanization, as well as the sustainable development of cities. (4) Existing studies pay less attention to the spatial zoning method of coordination types between tourism competitiveness and green urbanization, as well as the research on the collaborative development path between the two.

The Yellow River Basin (YRB), with its vast area and rich tourism resources, provides the essential conditions for the development of tourism, but the overall development level of tourism is currently low. At the same time, the level of regional economic development and urbanization is lower, and the quality of urbanization is lower, so it is pressing to accelerate the construction of green urbanization. Based on this, it is of vital significance to probe the spatial coupling relationship, interaction mechanism and synergistic development path of county tourism competitiveness and new urbanization on the basis of scientific evaluation of county tourism competitiveness and green urbanization level of counties in the YRB for high-quality urban development and regional sustainable development. Research ideas: (1) Based on multi-source data, build an extended assessment indicator system of county tourism competitiveness and green urbanization level, and then comprehensively evaluate the county tourism competitiveness and green urbanization level in the YRB, and analyze the spatial pattern of county tourism competitiveness and green urbanization and the spatial coupling relationship between them. (2) Based on the conceptual model of the interaction mechanism between tourism competitiveness and green urbanization, the interaction relationship between tourism competitiveness and green urbanization was systematically analyzed. (3) Construct the quantitative division method of the synergistic type areas between tourism competitiveness and green urbanization, and then divide the coordination/discoordination type areas between county tourism competitiveness and green urbanization, and further put forward targeted synergistic development strategies according to different type areas.

Materials and methods

This study was divided into three sections (Fig.  1 ). The first part was to construct an extended evaluation index system of county tourism competitiveness and green urbanization level based on multi-source data, and then comprehensively evaluate county tourism competitiveness and green urbanization level in the YRB, and analyze the spatial pattern of county tourism competitiveness and green urbanization level and their spatial coupling relationship. In the second part, we systematically analyze the mutual influence between tourism competitiveness and new urbanization, and then explain the interaction mechanism between tourism competitiveness and green urbanization. In the third section, 735 counties are classified according to the quantitative classification method of coordinated development type, and a coordinated development strategy for tourism competitiveness and new urbanization was further proposed for each type area.

Research framework of this study.

Study area and data sources

In this study, all counties in the Yellow River Basin (YRB) were taken as the study area (Fig.  2 ). Referring to the existing studies 32 , the boundaries of the study area are mainly provinces and regions through which the Yellow River flows, including 8 provincial administrative units of Shandong, Henan, Shanxi, Shaanxi, Ningxia, Inner Mongolia (excluding Chifeng, Tongliao, Hinggan League and Hulunbuir), Gansu, and Qinghai Province, and 734 county-level administrative units. The YRB spans three topographic stairways, east and west. The YRB has abundant tourism resources, which provide basic conditions for tourism development, but the overall development degree of tourism is low. Meantime, the level of regional economic development and urbanization level is low, and the development of cities varies widely, so it is urgent to accelerate the construction of green urbanization. In this context, exploring the synergistic relationship, interaction mechanism between green urbanization and tourism competitiveness at counties level in the YRB can effectively promote the regional high-quality and sustainable development.

The overview map ( a ), administrative zoning map ( b ), and elevation map of the YRB ( c ), the base map used in the maps in figure number 2, 5, 6, 7, 10 are drawn from the standard map service system of the Ministry of Natural Resources of China (Drawing review No. GS (2019) 1697, http://bzdt.ch.mnr.gov.cn/download.html ), and the base map has not been modified. Composed using ESRI ArcGIS 10.2 Software. *This work is licensed under a Creative Commons by Attribution (CC BY 4.0) license. **ESRI, China.

In this paper, 2022 was taken as the research year. The data types primarily include four aspects: (1) Administrative division data. The regionalization of provincial, municipal and county-level administrative units in the YRB was provided by Resource and Environment Science and Data Center, Chinese Academy of Sciences (RESDC) ( https://www.resdc.cn/Default.aspx ). (2) POI (Points of Interest) data. The POI data of star scenic spots, star hotels, travel agencies, public toilets and universities in 2022 are provided by RESDC ( https://www.resdc.cn/Default.aspx ). China's rural tourism key village data in 2022, Chinese history and culture town and village, town of data with Chinese characteristics in 2020, China's national intangible cultural heritage data in 2021, China's key document protection unit data in 2022, and China's traditional villages data in 2022 are come from China's state council web site ( https://www.gov.cn/zhengce/zhengceku/ ). China's time-honored brand data from the Chinese Ministry of Commerce website ( http://www.mofcom.gov.cn/article/zwgk/gkgztz/ ). (3) Remote sensing data. The raster data of land use in 2022 comes from the research results of Yang et al. (2021) (the data has been updated to 2022), with a data resolution of 30 m 33 . PM2.5 concentration raster data in 2022 set was derived from Atmospheric Composition Analysis at Saint Louis University in Beijing, USA Group ( https://sites.wustl.edu/acag/datasets/surface-pm2-5/ ) (the data has been updated to 2022), the spatial resolution of 1 km 34 , 35 . (4) Socioeconomic statistics data at county level. The county socio-economic statistics data from the county statistical almanac of China and the county socio-economic statistical bulletin.

The theoretical interaction mechanism of green urbanization and tourism competitiveness

Urban complex ecosystem is a "nature-economy-society" regional composite system integrating natural, economic, and social system 2 . The new urbanization subsystem and the tourism competitiveness subsystem are two open and interrelated subsystems in the urban complex ecosystem. There are complex interaction relations between the two subsystems, which interact and influence each other. The green urbanization adheres to the development concept of "intensive, smart, green and low-carbon", and aims to coordinate urban–rural development, integrate urban and rural areas, improve infrastructure, optimize industrial structure, intensive resources, optimize the ecological environment and make residents livable. Tourism is a comprehensive industry involving "food, accommodation, transportation, travel, shopping and entertainment". The improvement of county tourism competitiveness can promote the integration of various regional resources and industrial integrated development, and play an important role in absorbing urban and rural residents' employment, adjusting industrial structure, driving urban–rural economic development, protecting resources and environment, and improving residents' quality of life. Therefore, green urbanization and tourism competitiveness have a lot in common in terms of development concepts, development goals, focus points and goals, such as urban–rural integration, industrial coordination, resource intensification, ecological environment optimization and inhabitability. On the one hand, the improvement of tourism competitiveness can provide continuous driving force for economic urbanization, industrial low-carbon transformation, social service level, infrastructure construction, ecological environmental protection, etc., and then promote the steady development of new urbanization. On the other hand, the development of green urbanization can improve the quality of economic development, the quality of ecological environment, the perfection of infrastructure, the level of social services, and the level of coordinated development between urban and rural areas, thus improving the development level of tourism service industry, the competitiveness of infrastructure, market, service, resource, talent and environmental influence, and accelerating the virtuous circle within the tourism industry.

Based on this, we put forward the theoretical mechanism of green urbanization and tourism competitiveness (Fig.  3 ), and put forward the following hypotheses: (1) The development of green urbanization has a positive supporting effect on tourism competitiveness. (2) Tourism competitiveness plays a positive role in promoting green urbanization.

The theoretical interaction mechanism of green urbanization and tourism competitiveness.

Quantitative evaluation of green urbanization level

Green urbanization is a coordinated urbanization of industry, pop, land, society, and environment. It is oriented towards improving quality, centered on the people, driven by technological innovation, and based on the principles of green, low-carbon, and overall planning 4 , 7 , 8 . The green urbanization has great differences compared with traditional urbanization, which are reflected in the following ways: First, green urbanization pays more attention to the economic development quality and urbanization construction, as well as ecological and environmental protection 12 , 19 , 20 . Second, the green urbanization is based on urban–rural integration, industrial interaction, resource saving, pleasant living environment as the elementary features of urbanization 7 , 8 , 9 , 10 .

On the basis of a deep understanding of the connotation and essence of green urbanization, we have established a evaluation index system of green urbanization level composed of 6 criteria layers (18 specific indicators) including quality of economic development, population development, social progress, infrastructure construction, urban–rural integrated development, and eco-environmental protection (Table 1 ). In view of the difference of positive and negative properties of evaluation indicators, this study selects the method of maximum value standardization to standardize the indicator data. The entropy method was used to calculate the indicator weight.

Quantitative evaluation of tourism competitiveness

County tourism is a tourism area with regional characteristics and complete functions based on the geographical space of county administrative districts and the participation of county governments, tourism departments and enterprises. It is supported by local tourism resources with local characteristics, guided by the market and centered on tourism products 17 . Combined with the previous study results 6 , 16 , 17 , 18 , 19 , this study believes that the county tourism competitiveness is the past, present and future tourism market position and competitiveness level created by the county government, tourism departments and enterprises to realize the sustainable development of its own tourism destination and tourism industry by utilizing the local resource advantages and various opportunities.

On the basis of a deep understanding of the concept and connotation of county tourism competitiveness, this study follows the principles of concise and scientific, systematic, targeted and regional combination, the availability of index data and so on, and carries out the selection of specific evaluation indicators of county tourism competitiveness and construction of evaluation index system. Finally, the evaluation index system of county tourism comprehensive competitiveness is constructed, which consists of 3 criteria layers, 8 subdivision criteria layers and 23 specific indicators (Table 2 ). Tourism competitiveness of a county is a comprehensive evaluation of the overall strength of tourism in a specific period, reflecting the past, present and future tourism market position and competitiveness level of a county, which is mainly reflected in three aspects: real tourism competitiveness, potential tourism competitiveness and tourism competitive influence 6 , 16 , 19 , 20 . Among them, the real competitiveness of tourism is reflected by 7 specific indicators in 2 aspects, including market competitiveness and service competitiveness. The potential competitiveness of tourism is reflected by 10 specific indicators in four aspects: cultural resource competitiveness, location competitiveness, infrastructure competitiveness and human competitiveness. The competitive influence of tourism is reflected by six specific indicators in two aspects, namely economic influence and environmental influence. For the different positive and negative orientations of the indicators in the evaluation index system, this paper selects the maximum difference dormalization method to standardize the index data. The entropy method was used to calculate the indicator weight.

Bivariate spatial autocorrelation method

We adopt the Bivariate spatial autocorrelation (BSA) method to analyze whether there is a prominent spatial dependence between green urbanization and tourism competitiveness 36 . The equation is:

where \({I}_{\text{ur}}\) is the global bivariable Moran's I index between green urbanization and tourism competitiveness. n is the count of units, \({z}_{i}^{u}\) is the green urbanization level of unit i adjacent to unit j , \({z}_{j}^{r}\) is the tourism competitiveness of unit j adjacent to unit i , \({w}_{ij}\) is the weight matrix.

Optimal parameters-based geographical detector model

The Optimal parameters-based geographical detector (OPGD) model is an improvement model based on Geographical Detector (GD) model 37 , 38 , which can explore the potential factors or explanatory variables from the perspective of space, and explore the potential interaction of variables 38 . The biggest advantage of OPGD model compared with the traditional GD model is that it can identify the optimal method of data discrete and the optimal number of breakpoints.

(1) Factor detector. The detector can analyze the influencing factors of green urbanization and tourism competitiveness. The model formula:

In the formula, q value is the explanatory power of factor. h and N h is the count of layers and samples, \({\sigma }_{h}\) is the variance of the green urbanization and tourism competitiveness. \({Y}_{i}\) is the level of green urbanization and tourism competitiveness.

(2) Interaction detector. The detector can identify the interaction between influencing factors (the comprehensive effect of evaluation factors on green urbanization and tourism competitiveness). The five types of interaction are illustrated in Fig.  4 .

Interaction types.

The division method of coordination type between green urbanization and tourism competitiveness

In order to quantitatively divide the coordination types between green urbanization and tourism competitiveness, and further propose targeted collaborative development strategies according to different types of areas, this study puts forward the quantitative division method of coordination type. The detailed steps of the division method are as follows: Firstly, the extreme value standardization method is adopted to standardize the tourism competitiveness value and the green urbanization level respectively. Secondly, the ratio between the standardized value of green urbanization and the standardized value of tourism competitiveness is calculated. Finally, according to the ratio and interval value of the two, the collaborative type was divided. The formula is:

where \({x}_{i}\) and \({y}_{i}\) is the green urbanization and tourism competitiveness. \({Max(x}_{i})\) and \({Max(y}_{i})\) is the maximum value of green urbanization and tourism competitiveness. \({X}_{i}\) and \({Y}_{i}\) is the standardized value of the green urbanization and tourism competitiveness. \({A}_{i}\) is the ratio of \({X}_{i}\) to \({Y}_{i}\) , \({B}_{i}\) is the coordinated development type.

Results and discussion

The spatial coupling relationship between green urbanization and tourism competitiveness, spatial pattern of green urbanization level.

This paper selects Jenks natural break point method to grade the green urbanization level, which can be divided into six types: low level area, lower level area, medium level area, medium–high level area, higher level area, and high level area (Fig.  5 ).

Spatial distribution of green urbanization in the YRB.

As shown in Fig.  5 , the green urbanization level show a multi-core circle structure, with provincial capitals, prefecture-level city districts and economically developed counties as high-value centers, and the trend of concentrated distribution is significant. In detail, the low-level areas are mainly distributed in the central and western regions of Qinghai Province, the northwestern regions of Gansu Province, and the western and northeastern regions of Inner Mongolia. The lower-level areas are mainly distributed in the central and southern parts of Gansu, the central parts of Ningxia, the central parts of Inner Mongolia, the central and northern parts of Shaanxi Province, the northern parts of Shanxi Province, the counties under the jurisdiction of Shangqiu and Zhoukou City in eastern Henan, the southeastern counties under the jurisdiction of Nanyang City in southern Henan and the counties under the jurisdiction of Zhumadian City. The higher and high level areas are mainly distributed in the counties and districts under the jurisdiction of the provincial capital Xining City, the Chengguan District under the jurisdiction of the provincial capital Lanzhou City, the economically developed counties and districts of Gansu Province (Jiayuguan, Ganzhou, Liangzhou, Qinchuan), the provincial capitals Yinchuan City, the provincial capitals Xi 'an City, Baota District, the provincial capitals Taiyuan City, Jincheng, and the provincial capitals Zhengzhou has jurisdiction over counties, economically developed counties in the west and north of Henan Province (Jiyuan, Luoyang city jurisdiction, Nanyang city jurisdiction, Puyang city jurisdiction), the provincial capital of Jinan City, the southeast coast of Shandong Province. The medium and medium–high level areas are mainly distributed around the high and higher level areas, showing an obvious circular distribution structure.

Spatial pattern of tourism competitiveness

This paper selects Jenks natural break point method to grade the calculation results of county tourism competitiveness index, which can be divided into six types: low competitiveness area, lower competitiveness area, medium competitiveness area, medium–high competitiveness area, higher competitiveness area, and high competitiveness area.

As shown in Fig.  6 , the tourism competitiveness of counties in the Yellow River Basin shows a polycentric spatial differentiation. In detail, the regions with low and lower competitiveness are mainly distributed in western Qinghai Province, western Inner Mongolia, Northern Shaanxi Plateau, northern Shanxi, northern Henan, eastern Henan, southern Henan, western and northern Shandong. The medium and high competitiveness areas are mainly distributed in northern and southern Gansu, northern and eastern Inner Mongolia, northern Shanxi, central Henan, southern Henan and central Shandong. High and medium–high competitiveness areas are mainly distributed in eastern Qinghai Province, northern and central Gansu, northern Ningxia, northern Shaanxi and southern Shaanxi Province, central Shanxi, southern Shanxi, western Henan Province, central Shandong Province, and southeast coastal areas of Shandong Province.

Spatial distribution of county tourism competitiveness in the Yellow River Basin.

Spatial coupling pattern between green urbanization and tourism competitiveness

The results of model analysis show that the global bivariate Moran's I index between tourism competitiveness and green urbanization variables is 0.352, indicating that there is a significant positive spatial agglomeration relationship between tourism competitiveness and green urbanization variables, that is, they are in the phase of benign resonant coupling.

The global bivariate Moran's I index can only provide a global assessment of the spatial correlation between tourism competitiveness and green urbanization as a whole, but it suffers from the drawback that it ignores the instability of spatial process and cannot judge the local spatial agglomeration characteristics. Therefore, with the help of local bivariate spatial autocorrelations, we analyzed the local spatial agglomeration characteristics of county-scale tourism competitiveness and green urbanization in the Yellow River Basin. As shown in Fig.  7 , LISA agglomeration map contains four types of local spatial agglomeration, namely high-high type (HH), low–high type (LH), low-low type (LL) and high-low type (HL). Overall, there is a significant spatial agglomeration between the tourism competitiveness and new urbanization in the Yellow River Basin, and the spatial agglomeration types are mainly HH and LL types. In detail:

Spatial coupling pattern between green urbanization and tourism competitiveness in the YRB.

High-value agglomeration areas (H–H correlation type) indicate that tourism competitiveness and green urbanization are high. This type of district is mainly distributed in the provincial capital and its surrounding areas (Xining City, Lanzhou City, Yinchuan City, Hohhot city, Xi 'an City circle, Taiyuan City circle, Zhengzhou City circle, Jinan City Circle), Ordos City, Jincheng City, Luoyang City, Jiyuan City, Jiaozuo City, Jining City, Zibo City, Qingdao City, Weihai City. The reason is that the high-value agglomeration area of provincial capital is located in the political, economic and cultural center, with strong economic strength, relatively perfect urban infrastructure and social security system, high ecological environment quality and high level of green urbanization. At the same time, the historical and cultural accumulation is profound, tourism resources are rich, and tourism competitiveness is also high. Therefore, its green urbanization level and tourism competitiveness show a high value coupling development state. Ordos in Inner Mongolia has benefited from the economic development dividends brought by the development of mining industry and animal husbandry, with relatively perfect social services and urban infrastructure, and a high level of green urbanization construction. On the other hand, Ordos Plateau is rich in eco-tourism resources and has strong tourism competitiveness. Therefore, its green urbanization level and tourism competitiveness show a high value coupling development state. Luoyang, Zibo,and Jining City are all tourist cities with a long history and profound culture, with rich historical and cultural heritage and ecological tourism resources; At the same time, it has a solid industrial foundation, strong economic strength, perfect urban infrastructure construction, high social welfare level, and remarkable achievements in green urbanization. Therefore, its green urbanization level and tourism competitiveness show a high value coupling development state. Jincheng, Jiyuan, and Jiaozuo City are located in the south of Taihang Mountain, are national forest cities and national garden cities, rich in eco-tourism resources; At the same time, its economic strength is strong, the urban infrastructure and social security system is relatively perfect, the urban–rural income gap is small, the living environment is suitable, and the green urbanization construction has achieved remarkable results. Therefore, its green urbanization level and tourism competitiveness show a high value coupling development state. The development of coastal eco-tourism in Qingdao and Weihai started earlier, and the level of tourism competitiveness has always been in an advantageous position. At the same time, its industrial structure is perfect, the economic strength is strong, the urban infrastructure is perfect, the ecological environment is beautiful, so the green urbanization level is high.

Low-value cluster (L–L correlation type) indicates that tourism competitiveness and green urbanization level are low. This type of district is mainly distributed in central and western counties of Qinghai Province, southwestern counties of Gansu Province, western and eastern regions of Inner Mongolia (Alashan City), Yulin City of Shaanxi Province (Dingbian County, Zizhou City, etc.), Qinzhou City and Shuozhou City of Shanxi Province, western counties of Shangqiu City (Suixian County and Taikang County) of Henan Province, Zhumadian City and eastern counties of Xinyang City (Xincai County, Zhengyang County, Xixian County, etc.). Among them, the counties in the central and western parts of Qinghai Province and the counties in the southwest of Gansu Province are located in the plateau areas that are rarely visited by people, with low economic development and slow urbanization development. Green urbanization is still in its infancy. Meanwhile, eco-tourism resources are rich, but the level of tourism resources development is low, and tourism infrastructure is relatively backward. Therefore, tourism competitiveness and green urbanization level are in a low level coupling development stage. Alashan and Yulin City are located in the desert areas with poor ecological environment. The urbanization level is low. The primary and secondary industries are the main industries, and the development gap between urban and rural areas is large. At the same time, tourism resources are scarce and tourism infrastructure is relatively backward. Therefore, tourism competitiveness and green urbanization level are in a low level coupling development stage. Qinzhou City, Shuozhou City, western counties of Shangqiu City, and eastern counties of Xinyang and Zhumadian City are all agricultural counties. The industrial structure is dominated by the primary and secondary industries, the economic development is relatively extensive, the tourism resources are relatively scarce, and the tourism infrastructure is not perfect. Therefore, the level of green urbanization and the level of green urbanization are at a low level coupling development stage.

Low-value heterogeneous areas (Low–High correlation type) indicate that tourism competitiveness is low, while the level of green urbanization is high. This type of area is mainly distributed in the central and eastern provinces of the Yellow River basin, including Lintong District of Xi 'an, southern counties of Yuncheng (Wanrong County, Linyi County, Hengqu County, etc.), Zhongmu County of Zhengzhou City, Xinxiang Yuanyang County, central counties of Shandong Province (Guarao County, Boxing County, Hengtai County, Wulian County, etc.), and eastern counties of Shandong Province (Zouping District, Futian District). Among them, the counties around the core cities, such as Lintong District of Xi 'an, Zhongmou County of Zhengzhou, and Yantai District of Shandong Province (Zouping District and Futian District), are positively affected by the economic radiation of the core cities, with fast economic development, perfect infrastructure and high level of green urbanization. However, as an economic development zone, the resource endowment is poor, the tourism resources are scarce, the tourism infrastructure is imperfect, and the tourism market demand is low, so the tourism competitiveness is low. The development of tourism competitiveness in the southern part of Yuncheng City, the middle part of Shandong Province, and Yuanyang County are mainly restricted by the factors of resource endowment, location condition and tourism market. However, in the process of rapid urbanization, the infrastructure and social security system are gradually improved, the ecological environment is gradually improved, and the level of green urbanization has been submitted.

The high value heterogeneous area (High-Low correlation type) indicates that the tourism competitiveness is high but the new-type urbanization level is low. This type of area is mainly distributed in the western tourist cities of Gansu Province (Dunhuang City, Yumen City, etc.), the central and eastern tourist cities of Inner Mongolia (Erlianhot City, Xilinhot City, etc.), Yulin City of Shaanxi Province (Jingbian County, Wuqi County, Zhidan County, etc.), and the counties of Luliangshan Mountain region of Shaanxi Province (Pinglu District, Hunyuan County, Ningwu County, Xing County, Loufan County, Liulin County and Jiaokou County). Among them, the western cities of Gansu Province have a large number of historical and cultural monuments and ancient buildings, and the development of heritage tourism is good and the tourism competitiveness is strong, but the development level of green urbanization is low. The central and eastern Inner Mongolia cities mainly develop grassland eco-tourism, which has strong tourism competitiveness, but the development level of green urbanization is low. Yulin City in Shaanxi province mainly develops desert tourism and has strong tourism competitiveness, but its economic development mainly focuses on high-polluting industries such as coal mining and smelting industry, and its ecological environment is poor, and the development level of green urbanization is low. In recent years, the county area of Luliang Mountain in Shaanxi Province develops eco-tourism around eco-tourism resources, and the tourism competitiveness is gradually enhanced. However, the overall development speed of green urbanization is slower than the competitiveness of tourism.

Interactive mechanism between green urbanization and tourism competitiveness

The influence of green urbanization on tourism competitiveness.

(1) Analysis results of factor detector

The analysis results of OPGD model show (Table 3 ) that the explanatory power (q value) of green urbanization, quality of economic development, population development, social progress, infrastructure construction, urban–rural integrated development, and eco-environmental protection factors on tourism competitiveness is greater than 0.1, and Pearson correlation coefficient is positive, and all pass the 1% significance level test. It shows that the above factors have a significant positive impact on tourism competitiveness. Among them, the X1 has the greatest impact on tourism competitiveness, followed by the X3 , X4 , X5 , X2, X7, and X6 . The reason for this, the development of population provides population support for urban development and construction and tourism-related service industry, and directly promotes the development of economic and tourism-related service industry, and quality of economic development. Quality of economic development provides strong financial support for social development and infrastructure construction and indirectly promotes the comprehensive competitiveness of tourism. Social progress and infrastructure construction provide perfect infrastructure and comfortable tourism experience for tourism development, which directly promotes the tourism competitiveness. Eco-environmental protection factors have many influences on the tourism competitiveness. Firstly, the regions with higher ecological environmental quality generally have better tourism resource endowment. Secondly, the improvement of eco-environment quality can promote the development of regional eco-tourism. In addition, tourists' travel experience and impression can be improved by improving the quality of eco-environment, and the rate of repeat visits can be increased. The factor of urban–rural integrated development has the least impact on the tourism competitiveness.

(2) Analysis of interaction between factors

The results are shown in Fig.  8 , the interaction types of factors include double-factor enhancement, nonlinear enhancement and attenuation. In detail, the interaction between economic development quality and population development, social progress, infrastructure construction, urban–rural coordinated development, and eco-environmental protection has a strong interaction effect on tourism competitiveness. The interaction between population urbanization, infrastructure construction level and urban–rural integration level has a strong interactive impact on the tourism competitiveness, but the interaction type of population development, social progress, and eco-environmental protection is weaker type. The interaction between social progress, infrastructure construction, urban–rural coordinated development, and eco-environmental protection has a strong interaction effect on the tourism competitiveness, and the interaction between urban–rural coordinated and eco-environmental protection has a strong interaction effect on the tourism competitiveness. It shows that there is a close link between social progress, infrastructure construction, urban–rural coordinated development, and eco-environmental protection.

Interactive influence of influencing factors on tourism competitiveness.

The influence of tourism competitiveness on green urbanization

The results show (Table 4 ) that the explanatory power (q value) of tourism competitiveness, market competitiveness, service competitiveness, resource competitiveness, location competitiveness, infrastructure competitiveness, talent competitiveness and environmental influence factors are all greater than 0.1, and Pearson correlation coefficient is positive, and all pass the 1% significance level test. All the above eight factors have a significant positive impact on the level of green urbanization. The X6 has the greatest influence on the green urbanization level, followed by X1, X3, X5, X4, X2, X9, and X7. It shows that tourism competitiveness and its subsystems can observably promote the green urbanization. The reason is that the construction of transportation infrastructure such as high-speed rail, highway and bus (infrastructure competitiveness) and the construction of tourism service facilities such as star-rated hotels, travel agencies and public toilets (service competitiveness) can directly promote the improvement of the green urbanization. Superior geographical location and talent reserve in the service industry significantly promote the improvement of regional competitiveness and talent competitiveness, and also serve as a continuous driving force to promote the green urbanization. Market competitiveness, resource competitiveness and environmental influence are not only important resource endowment and basic factors required by tourism development, but also core factors of green urbanization development. Economic impact factors have the least influence on the green urbanization. The reason is that compared with the traditional urbanization that unilaterally pursues spatial expansion and economic size, green urbanization pays more attention to the comprehensive, balanced and green development of social development potential (education, science and technology, etc.), infrastructure construction, eco-environmental protection, urban and rural balanced development and other aspects.

As shown in Fig.  9 , the tourism competitiveness and environmental influence factors have the largest interactive influence on the green urbanization (q value = 0.879). The second is the interactive influence of tourism comprehensive competitiveness and service competitiveness, location competitiveness and infrastructure competitiveness, location competitiveness and market competitiveness, service competitiveness and resource competitiveness. The interactive influence of infrastructure competitiveness, market competitiveness, service competitiveness, resource competitiveness, location competitiveness, talent competitiveness, economic influence, and environmental influence. The combined effect of market competitiveness and economic influence did not enhance the interaction effect on the green urbanization. On the whole, the interaction effect of tourism competitiveness factors and their subsystems on the level of new urbanization is significantly higher than that of single factors, indicating that tourism competitiveness and the complex force among its subsystems have an obvious promoting effect on the level of new urbanization.

Interactive influence of influencing factors on green urbanization.

(1) Driving effect of tourism competitiveness on green urbanization. (1) Tourism competitiveness promotes economic urbanization construction. Tourism is a comprehensive industry involving "food, accommodation, transportation, travel, shopping and entertainment". County tourism plays a significant role in absorbing employment, adjusting industrial structure and driving regional economic development. It promotes economic urbanization by promoting the development of local characteristics industries and the transformation of service industries. (2) Tourism competitiveness promotes the equal development between urban and rural areas. At present, China's county cities generally exist dual structure problems such as urban–rural division and large urban–rural gap, and tourism can bring capital flow, people flow and logistics to rural areas by virtue of strong integration and aggregation. With the advent of the age of mass leisure, tourists' demand for the original ecological tourism distributed in the countryside is getting higher and higher. In remote rural areas with rich tourism resources, rich cultural deposits and rich ethnic customs, tourism can integrate various tourism resources and funds, combine tourism development with poverty alleviation, improve farmers' income, and promote the equal development between urban and rural areas. (3) The promotion of tourism competitiveness can promote population urbanization. As a labor-intensive industry, tourism can not only provide direct employment, but also promote indirect employment. On the one hand, it can provide a large number of employment opportunities for travel agencies, catering and accommodation, entertainment and leisure, tourist attractions and other directly related industries. On the other hand, it can also provide employment opportunities for transportation, health care, insurance and other industries indirectly related to tourism, increase the employment rate of urban and rural population, and ultimately promote population urbanization. (4) County tourism competitiveness promotes infrastructure construction and social comprehensive development. On the one hand, cities with strong tourism competitiveness will carry out tourism-related infrastructure construction and public service supporting facilities to optimize tourism services and increase tourists' satisfaction. On the other hand, the improvement of tourism competitiveness will attract a large number of financial funds and social funds into the development of tourism and the construction of related supporting industries, thus promoting the construction of infrastructure and improving the social services. (5) Tourism competitiveness promotes ecological urbanization. Tourism development can promote the construction of more parks and green landscapes in cities and towns, improve the ecological environment and air quality. In addition, the improvement of tourism competitiveness and tourism innovation level is conducive to the emergence of new tourism business forms, promote the continuous transformation of urban functions and industrial structure to the direction of green, ecological and low-carbon, and promote ecological urbanization.

In summary, the improvement of tourism competitiveness can provide continuous driving force for economic urbanization, industrial transformation and upgrading, population urbanization, social services, infrastructure construction, cultural inheritance, ecological and environmental protection, and thus promote the steady development of green urbanization.

(2) The supporting effect of green urbanization on tourism competitiveness. Population agglomeration brings a large number of labor resources to cities and towns, and also provides population support for urban development and construction and tourism-related service industry, which directly promotes the development of economic and tourism-related service industry. Economic development provides strong financial support for social progress and infrastructure construction and indirectly promotes the tourism competitiveness. Eco-environment protection can promote the development of regional eco-tourism, and at the same time, it can also improve the tourism experience and impression of tourists by improving the quality of ecological environment, and increase the repeat rate. Along with the construction and development of green urbanization, with its excellent conditions such as perfect infrastructure, environment and modern education, green urbanization has enhanced the attraction of scientific and technological innovative talents. At the same time, green urbanization has a "siphon effect", enabling innovative elements such as capital, technology and knowledge to gather in cities and towns, providing important support for the development of tourism industry. On the whole, the development of green urbanization can support the development of tourism service industry, tourism infrastructure construction, tourism personnel training and eco-environment improvement, so as to enhance the development level of tourism service industry, tourism infrastructure competitiveness, talent competitiveness and environmental influence. In addition, in the process of new urbanization development, tourism industry agglomeration will be formed, and high-tech innovative tourism enterprises will constantly impact traditional tourism enterprises, enhance the competition among tourism industries and improve the competitiveness of tourism products. The formation of "locking effect" is helpful to improve the science and technology level, and further promote the improvement of tourism innovation level. When the tourism industry enters the stage of rapid development and a virtuous cycle, it will attract a large number of financial funds and social funds into the tourism development and the construction of related supporting industries, drive the local economic influence, market competitiveness, service competitiveness and resource competitiveness, and accelerate the virtuous cycle within the tourism industry.

The coordinated development pathway of green urbanization and tourism competitiveness

The division of coordinated development types.

According to the spatial zoning method of coordinated development type, 735 counties and districts are divided into 6 types of districts. That is, mild coordination zone type I (52), mild coordination zone type II (231), mild discoordination zone (132), moderate discoordination zone (124), severe discoordination zone (124), and extremely discoordination zone (103) (Fig.  10 ). Take a closer look:

Results of classification of coordinated development types.

(1) Type I of mild coordination area and type II of mild coordination area. Among them, type I of mild coordination area represents the tourism competitiveness value of counties slightly less than the green urbanization. This type can be subdivided into type I of double-lag coordination area (both tourism competitiveness and green urbanization level are small) and type I of double-high coordination area (both tourism competitiveness and new urbanization level are large). The tourism competitiveness value of type II of mild coordination area is slightly greater than that of green urbanization. This type area can also be subdivided into type II of double-lag coordination and type II of double-high coordination. Type I of double-lag coordination area and double-lag coordination type II are mainly distributed in plain counties in the middle of the YRB, mountain counties and agricultural counties in the east region. Type I of double-high coordination area and type II of double-high coordination area are principally distributed in the economically developed region of the Guanzhong Basin and Fenhe River Basin, the central and western Henan Province (Luoyang, Zhengzhou, Pingdingshan, and Xuchang), the southern coast of Shandong Province (Rizhao, Qingdao, and Weihai), and Jinan metropolitan area.

(2) Mildly incongruous area. In this type of area, the ratio between the standardized value of tourism competitiveness and the standardized value of green urbanization level is 1.1–1.2, and the overall situation is slightly uncoordinated (tourism competitiveness is strong, but green urbanization is relatively weak). This type of area is principally distributed in the surrounding regions of the provincial capitals Xining and Yinchuan City, the tourist cities in northern Gansu Province, the mountainous counties of Liupanshan Mountain in Gansu Province, the counties around Xi 'an Metropolitan area, the Qinling Mountains, the Zhongtiao Mountains and Taihang Mountains in Shanxi Province, the mountainous counties in the northwest of Henan Province, the plain counties in southern Henan Province, and the coastal counties in the southeast of Shandong Province. According to its characteristics, it can be divided into three categories: (1) economically developed cities around provincial capitals; (2) Mountainous and coastal tourist cities with relatively weak economic development level but rich natural tourism resources; (3) Western tourist cities with relatively weak economic development level but rich natural resources and cultural heritage.

(3) Moderate dissonance area. This type of area is in a moderately uncoordinated state (standardized value of tourism competitiveness/standardized value of green urbanization level = 1.2–1.3). Compared with the mildly uncoordinated area, the gap between tourism competitiveness and the green urbanization level is larger, and the development level of tourism competitiveness is better than that of green urbanization. This type of area is principally distributed in the central region of the YRB, including the areas around the provincial capitals Xining City, Lanzhou City and Yinchuan City, the tourist cities in central and northern Gansu Province, Alashan Left Banner in central Inner Mongolia, Yulin City in the east of the Mu Us Desert, the Qinling Mountains, the mountains in central and eastern Shanxi Province, the counties in the western Henan Mountains and the counties in the southern Henan mountains.

(4) Severe dissonance area and extreme dissonance area. The severely uncoordinated area represents that the standardized value of tourism competitiveness is much higher than that of green urbanization (the ratio between the two is 1.3–1.4). The extremely uncoordinated areas represent that the standardized value of tourism competitiveness is much higher than that of green urbanization (the ratio between the two is greater than 1.4). The two types of regions coincide with each other in spatial distribution and are clustered, mainly distributed in large scale in most of Qinghai Province, the northern border area of Inner Mongolia, the eastern part of the Mu Us Desert in northern Shaanxi Province, the western mountainous area of Shanxi Province, as well as the economically underdeveloped inter-provincial border areas and remote mountainous areas with inconvenient transportation.

Strategies for coordinated development

(1) Mild coordination zone Type I and mild coordination zone type II. Type I of double lag coordination and type II of double lag coordination should make use of economic advantages and characteristic advantageous industries to promote the optimization, transformation and upgrading of industrial structure and drive the local economy. Improve people's living standards, improve the eco-environment, and make every effort to promote the construction of green urbanization. At the same time, tourism resources with local characteristics should be fully tapped to develop highly attractive tourism projects. The government should guide financial funds and social funds to invest in the development of tourism and the construction of related supporting industries, and promote the construction of tourism infrastructure and related supporting industries of public service facilities. Type I of double-high coordination and type II of double-high coordination should rely on rich city sightseeing resources and natural tourism resources, encourage cultural and tourism integration and innovation and increase tourism investment, improve the training system of tourism practitioners, strengthen the construction of tourism colleges and universities, improve the construction of tourism transportation, tourism catering, continue to innovate and develop, do a good job in "tourism+", Actively promote the transformation and upgrading of the tourism industry, and promote the further development of new urbanization with tourism innovation. In terms of the development strategy of new urbanization, we should improve the medium and long-term planning of new urbanization, and realize coordinated and intensive development among resources, industries, cities, management and regional development according to the comprehensive supply level of regional water resources, land resources, energy and mineral resources. Abolish the household registration system that separates urban and rural areas, establish the withdrawal and compensation mechanism of the right to use rural housing land, and establish and improve the urban and rural public service and social security system. We will improve cross-regional mechanisms for compensating and balancing the occupation and subsidy of resource protection, and promote coordinated development between urban development, resource exploitation and utilization, farmland protection and ecological protection. We will continue to build a new type of smart city that puts people first and develops harmoniously in the fields of science, education, culture, health and sports, so as to provide support and guarantee for improving the comprehensive competitiveness of tourism.

(2) Mildly incongruous zone. This type of area indicates that tourism competitiveness is strong, but green urbanization is relatively weak. In terms of green urbanization development strategy, emphasis should be placed on promoting green urbanization construction, developing moderate, intensive, green, and sharing type of towns. With ecology as the carrier, ecological and environmental protection mechanism should be established, and systematized, standardized and market-oriented ecological compensation mechanism should be gradually established. Formulate policies for the development of circular economy, and gradually establish a mechanism for the full coverage of resource recycling. We will formulate policies to support ecological industries, give priority to the development of resource-saving and environment-friendly industries, and encourage the development of high-tech and service industries with low dependence on resources and high added value. In terms of tourism competitiveness development strategy, this type of area is generally rich in natural tourism resources (mountain scenery, forest landscape, coastal landscape), urban sightseeing scenery and cultural relics, and the overall development level of tourism is also high. In the future, we should continue to improve the training system of tourism practitioners, strengthen the construction of tourism colleges and universities, and improve the construction of tourism transportation and tourism catering. Continue to innovate and develop, do a good job in "tourism+", actively promote the transformation and upgrading of the tourism industry, and promote the development of green urbanization with tourism innovation.

(3) Moderately uncoordinated zone. Compared with the mildly uncoordinated area, the gap between the tourism competitiveness and the green urbanization is larger, and the tourism competitiveness level is better than the green urbanization. In terms of tourism competitiveness development strategy, the tourism competitiveness of this type of area is strong. It should continue to improve the training system of tourism practitioners, strengthen the construction of tourism colleges, improve the construction of tourism transportation, tourism catering, and actively promote the transformation and upgrading of the tourism industry. In terms of the green urbanization development strategy, we should pay more attention to promoting the construction of new urbanization, pay attention to grasp their own advantages and characteristics, build a system of urban linkage and urban–rural complementarity, focus on creating characteristic towns with characteristic industries as the core, in order to promote the construction of new urbanization, in the process of promoting new urbanization, explore their own advantages, and gradually promote the development of industry. And then promote the improvement of tourism innovation ability. Mountain counties should develop the towns of moderate type, intensive type, green type, and sharing type, rationally planning and regulating the speed and scale of city extension, and build a protective and cooperative land use pattern through the planning and control of main functional zones. The speed of land urbanization should match the needs of population and industrial development, and the degree of intensive use and cohesion of urban land should be improved. We will strictly regulate market access policies for high-polluting and energy-intensive enterprises, and guide and foster low-carbon and green industries. In addition, with the ecology as the carrier, we will establish a mechanism for protecting the ecological environment, and gradually establish a systematic, standardized and market-based mechanism for compensating for ecological losses. Formulate a circular economy development policy, and gradually establish a full coverage of resources recycling mechanism. We will formulate policies to support ecological industries, give priority to the development of resource-saving and environment-friendly industries, and encourage the development of high-tech and service industries with low dependence on resources and high added value.

(4) Severely incongruous zone and extremely incongruous zone. The tourism competitiveness and green urbanization of these two types of areas are both in the primary stage of development, and the tourism competitiveness is better than that of green urbanization. In terms of tourism development, construction and competitiveness improvement, first of all, we should introduce successful tourism development experience of typical counties in central and eastern regions, formulate short and medium term development plans to promote tourism development, construction and competitiveness improvement according to regional advantages and characteristics, and guide the gradual and orderly development of regional tourism. Fully tap the tourism resources with regional characteristics, develop highly attractive tourism projects, and expand the influence of the tourism industry. We should improve the training system for tourism professionals, strengthen the construction of tourism colleges and universities, strengthen the construction of tourism transportation and tourism catering, continue to innovate and explore, and actively promote the development of tourism industry and its related supporting industries. In terms of green urbanization development strategy, first, establish a market-oriented mechanism for ecological industry cultivate based on the market. There are enormous discrepancy in resource endowment and industrial base among economically underdeveloped counties. According to the "goose array theory" in economics, each economy should rely on its own resource advantages to promote the upgrading and change of industrial structure, and form a "high-end, high-quality, high-tech, low-carbon and ecological green industry system, so that green industry can become a new engine. Second, with the support of science and technology, the establishment of ecological energy technology innovation mechanism. Scientific and technological progress and independent innovation should be taken as an important support and way to accelerate the transformation of economic development mode, focusing on the commonalities of pillar industries, strategic emerging industries and key industrial clusters and key technological breakthroughs. Third, we need to establish a mechanism for protecting the eco-environment, with ecology as the carrier. Gradually establish a systematic, standardized and market-oriented ecological compensation mechanism. Formulate development policies for the circular economy, and gradually establish a comprehensive mechanism for the recycling of resources. We will formulate policies to support ecological industries, give priority to the development of resource-saving and environment-friendly industries, and incite the cultivate of high-tech industries with low dependence on resources and high added value.

Conclusions

Exploring the spatial cooperative relationship and interaction mechanism between green urbanization and tourism competitiveness is of great significance for promoting urban sustainable development. In this study, the green urbanization level and tourism competitiveness in the YRB were scientifically evaluated based on multi-source data, and then the interactive mechanism and collaborative development path of green urbanization and tourism competitiveness were deeply discussed. The main conclusions are as follows:

(1) The green urbanization shows a multi-core circle structure with provincial capitals, prefecture-level city districts and economically developed counties as the high-value centers, and the trend of agglomeration distribution is significant. The tourism competitiveness presents a polycentric spatial structure. Additionally, a significant spatial coupling relationship between green urbanization and tourism competitiveness exists in the YRB.

(2) Factors such as green urbanization level, social progress, population development, infrastructure construction, economic development quality, and eco-environmental protection all have significant effects on the tourism competitiveness. Tourism competitiveness, service competitiveness, location competitiveness, resource competitiveness, market competitiveness, environmental influence and talent competitiveness have a significant impact on the green urbanization level.

(3) The green urbanization has multiple supporting effects on tourism competitiveness. The development of population development provides population support for industrial development, tourism and related service industries. Social progress and infrastructure construction play a basic supporting role in tourism competitiveness. Economic development quality provides strong financial support for social development and infrastructure construction and indirectly promotes the tourism competitiveness. Eco-environmental protection can promote the quality of the eco-environment and improve tourists' travel experience and impression. Tourism competitiveness has a strong driving effect on green urbanization. The improvement of tourism competitiveness can provide a continuous driving force for economic development quality, industrial transformation, population development, social progress, infrastructure construction, eco-environmental protection, and thus promote the steady development of green urbanization. Tourism competitiveness and green urbanization complement each other. Tourism competitiveness can promote the development of green urbanization, and the improvement of green urbanization level can support tourism competitiveness. The two complement each other, forming a virtuous circle of tourism competitiveness and green urbanization, and ultimately promoting the high-quality development and sustainable development of counties together.

(4) According to the spatial division method of coordinated development types, 735 counties and districts were divided into 6 t0ypes, namely, type I of mildly coordinated area, type II of mildly coordinated area, mildly uncoordinated area, moderately uncoordinated area, severely uncoordinated area and extremely uncoordinated area. Then, according to the development situation of different types of areas, it puts forward the coordinated development strategy of tourism competitiveness and green urbanization of each type areas.

This study systematically explores the spatial coupling relationship and interaction mechanism between green urbanization and tourism competitiveness, which is of great significance for promoting sustainable urban development. However, there are still some limitations and disadvantages. First, due to the lack of indicators related to infrastructure competitiveness before 2022 (vector data of high-speed rail sites, airport sites and highway networks) and service competitiveness before 2016 (number of star-rated hotels, travel agencies and public toilets in the counties). Therefore, only cross-sectional data (2022) was used in this study, but panel data was not used. Second, due to the difficulty in obtaining county index data, the evaluation index system of county green urbanization and tourism competitiveness proposed in this study needs to be further improved. In the future, remote sensing technology, big data technology and survey questionnaire can be used to obtain more evaluation index data, so as to improve the evaluation index system of county green urbanization and tourism competitiveness. Third, this study has not considered the negative impact factors on green urbanization and tourism competitiveness, as well as the negative impact factors of the coupling relationship between the two, which is what future research needs to pay attention to. Fourth, although this study attempts to systematically analyze the theoretical interaction mechanism between green urbanization and tourism competitiveness, its theoretical interaction mechanism has not been fully revealed. Therefore, the theoretical interaction mechanism between green urbanization and tourism competitiveness remains the focus and difficulty of future research.

Data availability

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.

Fang, C. L. How to promote the green development of urbanization in the Tibetan Plateau?. J. Geogr. Sci. 33 (3), 639–654 (2023).

Article   Google Scholar  

Shen, W., Li, Y. & Qin, Y. C. Research on the influencing factors and multi-scale regulatory pathway of ecosystem health: A case study in the Middle Reaches of the Yellow River, China. J. Clean. Prod. 406 , 137038 (2023).

Klimanova, O., Illarionova, O. & Grunewald, K. Green infrastructure, urbanization, and ecosystem services: The main challenges for Russia’s largest cities. Land 10 (12), 1292 (2021).

Shi, L. P., Cai, Z. Y. & Ding, X. H. What factors affect the level of green urbanization in the Yellow River Basin in the context of new-type urbanization?. Sustainability 12 , 2488 (2020).

Erick, W. O., Damiannah, K. & Jackson, K. T. Effect of knowledge creation by tourism stakeholders on destination competitiveness, Baringo County, Kenya. Int. J. Bus. Manag. 8 (3), 24940 (2020).

Google Scholar  

Shi, Y. Q., Zhong, L. S., Chen, T. & Yu, H. Tourism competitiveness evaluation and spatio-temporal characteristics of Chinese border counties. Chin. Geogr. Sci. 26 (6), 817–828 (2016).

Cheng, Z. H. & Wang, L. Can new urbanization improve urban total-factor energy efficiency in China?. Energy 266 , 126494 (2023).

Huang, Z. Y., An, X. Y. & Cai, X. R. The impact of new urbanization on PM25 concentration based on spatial spillover effects: Evidence from 283 cities in China. Sustain. Cities Soc. 90 , 104386 (2023).

Xiao, Y., Huang, H. & Qian, X. M. Can green urbanization reduce urban building carbon emissions? New evidence from China. Sustain. Cities Soc. 90 , 104410 (2023).

Feng, Y. D., Yuan, H. X., Liu, Y. B. & Zhang, S. H. Does green urbanization policy promote green energy efficiency? Evidence from a quasi-natural experiment in China. Energy Econ. 123 , 106752 (2023).

Ma, L., Xiang, L. G., Wang, C., Chen, N. C. & Wang, W. Spatiotemporal evolution of urban carbon balance and its response to green urbanization: A case of the middle reaches of the Yangtze River Urban Agglomerations, China. J. Clean. Prod. 380 , 135122 (2022).

Article   CAS   Google Scholar  

Shan, Z. R. & Huang, Y. P. Analysis of concept connotation, target content, planning strategy and cognitive misunderstanding of “new urbanization”. Urban Plan. Forum 20 (2), 16–22 (2013).

Chen, M. X., Gong, Y. H., Lu, D. D. & Ye, C. Build a people-oriented urbanization: China’s green urbanization dream and Anhui model. Land Use Policy 80 , 1–9 (2019).

Zeng, L. J., Wang, J. F. & Zhang, J. S. A path matching model on new urbanization in mineral resource abundant regions. Resour. Policy 73 , 102214 (2021).

Zeng, L. J., Zhao, Y. & Wang, X. L. How to develop the new urbanization in mineral resources abundant regions in China? A VIKOR-based path matching model. Resour. Policy 79 , 103095 (2022).

Cracolici, M. F., Nijkamp, P. & Rietveld, P. Assessment of tourism competitiveness by analysing destination efficiency. Tour. Econ. 14 (2), 325–342 (2008).

Cibinskiene, A. & Snieskiene, G. Evaluation of city tourism competitiveness. Procedia Soc. Behav. Sci. 213 , 105–110 (2015).

Zhang, H. Q., He, Y. F. & Tian, X. H. Study on the evaluation system for county tourism competitiveness in Guangdong Province. Econ. Geogr. 32 (9), 172–176 (2012).

Yao, S. M., Zhang, P. Y., Yu, C., Li, G. Y. & Wang, C. X. The theory and practice of new urbanization in China. Sci. Geogr. Sin. 34 (6), 641–647 (2014).

Fu, Z., Liu, H. Z. & Liang, W. L. Construction of evaluation system of county tourism competitiveness from the perspective of whole region. Stat. Decis. 34 (22), 60–64 (2018).

Geng, N. N. & Shao, X. Y. Evaluation of provincial tourism competitiveness in the Yellow River Basin based on clustering algorithm. Mob. Inf. Syst. 2022 , 8904499 (2022).

Yan, X. G., Liu, M. & Lu, Y. S. Spatial stratified heterogeneity and influencing factors of county tourism competitiveness in Jinzhong City. J. Hainan Normal Univ. (Nat. Sci.) 35 (2), 208–218 (2022).

Hao, M., Li, G., Chen, C. Y. & Liang, L. T. A coupling relationship between new-type urbanization and tourism resource conversion efficiency: A case study of the Yellow River Basin in China. Sustainability 14 (21), 14007 (2022).

Yang, X. Y. et al. Coupling and interaction between tourism eco-efficiency and new urbanization in the Yangtze River Economic Belt: Based on the perspective of uncoupling coordination. Environ. Dev. Sustain. 25 (11), 13171–13197 (2022).

Wang, Q. R., Mao, Z. X., Xian, L. H. & Liang, Z. X. A study on the coupling coordination between tourism and the low-carbon city. Asia Pac. J. Tour. Res. 24 (6), 550–562 (2019).

Zhang, P. Y. et al. Coupled and coordinated development of the tourism industry and urbanization in marginal and less developed regions—Taking the mountainous border areas of Western Yunnan as a case study. Land 12 (3), 640 (2023).

Article   MathSciNet   Google Scholar  

Xu, D. et al. Quantization of the coupling mechanism between eco-environmental quality and urbanization from multisource remote sensing data. J. Clean. Prod. 321 , 128948 (2021).

Ma, M. Y. & Tang, J. X. Interactive coercive relationship and spatio-temporal coupling coordination degree between tourism urbanization and eco-environment: A case study in Western China. Ecol. Indic. 142 , 109149 (2022).

Tian, L., Sun, F. Z. & Zhang, Z. B. Coupling coordination and driving mechanism of tourism industry, urbanization, and ecological environment: A case study of Shandong, China. Environ. Dev. Sustain. https://doi.org/10.1007/s10668-023-03815-8 (2023).

Article   PubMed   PubMed Central   Google Scholar  

Zhao, H. Y. & Shi, X. H. Study on the development strategy of tourism city planning in Harbin under the background of new urbanization. IOP Conf. Ser. Earth Environ. Sci. 787 (1), 012055 (2021).

Luo, Z. H., Wang, F. L., Pan, H. Z., Li, L. & Dai, M. Evaluation and improvement strategy of tourism industrial park competitiveness from the perspective of new urbanization: Case study of the suining tourism industrial park. J. Urban Plan. Dev. 148 (4), 865 (2022).

Zhang, B. F. & Miao, C. H. Spatio-temporal pattern evolution and driving forces of land use in the Yellow River Basin. Resour. Sci. 42 (3), 460–473 (2020).

Yang, J. & Huang, X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. Earth Syst. Sci. Data 13 , 3907–3925 (2021).

Article   ADS   Google Scholar  

Hammer, M. S. et al. Global estimates and long-term trends of fine particulate matter concentrations (1998–2018). Environ. Sci. Technol. 54 (13), 7879–7890 (2020).

Article   ADS   CAS   PubMed   Google Scholar  

VanDonkelaar, A., Hammer, M. S., Bindle, L., Brauer, M. & Brook, J. R. Monthly global estimates of fine particulate matter and their uncertainty. Environ. Sci. Technol. 55 (22), 15287–15300 (2021).

Article   ADS   CAS   Google Scholar  

Zhou, K. & Fan, J. Regional disparity of environmental pollution source and its socio-economic influencing factors: Based on the cross-section data of 339 cities at prefecture level or above in China. Acta Geogr. Sin. 71 (11), 1911–1925 (2016).

Wang, J. F. & Xu, C. D. Geodetector: Principle and prospective. Acta Geogr. Sin. 72 (1), 116–134 (2017).

Song, Y., Wang, J., Ge, Y. & Xu, C. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data. GI Sci. Remote Sens. 57 (5), 593–610 (2020).

Download references

This research was supported by the National Natural Science Foundation of China (42101206), the Key Disciplines of Tourism Management in Henan Province, the Collaborative Innovation Center of Smart Tourism in Henan Province, the Key R&D and Promotion Projects in Henan Province_key projects of soft science research (232400411024), Outstanding Youth Science Fund of Henan Province (242300421144), Henan Science and Technology Innovation Talent Project (24HASTIT050), the Key Research Project of Higher Education Institutions of Henan Province (24A170023, 24A170022, 24B170008).

Author information

Authors and affiliations.

College of Geography and Tourism, Luoyang Normal University, Luoyang, 471022, China

Wei Shen, Weiwei Cao, Ruyi Yu & Jinlong Cheng

College of Law and Sociology, Luoyang Normal University, Luoyang, 471022, China

College of Geography and Environmental Science, Henan University, Kaifeng, 475004, China

Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Kaifeng, 475004, China

You can also search for this author in PubMed   Google Scholar

Contributions

W.S.: Conceptualization, software, methodology, writing—original draft preparation, writing—reviewing and editing. J.C.: validation, data curation, software, visualization, and funding acquisition. Y.C.: software, methodology, and data curation. W.C.: validation, data curation, software. R.Y.: Conceptualization, data curation, Supervision. All the authors consent to transfer publishing rights to the journal.

Corresponding author

Correspondence to Jinlong Cheng .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Shen, W., Chen, Y., Cao, W. et al. Coupling and interaction mechanism between green urbanization and tourism competitiveness based an empirical study in the Yellow River Basin of China. Sci Rep 14 , 13167 (2024). https://doi.org/10.1038/s41598-024-64164-8

Download citation

Received : 09 March 2024

Accepted : 05 June 2024

Published : 07 June 2024

DOI : https://doi.org/10.1038/s41598-024-64164-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Green urbanization
• Tourism competitiveness
• Spatial coupling relation
• Interactive mechanism
• Coordinated development strategies

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Take advantage of the search to browse through the World Heritage Centre information.

Share on social media

Unesco social media, world heritage list, afghanistan.

• Minaret and Archaeological Remains of Jam
• Cultural Landscape and Archaeological Remains of the Bamiyan Valley
• Natural and Cultural Heritage of the Ohrid region * 1
• Historic Centres of Berat and Gjirokastra
• Ancient and Primeval Beech Forests of the Carpathians and Other Regions of Europe *
• Al Qal'a of Beni Hammad
• M'Zab Valley
• Tassili n'Ajjer #
• Kasbah of Algiers
• Madriu-Perafita-Claror Valley
• Mbanza Kongo, Vestiges of the Capital of the former Kingdom of Kongo

Antigua and Barbuda

• Antigua Naval Dockyard and Related Archaeological Sites
• Los Glaciares National Park #
• Jesuit Missions of the Guaranis: San Ignacio Mini, Santa Ana, Nuestra Señora de Loreto and Santa Maria Mayor (Argentina), Ruins of Sao Miguel das Missoes (Brazil) *
• Iguazu National Park
• Cueva de las Manos, Río Pinturas
• Península Valdés
• Ischigualasto / Talampaya Natural Parks
• Jesuit Block and Estancias of Córdoba
• Quebrada de Humahuaca
• Qhapaq Ñan, Andean Road System *
• The Architectural Work of Le Corbusier, an Outstanding Contribution to the Modern Movement *
• Los Alerces National Park
• ESMA Museum and Site of Memory – Former Clandestine Centre of Detention, Torture and Extermination
• Monasteries of Haghpat and Sanahin
• Cathedral and Churches of Echmiatsin and the Archaeological Site of Zvartnots
• Monastery of Geghard and the Upper Azat Valley
• Great Barrier Reef
• Kakadu National Park
• Willandra Lakes Region
• Lord Howe Island Group
• Tasmanian Wilderness
• Gondwana Rainforests of Australia 2
• Ulu r u-Kata Tju t a National Park 3
• Wet Tropics of Queensland
• Shark Bay, Western Australia
• K’gari (Fraser Island)
• Australian Fossil Mammal Sites (Riversleigh / Naracoorte)
• Heard and McDonald Islands
• Macquarie Island
• Greater Blue Mountains Area
• Purnululu National Park
• Royal Exhibition Building and Carlton Gardens
• Sydney Opera House
• Australian Convict Sites
• Ningaloo Coast
• Budj Bim Cultural Landscape
• Historic Centre of the City of Salzburg
• Palace and Gardens of Schönbrunn
• Hallstatt-Dachstein / Salzkammergut Cultural Landscape
• Semmering Railway
• City of Graz – Historic Centre and Schloss Eggenberg
• Wachau Cultural Landscape
• Historic Centre of Vienna
• Fertö / Neusiedlersee Cultural Landscape *
• Prehistoric Pile Dwellings around the Alps *
• Frontiers of the Roman Empire – The Danube Limes (Western Segment) *
• The Great Spa Towns of Europe *
• Walled City of Baku with the Shirvanshah's Palace and Maiden Tower
• Gobustan Rock Art Cultural Landscape
• Historic Centre of Sheki with the Khan’s Palace
• Hyrcanian Forests *
• Cultural Landscape of Khinalig People and “Köç Yolu” Transhumance Route
• Qal’at al-Bahrain – Ancient Harbour and Capital of Dilmun
• Pearling, Testimony of an Island Economy
• Dilmun Burial Mounds
• Historic Mosque City of Bagerhat
• Ruins of the Buddhist Vihara at Paharpur
• The Sundarbans
• Historic Bridgetown and its Garrison
• Białowieża Forest *
• Mir Castle Complex
• Architectural, Residential and Cultural Complex of the Radziwill Family at Nesvizh
• Struve Geodetic Arc *
• Flemish Béguinages
• La Grand-Place, Brussels
• The Four Lifts on the Canal du Centre and their Environs, La Louvière and Le Roeulx (Hainaut)
• Belfries of Belgium and France * 4
• Historic Centre of Brugge
• Major Town Houses of the Architect Victor Horta (Brussels)
• Neolithic Flint Mines at Spiennes (Mons)
• Notre-Dame Cathedral in Tournai
• Plantin-Moretus House-Workshops-Museum Complex
• Stoclet House
• Major Mining Sites of Wallonia
• Colonies of Benevolence *
• Funerary and memory sites of the First World War (Western Front) *
• Belize Barrier Reef Reserve System
• Royal Palaces of Abomey
• W-Arly-Pendjari Complex *
• Koutammakou, the Land of the Batammariba *

Bolivia (Plurinational State of)

• City of Potosí
• Jesuit Missions of the Chiquitos
• Historic City of Sucre
• Fuerte de Samaipata
• Noel Kempff Mercado National Park
• Tiwanaku: Spiritual and Political Centre of the Tiwanaku Culture

Bosnia and Herzegovina

• Old Bridge Area of the Old City of Mostar
• Mehmed Paša Sokolović Bridge in Višegrad
• Stećci Medieval Tombstone Graveyards *
• Okavango Delta
• Historic Town of Ouro Preto
• Historic Centre of the Town of Olinda
• Historic Centre of Salvador de Bahia
• Sanctuary of Bom Jesus do Congonhas
• Iguaçu National Park
• Serra da Capivara National Park
• Historic Centre of São Luís
• Atlantic Forest South-East Reserves
• Discovery Coast Atlantic Forest Reserves
• Historic Centre of the Town of Diamantina
• Central Amazon Conservation Complex 5
• Pantanal Conservation Area
• Brazilian Atlantic Islands: Fernando de Noronha and Atol das Rocas Reserves
• Cerrado Protected Areas: Chapada dos Veadeiros and Emas National Parks
• Historic Centre of the Town of Goiás
• São Francisco Square in the Town of São Cristóvão
• Rio de Janeiro: Carioca Landscapes between the Mountain and the Sea
• Pampulha Modern Ensemble
• Valongo Wharf Archaeological Site
• Paraty and Ilha Grande – Culture and Biodiversity
• Sítio Roberto Burle Marx
• Boyana Church
• Madara Rider
• Rock-Hewn Churches of Ivanovo
• Thracian Tomb of Kazanlak
• Ancient City of Nessebar
• Pirin National Park
• Rila Monastery
• Srebarna Nature Reserve
• Thracian Tomb of Sveshtari

Burkina Faso

• Ruins of Loropéni
• Ancient Ferrous Metallurgy Sites of Burkina Faso
• Cidade Velha, Historic Centre of Ribeira Grande
• Temple of Preah Vihear
• Temple Zone of Sambor Prei Kuk, Archaeological Site of Ancient Ishanapura
• Koh Ker: Archaeological Site of Ancient Lingapura or Chok Gargyar
• Dja Faunal Reserve
• Sangha Trinational *
• L’Anse aux Meadows National Historic Site
• Nahanni National Park #
• Dinosaur Provincial Park
• Kluane / Wrangell-St. Elias / Glacier Bay / Tatshenshini-Alsek # * 6
• Head-Smashed-In Buffalo Jump
• S G ang Gwaay
• Wood Buffalo National Park
• Canadian Rocky Mountain Parks # 7
• Historic District of Old Québec
• Gros Morne National Park
• Old Town Lunenburg
• Waterton Glacier International Peace Park *
• Miguasha National Park
• Rideau Canal
• Joggins Fossil Cliffs
• Landscape of Grand Pré
• Red Bay Basque Whaling Station
• Mistaken Point
• Pimachiowin Aki
• Writing-on-Stone / Áísínai’pi
• Tr’ondëk-Klondike

Central African Republic

• Manovo-Gounda St Floris National Park
• Lakes of Ounianga
• Ennedi Massif: Natural and Cultural Landscape
• Rapa Nui National Park
• Churches of Chiloé
• Historic Quarter of the Seaport City of Valparaíso
• Humberstone and Santa Laura Saltpeter Works
• Sewell Mining Town
• Settlement and Artificial Mummification of the Chinchorro Culture in the Arica and Parinacota Region
• Imperial Palaces of the Ming and Qing Dynasties in Beijing and Shenyang
• Mausoleum of the First Qin Emperor
• Mogao Caves
• Mount Taishan
• Peking Man Site at Zhoukoudian
• The Great Wall
• Mount Huangshan
• Huanglong Scenic and Historic Interest Area
• Jiuzhaigou Valley Scenic and Historic Interest Area
• Wulingyuan Scenic and Historic Interest Area
• Ancient Building Complex in the Wudang Mountains
• Historic Ensemble of the Potala Palace, Lhasa 8
• Mountain Resort and its Outlying Temples, Chengde
• Temple and Cemetery of Confucius and the Kong Family Mansion in Qufu
• Lushan National Park
• Mount Emei Scenic Area, including Leshan Giant Buddha Scenic Area
• Ancient City of Ping Yao
• Classical Gardens of Suzhou
• Old Town of Lijiang
• Summer Palace, an Imperial Garden in Beijing
• Temple of Heaven: an Imperial Sacrificial Altar in Beijing
• Dazu Rock Carvings
• Ancient Villages in Southern Anhui – Xidi and Hongcun
• Imperial Tombs of the Ming and Qing Dynasties
• Longmen Grottoes
• Mount Qingcheng and the Dujiangyan Irrigation System
• Yungang Grottoes
• Three Parallel Rivers of Yunnan Protected Areas
• Capital Cities and Tombs of the Ancient Koguryo Kingdom
• Historic Centre of Macao
• Sichuan Giant Panda Sanctuaries - Wolong, Mt Siguniang and Jiajin Mountains
• Kaiping Diaolou and Villages
• South China Karst
• Fujian Tulou
• Mount Sanqingshan National Park
• Mount Wutai
• China Danxia
• Historic Monuments of Dengfeng in “The Centre of Heaven and Earth”
• West Lake Cultural Landscape of Hangzhou
• Chengjiang Fossil Site
• Site of Xanadu
• Cultural Landscape of Honghe Hani Rice Terraces
• Xinjiang Tianshan
• Silk Roads: the Routes Network of Chang'an-Tianshan Corridor *
• The Grand Canal
• Hubei Shennongjia
• Zuojiang Huashan Rock Art Cultural Landscape
• Kulangsu, a Historic International Settlement
• Qinghai Hoh Xil
• Fanjingshan
• Archaeological Ruins of Liangzhu City
• Migratory Bird Sanctuaries along the Coast of Yellow Sea-Bohai Gulf of China (Phase I)
• Quanzhou: Emporium of the World in Song-Yuan China
• Cultural Landscape of Old Tea Forests of the Jingmai Mountain in Pu’er
• Port, Fortresses and Group of Monuments, Cartagena
• Los Katíos National Park
• Historic Centre of Santa Cruz de Mompox
• National Archeological Park of Tierradentro
• San Agustín Archaeological Park
• Malpelo Fauna and Flora Sanctuary
• Coffee Cultural Landscape of Colombia
• Chiribiquete National Park – “The Maloca of the Jaguar”
• Forest Massif of Odzala-Kokoua
• Talamanca Range-La Amistad Reserves / La Amistad National Park *
• Cocos Island National Park
• Area de Conservación Guanacaste
• Precolumbian Chiefdom Settlements with Stone Spheres of the Diquís

Côte d'Ivoire

• Mount Nimba Strict Nature Reserve *
• Taï National Park
• Comoé National Park
• Historic Town of Grand-Bassam
• Sudanese style mosques in northern Côte d’Ivoire
• Historical Complex of Split with the Palace of Diocletian
• Old City of Dubrovnik
• Plitvice Lakes National Park #
• Episcopal Complex of the Euphrasian Basilica in the Historic Centre of Poreč
• Historic City of Trogir
• The Cathedral of St James in Šibenik
• Stari Grad Plain
• Venetian Works of Defence between the 16th and 17th Centuries: Stato da Terra – Western Stato da Mar *
• Old Havana and its Fortification System
• Trinidad and the Valley de los Ingenios
• San Pedro de la Roca Castle, Santiago de Cuba
• Desembarco del Granma National Park
• Viñales Valley
• Archaeological Landscape of the First Coffee Plantations in the South-East of Cuba
• Alejandro de Humboldt National Park
• Urban Historic Centre of Cienfuegos
• Historic Centre of Camagüey
• Painted Churches in the Troodos Region
• Choirokoitia
• Historic Centre of Český Krumlov
• Historic Centre of Prague
• Historic Centre of Telč
• Pilgrimage Church of St John of Nepomuk at Zelená Hora
• Kutná Hora: Historical Town Centre with the Church of St Barbara and the Cathedral of Our Lady at Sedlec
• Lednice-Valtice Cultural Landscape
• Gardens and Castle at Kroměříž
• Holašovice Historic Village
• Litomyšl Castle
• Holy Trinity Column in Olomouc
• Tugendhat Villa in Brno
• Jewish Quarter and St Procopius' Basilica in Třebíč
• Erzgebirge/Krušnohoří Mining Region *
• Landscape for Breeding and Training of Ceremonial Carriage Horses at Kladruby nad Labem
• Žatec and the Landscape of Saaz Hops

Democratic People's Republic of Korea

• Complex of Koguryo Tombs
• Historic Monuments and Sites in Kaesong

Democratic Republic of the Congo

• Virunga National Park #
• Kahuzi-Biega National Park
• Garamba National Park
• Salonga National Park
• Okapi Wildlife Reserve
• Jelling Mounds, Runic Stones and Church
• Roskilde Cathedral
• Kronborg Castle
• Ilulissat Icefjord
• Wadden Sea *
• Stevns Klint
• Christiansfeld, a Moravian Church Settlement
• The par force hunting landscape in North Zealand
• Kujataa Greenland: Norse and Inuit Farming at the Edge of the Ice Cap
• Aasivissuit – Nipisat. Inuit Hunting Ground between Ice and Sea
• Viking-Age Ring Fortresses
• Morne Trois Pitons National Park

Dominican Republic

• Colonial City of Santo Domingo
• City of Quito
• Galápagos Islands
• Sangay National Park #
• Historic Centre of Santa Ana de los Ríos de Cuenca
• Ancient Thebes with its Necropolis
• Historic Cairo
• Memphis and its Necropolis – the Pyramid Fields from Giza to Dahshur
• Nubian Monuments from Abu Simbel to Philae
• Saint Catherine Area
• Wadi Al-Hitan (Whale Valley)

El Salvador

• Joya de Cerén Archaeological Site
• Asmara: A Modernist African City
• Historic Centre (Old Town) of Tallinn
• Rock-Hewn Churches, Lalibela
• Simien National Park
• Fasil Ghebbi, Gondar Region
• Lower Valley of the Awash
• Lower Valley of the Omo
• Harar Jugol, the Fortified Historic Town
• Konso Cultural Landscape
• Bale Mountains National Park
• The Gedeo Cultural Landscape
• Levuka Historical Port Town
• Fortress of Suomenlinna
• Petäjävesi Old Church
• Verla Groundwood and Board Mill
• Bronze Age Burial Site of Sammallahdenmäki
• High Coast / Kvarken Archipelago *
• Chartres Cathedral
• Mont-Saint-Michel and its Bay
• Palace and Park of Versailles
• Prehistoric Sites and Decorated Caves of the Vézère Valley
• Vézelay, Church and Hill
• Amiens Cathedral
• Arles, Roman and Romanesque Monuments
• Cistercian Abbey of Fontenay
• Palace and Park of Fontainebleau
• Roman Theatre and its Surroundings and the "Triumphal Arch" of Orange
• From the Great Saltworks of Salins-les-Bains to the Royal Saltworks of Arc-et-Senans, the Production of Open-pan Salt
• Abbey Church of Saint-Savin sur Gartempe
• Gulf of Porto: Calanche of Piana, Gulf of Girolata, Scandola Reserve #
• Place Stanislas, Place de la Carrière and Place d'Alliance in Nancy
• Pont du Gard (Roman Aqueduct)
• Strasbourg, Grande-Île and Neustadt
• Cathedral of Notre-Dame, Former Abbey of Saint-Rémi and Palace of Tau, Reims
• Paris, Banks of the Seine
• Bourges Cathedral
• Historic Centre of Avignon: Papal Palace, Episcopal Ensemble and Avignon Bridge
• Canal du Midi
• Historic Fortified City of Carcassonne
• Pyrénées - Mont Perdu *
• Historic Site of Lyon
• Routes of Santiago de Compostela in France
• Belfries of Belgium and France * 9
• Jurisdiction of Saint-Emilion
• The Loire Valley between Sully-sur-Loire and Chalonnes 10
• Provins, Town of Medieval Fairs
• Le Havre, the City Rebuilt by Auguste Perret
• Bordeaux, Port of the Moon
• Fortifications of Vauban
• Lagoons of New Caledonia: Reef Diversity and Associated Ecosystems
• Episcopal City of Albi
• Pitons, cirques and remparts of Reunion Island
• The Causses and the Cévennes, Mediterranean agro-pastoral Cultural Landscape
• Nord-Pas de Calais Mining Basin
• Decorated Cave of Pont d’Arc, known as Grotte Chauvet-Pont d’Arc, Ardèche
• Champagne Hillsides, Houses and Cellars
• The Climats, terroirs of Burgundy
• Taputapuātea
• Chaîne des Puys - Limagne fault tectonic arena
• French Austral Lands and Seas
• Cordouan Lighthouse
• Nice, Winter Resort Town of the Riviera
• The Maison Carrée of Nîmes
• Volcanoes and Forests of Mount Pelée and the Pitons of Northern Martinique
• Ecosystem and Relict Cultural Landscape of Lopé-Okanda
• Ivindo National Park
• Kunta Kinteh Island and Related Sites
• Stone Circles of Senegambia *
• Gelati Monastery
• Historical Monuments of Mtskheta
• Upper Svaneti
• Colchic Rainforests and Wetlands
• Aachen Cathedral
• Speyer Cathedral
• Würzburg Residence with the Court Gardens and Residence Square
• Pilgrimage Church of Wies
• Castles of Augustusburg and Falkenlust at Brühl
• St Mary's Cathedral and St Michael's Church at Hildesheim
• Roman Monuments, Cathedral of St Peter and Church of Our Lady in Trier
• Frontiers of the Roman Empire * 11
• Hanseatic City of Lübeck
• Palaces and Parks of Potsdam and Berlin
• Abbey and Altenmünster of Lorsch
• Mines of Rammelsberg, Historic Town of Goslar and Upper Harz Water Management System #
• Maulbronn Monastery Complex
• Town of Bamberg
• Collegiate Church, Castle and Old Town of Quedlinburg
• Völklingen Ironworks
• Messel Pit Fossil Site
• Bauhaus and its Sites in Weimar, Dessau and Bernau
• Cologne Cathedral
• Luther Memorials in Eisleben and Wittenberg
• Classical Weimar
• Museumsinsel (Museum Island), Berlin
• Wartburg Castle
• Garden Kingdom of Dessau-Wörlitz
• Monastic Island of Reichenau
• Zollverein Coal Mine Industrial Complex in Essen
• Historic Centres of Stralsund and Wismar
• Upper Middle Rhine Valley
• Dresden Elbe Valley Delisted 2009
• Muskauer Park / Park Mużakowski *
• Town Hall and Roland on the Marketplace of Bremen
• Old town of Regensburg with Stadtamhof
• Berlin Modernism Housing Estates
• Fagus Factory in Alfeld
• Margravial Opera House Bayreuth
• Bergpark Wilhelmshöhe
• Carolingian Westwork and Civitas Corvey
• Speicherstadt and Kontorhaus District with Chilehaus
• Caves and Ice Age Art in the Swabian Jura
• Archaeological Border complex of Hedeby and the Danevirke
• Naumburg Cathedral
• Water Management System of Augsburg
• Frontiers of the Roman Empire – The Lower German Limes *
• Mathildenhöhe Darmstadt
• ShUM Sites of Speyer, Worms and Mainz
• Jewish-Medieval Heritage of Erfurt
• Forts and Castles, Volta, Greater Accra, Central and Western Regions
• Asante Traditional Buildings
• Temple of Apollo Epicurius at Bassae
• Acropolis, Athens
• Archaeological Site of Delphi
• Medieval City of Rhodes
• Mount Athos
• Paleochristian and Byzantine Monuments of Thessalonika
• Sanctuary of Asklepios at Epidaurus
• Archaeological Site of Mystras
• Archaeological Site of Olympia
• Monasteries of Daphni, Hosios Loukas and Nea Moni of Chios
• Pythagoreion and Heraion of Samos
• Archaeological Site of Aigai (modern name Vergina)
• Archaeological Sites of Mycenae and Tiryns
• The Historic Centre (Chorá) with the Monastery of Saint-John the Theologian and the Cave of the Apocalypse on the Island of Pátmos
• Old Town of Corfu
• Archaeological Site of Philippi
• Zagori Cultural Landscape
• Antigua Guatemala
• Tikal National Park
• Archaeological Park and Ruins of Quirigua
• National Archaeological Park Tak’alik Ab’aj
• National History Park – Citadel, Sans Souci, Ramiers
• Historic Centre of Rome, the Properties of the Holy See in that City Enjoying Extraterritorial Rights and San Paolo Fuori le Mura * 12
• Vatican City
• Maya Site of Copan
• Río Plátano Biosphere Reserve
• Budapest, including the Banks of the Danube, the Buda Castle Quarter and Andrássy Avenue
• Old Village of Hollókő and its Surroundings
• Caves of Aggtelek Karst and Slovak Karst *
• Millenary Benedictine Abbey of Pannonhalma and its Natural Environment
• Hortobágy National Park - the Puszta
• Early Christian Necropolis of Pécs (Sopianae)
• Tokaj Wine Region Historic Cultural Landscape
• Þingvellir National Park
• Vatnajökull National Park - Dynamic Nature of Fire and Ice
• Ajanta Caves
• Ellora Caves
• Group of Monuments at Mahabalipuram
• Sun Temple, Konârak
• Kaziranga National Park
• Keoladeo National Park
• Manas Wildlife Sanctuary
• Churches and Convents of Goa
• Fatehpur Sikri
• Group of Monuments at Hampi
• Khajuraho Group of Monuments
• Elephanta Caves
• Great Living Chola Temples 13
• Group of Monuments at Pattadakal
• Sundarbans National Park
• Nanda Devi and Valley of Flowers National Parks
• Buddhist Monuments at Sanchi
• Humayun's Tomb, Delhi
• Qutb Minar and its Monuments, Delhi
• Mountain Railways of India
• Mahabodhi Temple Complex at Bodh Gaya
• Rock Shelters of Bhimbetka
• Champaner-Pavagadh Archaeological Park
• Chhatrapati Shivaji Terminus (formerly Victoria Terminus)
• Red Fort Complex
• The Jantar Mantar, Jaipur
• Western Ghats
• Hill Forts of Rajasthan
• Great Himalayan National Park Conservation Area
• Rani-ki-Vav (the Queen’s Stepwell) at Patan, Gujarat
• Archaeological Site of Nalanda Mahavihara at Nalanda, Bihar
• Khangchendzonga National Park
• Historic City of Ahmadabad
• Victorian Gothic and Art Deco Ensembles of Mumbai
• Jaipur City, Rajasthan
• Dholavira: a Harappan City
• Kakatiya Rudreshwara (Ramappa) Temple, Telangana
• Sacred Ensembles of the Hoysalas
• Santiniketan
• Borobudur Temple Compounds
• Komodo National Park
• Prambanan Temple Compounds
• Ujung Kulon National Park
• Sangiran Early Man Site
• Lorentz National Park
• Tropical Rainforest Heritage of Sumatra
• Cultural Landscape of Bali Province: the Subak System as a Manifestation of the Tri Hita Karana Philosophy
• Ombilin Coal Mining Heritage of Sawahlunto
• The Cosmological Axis of Yogyakarta and its Historic Landmarks

Iran (Islamic Republic of)

• Meidan Emam, Esfahan
• Tchogha Zanbil
• Takht-e Soleyman
• Bam and its Cultural Landscape
• Armenian Monastic Ensembles of Iran
• Shushtar Historical Hydraulic System
• Sheikh Safi al-din Khānegāh and Shrine Ensemble in Ardabil
• Tabriz Historic Bazaar Complex
• The Persian Garden
• Gonbad-e Qābus
• Masjed-e Jāmé of Isfahan
• Golestan Palace
• Shahr-i Sokhta
• Cultural Landscape of Maymand
• The Persian Qanat
• Historic City of Yazd
• Sassanid Archaeological Landscape of Fars Region
• Cultural Landscape of Hawraman/Uramanat
• Trans-Iranian Railway
• The Persian Caravanserai
• Ashur (Qal'at Sherqat)
• Samarra Archaeological City
• Erbil Citadel
• The Ahwar of Southern Iraq: Refuge of Biodiversity and the Relict Landscape of the Mesopotamian Cities
• Brú na Bóinne - Archaeological Ensemble of the Bend of the Boyne
• Sceilg Mhichíl
• Old City of Acre
• White City of Tel-Aviv – the Modern Movement
• Biblical Tels - Megiddo, Hazor, Beer Sheba
• Incense Route - Desert Cities in the Negev
• Bahá’i Holy Places in Haifa and the Western Galilee
• Sites of Human Evolution at Mount Carmel: The Nahal Me’arot / Wadi el-Mughara Caves
• Caves of Maresha and Bet-Guvrin in the Judean Lowlands as a Microcosm of the Land of the Caves
• Necropolis of Bet She’arim: A Landmark of Jewish Renewal
• Rock Drawings in Valcamonica
• Church and Dominican Convent of Santa Maria delle Grazie with “The Last Supper” by Leonardo da Vinci
• Historic Centre of Rome, the Properties of the Holy See in that City Enjoying Extraterritorial Rights and San Paolo Fuori le Mura * 14
• Historic Centre of Florence
• Piazza del Duomo, Pisa
• Venice and its Lagoon
• Historic Centre of San Gimignano
• The Sassi and the Park of the Rupestrian Churches of Matera
• City of Vicenza and the Palladian Villas of the Veneto
• Crespi d'Adda
• Ferrara, City of the Renaissance, and its Po Delta 15
• Historic Centre of Naples
• Historic Centre of Siena
• Castel del Monte
• Early Christian Monuments of Ravenna
• Historic Centre of the City of Pienza
• The Trulli of Alberobello
• 18th-Century Royal Palace at Caserta with the Park, the Aqueduct of Vanvitelli, and the San Leucio Complex
• Archaeological Area of Agrigento
• Archaeological Areas of Pompei, Herculaneum and Torre Annunziata
• Botanical Garden (Orto Botanico), Padua
• Cathedral, Torre Civica and Piazza Grande, Modena
• Costiera Amalfitana
• Portovenere, Cinque Terre, and the Islands (Palmaria, Tino and Tinetto)
• Residences of the Royal House of Savoy
• Su Nuraxi di Barumini
• Villa Romana del Casale
• Archaeological Area and the Patriarchal Basilica of Aquileia
• Cilento and Vallo di Diano National Park with the Archeological Sites of Paestum and Velia, and the Certosa di Padula
• Historic Centre of Urbino
• Villa Adriana (Tivoli)
• Assisi, the Basilica of San Francesco and Other Franciscan Sites
• City of Verona
• Isole Eolie (Aeolian Islands)
• Villa d'Este, Tivoli
• Late Baroque Towns of the Val di Noto (South-Eastern Sicily)
• Sacri Monti of Piedmont and Lombardy
• Monte San Giorgio *
• Etruscan Necropolises of Cerveteri and Tarquinia
• Val d'Orcia
• Syracuse and the Rocky Necropolis of Pantalica
• Genoa: Le Strade Nuove and the system of the Palazzi dei Rolli
• Mantua and Sabbioneta
• Rhaetian Railway in the Albula / Bernina Landscapes *
• The Dolomites
• Longobards in Italy. Places of the Power (568-774 A.D.)
• Medici Villas and Gardens in Tuscany
• Vineyard Landscape of Piedmont: Langhe-Roero and Monferrato
• Arab-Norman Palermo and the Cathedral Churches of Cefalú and Monreale
• Ivrea, industrial city of the 20th century
• Le Colline del Prosecco di Conegliano e Valdobbiadene
• Padua’s fourteenth-century fresco cycles
• The Porticoes of Bologna
• Evaporitic Karst and Caves of Northern Apennines
• Blue and John Crow Mountains
• Buddhist Monuments in the Horyu-ji Area
• Shirakami-Sanchi
• Historic Monuments of Ancient Kyoto (Kyoto, Uji and Otsu Cities)
• Historic Villages of Shirakawa-go and Gokayama
• Hiroshima Peace Memorial (Genbaku Dome)
• Itsukushima Shinto Shrine
• Historic Monuments of Ancient Nara
• Shrines and Temples of Nikko
• Gusuku Sites and Related Properties of the Kingdom of Ryukyu
• Sacred Sites and Pilgrimage Routes in the Kii Mountain Range
• Iwami Ginzan Silver Mine and its Cultural Landscape
• Hiraizumi – Temples, Gardens and Archaeological Sites Representing the Buddhist Pure Land
• Ogasawara Islands
• Fujisan, sacred place and source of artistic inspiration
• Tomioka Silk Mill and Related Sites
• Sites of Japan’s Meiji Industrial Revolution: Iron and Steel, Shipbuilding and Coal Mining
• Sacred Island of Okinoshima and Associated Sites in the Munakata Region
• Hidden Christian Sites in the Nagasaki Region
• Mozu-Furuichi Kofun Group: Mounded Tombs of Ancient Japan
• Amami-Oshima Island, Tokunoshima Island, Northern part of Okinawa Island, and Iriomote Island
• Jomon Prehistoric Sites in Northern Japan

Jerusalem (Site proposed by Jordan)

• Old City of Jerusalem and its Walls
• Quseir Amra
• Um er-Rasas (Kastrom Mefa'a)
• Wadi Rum Protected Area
• Baptism Site “Bethany Beyond the Jordan” (Al-Maghtas)
• As-Salt - The Place of Tolerance and Urban Hospitality
• Mausoleum of Khoja Ahmed Yasawi
• Petroglyphs of the Archaeological Landscape of Tanbaly
• Saryarka – Steppe and Lakes of Northern Kazakhstan
• Western Tien-Shan *
• Cold Winter Deserts of Turan *
• Lake Turkana National Parks
• Mount Kenya National Park/Natural Forest
• Lamu Old Town
• Sacred Mijikenda Kaya Forests
• Fort Jesus, Mombasa
• Kenya Lake System in the Great Rift Valley
• Thimlich Ohinga Archaeological Site
• Phoenix Islands Protected Area
• Sulaiman-Too Sacred Mountain

Lao People's Democratic Republic

• Town of Luang Prabang
• Vat Phou and Associated Ancient Settlements within the Champasak Cultural Landscape
• Megalithic Jar Sites in Xiengkhuang – Plain of Jars
• Historic Centre of Riga
• Old town of Kuldīga
• Ouadi Qadisha (the Holy Valley) and the Forest of the Cedars of God (Horsh Arz el-Rab)
• Rachid Karami International Fair-Tripoli
• Maloti-Drakensberg Park *
• Archaeological Site of Cyrene
• Archaeological Site of Leptis Magna
• Archaeological Site of Sabratha
• Rock-Art Sites of Tadrart Acacus
• Old Town of Ghadamès
• Vilnius Historic Centre
• Curonian Spit *
• Kernavė Archaeological Site (Cultural Reserve of Kernavė)
• Modernist Kaunas: Architecture of Optimism, 1919-1939
• City of Luxembourg: its Old Quarters and Fortifications
• Andrefana Dry Forests
• Royal Hill of Ambohimanga
• Rainforests of the Atsinanana
• Lake Malawi National Park
• Chongoni Rock-Art Area
• Gunung Mulu National Park
• Kinabalu Park
• Melaka and George Town, Historic Cities of the Straits of Malacca
• Archaeological Heritage of the Lenggong Valley
• Old Towns of Djenné
• Cliff of Bandiagara (Land of the Dogons)
• Tomb of Askia
• City of Valletta
• Ħal Saflieni Hypogeum
• Megalithic Temples of Malta 16

Marshall Islands

• Bikini Atoll Nuclear Test Site
• Banc d'Arguin National Park
• Ancient Ksour of Ouadane, Chinguetti, Tichitt and Oualata
• Aapravasi Ghat
• Le Morne Cultural Landscape
• Historic Centre of Mexico City and Xochimilco
• Historic Centre of Oaxaca and Archaeological Site of Monte Albán
• Historic Centre of Puebla
• Pre-Hispanic City and National Park of Palenque
• Pre-Hispanic City of Teotihuacan
• Historic Town of Guanajuato and Adjacent Mines
• Pre-Hispanic City of Chichen-Itza
• Historic Centre of Morelia
• El Tajin, Pre-Hispanic City
• Historic Centre of Zacatecas
• Rock Paintings of the Sierra de San Francisco
• Whale Sanctuary of El Vizcaino
• Earliest 16th-Century Monasteries on the Slopes of Popocatepetl
• Historic Monuments Zone of Querétaro
• Pre-Hispanic Town of Uxmal
• Hospicio Cabañas, Guadalajara
• Archaeological Zone of Paquimé, Casas Grandes
• Historic Monuments Zone of Tlacotalpan
• Archaeological Monuments Zone of Xochicalco
• Historic Fortified Town of Campeche
• Ancient Maya City and Protected Tropical Forests of Calakmul, Campeche
• Franciscan Missions in the Sierra Gorda of Querétaro
• Luis Barragán House and Studio
• Islands and Protected Areas of the Gulf of California
• Agave Landscape and Ancient Industrial Facilities of Tequila
• Central University City Campus of the Universidad Nacional Autónoma de México (UNAM)
• Monarch Butterfly Biosphere Reserve
• Protective town of San Miguel and the Sanctuary of Jesús Nazareno de Atotonilco
• Camino Real de Tierra Adentro
• Prehistoric Caves of Yagul and Mitla in the Central Valley of Oaxaca
• El Pinacate and Gran Desierto de Altar Biosphere Reserve
• Aqueduct of Padre Tembleque Hydraulic System
• Archipiélago de Revillagigedo
• Tehuacán-Cuicatlán Valley: originary habitat of Mesoamerica

Micronesia (Federated States of)

• Nan Madol: Ceremonial Centre of Eastern Micronesia
• Uvs Nuur Basin *
• Orkhon Valley Cultural Landscape
• Petroglyphic Complexes of the Mongolian Altai
• Great Burkhan Khaldun Mountain and its surrounding sacred landscape
• Landscapes of Dauria *
• Deer Stone Monuments and Related Bronze Age Sites
• Natural and Culturo-Historical Region of Kotor
• Durmitor National Park
• Medina of Fez
• Medina of Marrakesh
• Ksar of Ait-Ben-Haddou
• Historic City of Meknes
• Archaeological Site of Volubilis
• Medina of Tétouan (formerly known as Titawin)
• Medina of Essaouira (formerly Mogador)
• Portuguese City of Mazagan (El Jadida)
• Rabat, Modern Capital and Historic City: a Shared Heritage
• Island of Mozambique
• Pyu Ancient Cities
• Twyfelfontein or /Ui-//aes
• Namib Sand Sea
• Kathmandu Valley
• Sagarmatha National Park
• Chitwan National Park
• Lumbini, the Birthplace of the Lord Buddha

Netherlands (Kingdom of the)

• Schokland and Surroundings
• Dutch Water Defence Lines
• Historic Area of Willemstad, Inner City and Harbour, Curaçao
• Mill Network at Kinderdijk-Elshout
• Ir.D.F. Woudagemaal (D.F. Wouda Steam Pumping Station)
• Droogmakerij de Beemster (Beemster Polder)
• Rietveld Schröderhuis (Rietveld Schröder House)
• Seventeenth-Century Canal Ring Area of Amsterdam inside the Singelgracht
• Van Nellefabriek
• Eisinga Planetarium in Franeker

New Zealand

• Te Wahipounamu – South West New Zealand 17
• Tongariro National Park #
• New Zealand Sub-Antarctic Islands
• Ruins of León Viejo
• León Cathedral
• Air and Ténéré Natural Reserves
• Historic Centre of Agadez
• Sukur Cultural Landscape
• Osun-Osogbo Sacred Grove

North Macedonia

• Natural and Cultural Heritage of the Ohrid region * 18
• Urnes Stave Church
• Røros Mining Town and the Circumference
• Rock Art of Alta
• Vegaøyan – The Vega Archipelago
• West Norwegian Fjords – Geirangerfjord and Nærøyfjord
• Rjukan-Notodden Industrial Heritage Site
• Archaeological Sites of Bat, Al-Khutm and Al-Ayn
• Arabian Oryx Sanctuary Delisted 2007
• Land of Frankincense
• Aflaj Irrigation Systems of Oman
• Ancient City of Qalhat
• Archaeological Ruins at Moenjodaro
• Buddhist Ruins of Takht-i-Bahi and Neighbouring City Remains at Sahr-i-Bahlol
• Fort and Shalamar Gardens in Lahore
• Historical Monuments at Makli, Thatta
• Rohtas Fort
• Rock Islands Southern Lagoon
• Fortifications on the Caribbean Side of Panama: Portobelo-San Lorenzo
• Darien National Park
• Archaeological Site of Panamá Viejo and Historic District of Panamá
• Coiba National Park and its Special Zone of Marine Protection

Papua New Guinea

• Kuk Early Agricultural Site
• Jesuit Missions of La Santísima Trinidad de Paraná and Jesús de Tavarangue
• City of Cuzco
• Historic Sanctuary of Machu Picchu
• Chavin (Archaeological Site)
• Huascarán National Park #
• Chan Chan Archaeological Zone
• Manú National Park
• Historic Centre of Lima 19
• Río Abiseo National Park
• Lines and Geoglyphs of Nasca and Palpa
• Historical Centre of the City of Arequipa
• Sacred City of Caral-Supe
• Chankillo Archaeoastronomical Complex

Philippines

• Baroque Churches of the Philippines
• Tubbataha Reefs Natural Park
• Rice Terraces of the Philippine Cordilleras
• Historic City of Vigan
• Puerto-Princesa Subterranean River National Park
• Mount Hamiguitan Range Wildlife Sanctuary
• Historic Centre of Kraków
• Wieliczka and Bochnia Royal Salt Mines
• Auschwitz Birkenau German Nazi Concentration and Extermination Camp (1940-1945)
• Historic Centre of Warsaw
• Old City of Zamość
• Castle of the Teutonic Order in Malbork
• Medieval Town of Toruń
• Kalwaria Zebrzydowska: the Mannerist Architectural and Park Landscape Complex and Pilgrimage Park
• Churches of Peace in Jawor and Świdnica
• Wooden Churches of Southern Małopolska
• Centennial Hall in Wrocław
• Wooden Tserkvas of the Carpathian Region in Poland and Ukraine *
• Tarnowskie Góry Lead-Silver-Zinc Mine and its Underground Water Management System
• Krzemionki Prehistoric Striped Flint Mining Region
• Central Zone of the Town of Angra do Heroismo in the Azores
• Convent of Christ in Tomar
• Monastery of Batalha
• Monastery of the Hieronymites and Tower of Belém in Lisbon
• Historic Centre of Évora
• Monastery of Alcobaça
• Cultural Landscape of Sintra
• Historic Centre of Oporto, Luiz I Bridge and Monastery of Serra do Pilar
• Prehistoric Rock Art Sites in the Côa Valley and Siega Verde * 20
• Laurisilva of Madeira
• Alto Douro Wine Region
• Historic Centre of Guimarães and Couros Zone
• Landscape of the Pico Island Vineyard Culture
• Garrison Border Town of Elvas and its Fortifications
• University of Coimbra – Alta and Sofia
• Royal Building of Mafra – Palace, Basilica, Convent, Cerco Garden and Hunting Park ( Tapada )
• Sanctuary of Bom Jesus do Monte in Braga
• Al Zubarah Archaeological Site

Republic of Korea

• Haeinsa Temple Janggyeong Panjeon, the Depositories for the Tripitaka Koreana Woodblocks
• Jongmyo Shrine
• Seokguram Grotto and Bulguksa Temple
• Changdeokgung Palace Complex
• Hwaseong Fortress
• Gochang, Hwasun and Ganghwa Dolmen Sites
• Gyeongju Historic Areas
• Jeju Volcanic Island and Lava Tubes
• Royal Tombs of the Joseon Dynasty
• Historic Villages of Korea: Hahoe and Yangdong
• Namhansanseong
• Baekje Historic Areas
• Sansa, Buddhist Mountain Monasteries in Korea
• Seowon, Korean Neo-Confucian Academies
• Getbol, Korean Tidal Flats
• Gaya Tumuli

Republic of Moldova

• Danube Delta
• Churches of Moldavia
• Monastery of Horezu
• Villages with Fortified Churches in Transylvania 21
• Dacian Fortresses of the Orastie Mountains
• Historic Centre of Sighişoara
• Wooden Churches of Maramureş
• Roșia Montană Mining Landscape

Russian Federation

• Historic Centre of Saint Petersburg and Related Groups of Monuments
• Kizhi Pogost
• Kremlin and Red Square, Moscow
• Cultural and Historic Ensemble of the Solovetsky Islands
• Historic Monuments of Novgorod and Surroundings
• White Monuments of Vladimir and Suzdal
• Architectural Ensemble of the Trinity Sergius Lavra in Sergiev Posad
• Church of the Ascension, Kolomenskoye
• Virgin Komi Forests
• Lake Baikal
• Volcanoes of Kamchatka 22
• Golden Mountains of Altai
• Western Caucasus
• Ensemble of the Ferapontov Monastery
• Historic and Architectural Complex of the Kazan Kremlin
• Central Sikhote-Alin
• Citadel, Ancient City and Fortress Buildings of Derbent
• Ensemble of the Novodevichy Convent
• Natural System of Wrangel Island Reserve
• Historical Centre of the City of Yaroslavl
• Putorana Plateau
• Lena Pillars Nature Park
• Bolgar Historical and Archaeological Complex
• Assumption Cathedral and Monastery of the town-island of Sviyazhsk
• Churches of the Pskov School of Architecture
• Petroglyphs of Lake Onega and the White Sea
• Astronomical Observatories of Kazan Federal University
• Memorial sites of the Genocide: Nyamata, Murambi, Gisozi and Bisesero
• Nyungwe National Park

Saint Kitts and Nevis

• Brimstone Hill Fortress National Park

Saint Lucia

• Pitons Management Area
• San Marino Historic Centre and Mount Titano

Saudi Arabia

• Hegra Archaeological Site (al-Hijr / Madā ͐ in Ṣāliḥ)
• At-Turaif District in ad-Dir'iyah
• Historic Jeddah, the Gate to Makkah
• Rock Art in the Hail Region of Saudi Arabia
• Al-Ahsa Oasis, an Evolving Cultural Landscape
• Ḥimā Cultural Area
• ‘Uruq Bani Ma’arid
• Island of Gorée
• Niokolo-Koba National Park
• Djoudj National Bird Sanctuary
• Island of Saint-Louis
• Saloum Delta
• Bassari Country: Bassari, Fula and Bedik Cultural Landscapes
• Stari Ras and Sopoćani
• Studenica Monastery
• Medieval Monuments in Kosovo
• Gamzigrad-Romuliana, Palace of Galerius
• Aldabra Atoll
• Vallée de Mai Nature Reserve
• Singapore Botanic Gardens
• Historic Town of Banská Štiavnica and the Technical Monuments in its Vicinity
• Levoča, Spišský Hrad and the Associated Cultural Monuments
• Bardejov Town Conservation Reserve
• Wooden Churches of the Slovak part of the Carpathian Mountain Area
• Škocjan Caves #
• Heritage of Mercury. Almadén and Idrija *
• The works of Jože Plečnik in Ljubljana – Human Centred Urban Design

Solomon Islands

• East Rennell

South Africa

• Fossil Hominid Sites of South Africa
• iSimangaliso Wetland Park
• Robben Island
• Mapungubwe Cultural Landscape
• Cape Floral Region Protected Areas
• Vredefort Dome
• Richtersveld Cultural and Botanical Landscape
• ǂKhomani Cultural Landscape
• Barberton Makhonjwa Mountains
• Alhambra, Generalife and Albayzín, Granada 23
• Burgos Cathedral
• Historic Centre of Cordoba 24
• Monastery and Site of the Escurial, Madrid
• Works of Antoni Gaudí 25
• Cave of Altamira and Paleolithic Cave Art of Northern Spain
• Monuments of Oviedo and the Kingdom of the Asturias 26
• Old Town of Ávila with its Extra-Muros Churches
• Old Town of Segovia and its Aqueduct
• Santiago de Compostela (Old Town)
• Garajonay National Park
• Historic City of Toledo
• Mudejar Architecture of Aragon 27
• Old Town of Cáceres
• Cathedral, Alcázar and Archivo de Indias in Seville
• Old City of Salamanca
• Poblet Monastery
• Archaeological Ensemble of Mérida
• Routes of Santiago de Compostela: Camino Francés and Routes of Northern Spain
• Royal Monastery of Santa María de Guadalupe
• Doñana National Park
• Historic Walled Town of Cuenca
• La Lonja de la Seda de Valencia
• Las Médulas
• Palau de la Música Catalana and Hospital de Sant Pau, Barcelona
• San Millán Yuso and Suso Monasteries
• Prehistoric Rock Art Sites in the Côa Valley and Siega Verde * 28
• Rock Art of the Mediterranean Basin on the Iberian Peninsula
• University and Historic Precinct of Alcalá de Henares
• Ibiza, Biodiversity and Culture
• San Cristóbal de La Laguna
• Archaeological Ensemble of Tarraco
• Archaeological Site of Atapuerca
• Catalan Romanesque Churches of the Vall de Boí
• Palmeral of Elche
• Roman Walls of Lugo 29
• Aranjuez Cultural Landscape
• Renaissance Monumental Ensembles of Úbeda and Baeza
• Vizcaya Bridge
• Teide National Park
• Tower of Hercules
• Cultural Landscape of the Serra de Tramuntana
• Antequera Dolmens Site
• Caliphate City of Medina Azahara
• Risco Caido and the Sacred Mountains of Gran Canaria Cultural Landscape
• Paseo del Prado and Buen Retiro, a landscape of Arts and Sciences
• Prehistoric Sites of Talayotic Menorca
• Ancient City of Polonnaruwa
• Ancient City of Sigiriya
• Sacred City of Anuradhapura
• Old Town of Galle and its Fortifications
• Sacred City of Kandy
• Sinharaja Forest Reserve 30
• Rangiri Dambulla Cave Temple
• Central Highlands of Sri Lanka

State of Palestine

• Birthplace of Jesus: Church of the Nativity and the Pilgrimage Route, Bethlehem
• Palestine: Land of Olives and Vines – Cultural Landscape of Southern Jerusalem, Battir
• Hebron/Al-Khalil Old Town
• Ancient Jericho/Tell es-Sultan
• Gebel Barkal and the Sites of the Napatan Region
• Archaeological Sites of the Island of Meroe
• Sanganeb Marine National Park and Dungonab Bay – Mukkawar Island Marine National Park
• Central Suriname Nature Reserve
• Historic Inner City of Paramaribo
• Jodensavanne Archaeological Site: Jodensavanne Settlement and Cassipora Creek Cemetery
• Royal Domain of Drottningholm
• Birka and Hovgården
• Engelsberg Ironworks
• Rock Carvings in Tanum
• Skogskyrkogården
• Hanseatic Town of Visby
• Church Town of Gammelstad, Luleå
• Laponian Area
• Naval Port of Karlskrona
• Agricultural Landscape of Southern Öland
• Mining Area of the Great Copper Mountain in Falun
• Grimeton Radio Station, Varberg
• Decorated Farmhouses of Hälsingland

Switzerland

• Abbey of St Gall
• Benedictine Convent of St John at Müstair
• Old City of Berne
• Three Castles, Defensive Wall and Ramparts of the Market-Town of Bellinzona
• Swiss Alps Jungfrau-Aletsch
• Lavaux, Vineyard Terraces
• Swiss Tectonic Arena Sardona
• La Chaux-de-Fonds / Le Locle, Watchmaking Town Planning

Syrian Arab Republic

• Ancient City of Damascus
• Ancient City of Bosra
• Site of Palmyra
• Ancient City of Aleppo
• Crac des Chevaliers and Qal’at Salah El-Din
• Ancient Villages of Northern Syria
• Proto-urban Site of Sarazm
• Tajik National Park (Mountains of the Pamirs)
• Silk Roads: Zarafshan-Karakum Corridor *
• Tugay forests of the Tigrovaya Balka Nature Reserve
• Historic City of Ayutthaya
• Historic Town of Sukhothai and Associated Historic Towns
• Thungyai-Huai Kha Khaeng Wildlife Sanctuaries
• Ban Chiang Archaeological Site
• Dong Phayayen-Khao Yai Forest Complex
• Kaeng Krachan Forest Complex
• The Ancient Town of Si Thep and its Associated Dvaravati Monuments
• Amphitheatre of El Jem
• Archaeological Site of Carthage
• Medina of Tunis
• Ichkeul National Park
• Punic Town of Kerkuane and its Necropolis
• Medina of Sousse
• Dougga / Thugga
• Djerba: Testimony to a settlement pattern in an island territory
• Göreme National Park and the Rock Sites of Cappadocia
• Great Mosque and Hospital of Divriği
• Historic Areas of Istanbul
• Hattusha: the Hittite Capital
• Hierapolis-Pamukkale
• Xanthos-Letoon
• City of Safranbolu
• Archaeological Site of Troy
• Selimiye Mosque and its Social Complex
• Neolithic Site of Çatalhöyük
• Bursa and Cumalıkızık: the Birth of the Ottoman Empire
• Pergamon and its Multi-Layered Cultural Landscape
• Diyarbakır Fortress and Hevsel Gardens Cultural Landscape
• Archaeological Site of Ani
• Aphrodisias
• Göbekli Tepe
• Arslantepe Mound
• Wooden Hypostyle Mosques of Medieval Anatolia

Turkmenistan

• State Historical and Cultural Park “Ancient Merv”
• Kunya-Urgench
• Parthian Fortresses of Nisa
• Bwindi Impenetrable National Park
• Rwenzori Mountains National Park
• Tombs of Buganda Kings at Kasubi
• Kyiv: Saint-Sophia Cathedral and Related Monastic Buildings, Kyiv-Pechersk Lavra
• L'viv – the Ensemble of the Historic Centre
• Residence of Bukovinian and Dalmatian Metropolitans
• Ancient City of Tauric Chersonese and its Chora
• The Historic Centre of Odesa

United Arab Emirates

• Cultural Sites of Al Ain (Hafit, Hili, Bidaa Bint Saud and Oases Areas)

United Kingdom of Great Britain and Northern Ireland

• Castles and Town Walls of King Edward in Gwynedd
• Durham Castle and Cathedral
• Giant's Causeway and Causeway Coast
• Ironbridge Gorge
• Stonehenge, Avebury and Associated Sites
• Studley Royal Park including the Ruins of Fountains Abbey
• Blenheim Palace
• City of Bath
• Frontiers of the Roman Empire * 31
• Palace of Westminster and Westminster Abbey including Saint Margaret’s Church
• Canterbury Cathedral, St Augustine's Abbey, and St Martin's Church
• Henderson Island
• Tower of London
• Gough and Inaccessible Islands 32
• Old and New Towns of Edinburgh
• Maritime Greenwich
• Heart of Neolithic Orkney
• Blaenavon Industrial Landscape
• Historic Town of St George and Related Fortifications, Bermuda
• Derwent Valley Mills
• Dorset and East Devon Coast
• Royal Botanic Gardens, Kew
• Liverpool – Maritime Mercantile City Delisted 2021
• Cornwall and West Devon Mining Landscape
• Pontcysyllte Aqueduct and Canal
• The Forth Bridge
• Gorham's Cave Complex
• The English Lake District
• Jodrell Bank Observatory
• The Slate Landscape of Northwest Wales

United Republic of Tanzania

• Ngorongoro Conservation Area 33
• Ruins of Kilwa Kisiwani and Ruins of Songo Mnara
• Serengeti National Park
• Selous Game Reserve
• Kilimanjaro National Park
• Stone Town of Zanzibar
• Kondoa Rock-Art Sites

United States of America

• Mesa Verde National Park
• Yellowstone National Park
• Everglades National Park
• Grand Canyon National Park
• Independence Hall
• Kluane / Wrangell-St. Elias / Glacier Bay / Tatshenshini-Alsek # * 34
• Redwood National and State Parks
• Mammoth Cave National Park
• Olympic National Park
• Cahokia Mounds State Historic Site
• Great Smoky Mountains National Park
• La Fortaleza and San Juan National Historic Site in Puerto Rico
• Statue of Liberty
• Yosemite National Park #
• Chaco Culture
• Hawaii Volcanoes National Park #
• Monticello and the University of Virginia in Charlottesville
• Taos Pueblo
• Carlsbad Caverns National Park
• Papahānaumokuākea
• Monumental Earthworks of Poverty Point
• San Antonio Missions
• The 20th-Century Architecture of Frank Lloyd Wright
• Hopewell Ceremonial Earthworks
• Historic Quarter of the City of Colonia del Sacramento
• Fray Bentos Industrial Landscape
• The work of engineer Eladio Dieste: Church of Atlántida
• Itchan Kala
• Historic Centre of Bukhara
• Historic Centre of Shakhrisyabz
• Samarkand – Crossroad of Cultures
• Chief Roi Mata’s Domain

Venezuela (Bolivarian Republic of)

• Coro and its Port
• Canaima National Park
• Ciudad Universitaria de Caracas
• Complex of Hué Monuments
• Ha Long Bay - Cat Ba Archipelago
• Hoi An Ancient Town
• My Son Sanctuary
• Phong Nha-Ke Bang National Park
• Central Sector of the Imperial Citadel of Thang Long - Hanoi
• Citadel of the Ho Dynasty
• Trang An Landscape Complex
• Old Walled City of Shibam
• Old City of Sana'a
• Historic Town of Zabid
• Socotra Archipelago
• Landmarks of the Ancient Kingdom of Saba, Marib
• Mosi-oa-Tunya / Victoria Falls # *
• Mana Pools National Park, Sapi and Chewore Safari Areas
• Great Zimbabwe National Monument
• Khami Ruins National Monument
• Matobo Hills

In 1979, the Committee decided to inscribe the Ohrid Lake on the World Heritage List under natural criteria (iii). In 1980, this property was extended to include the cultural and historical area, and cultural criteria (i)(iii)(iv) were added.

Extension of the "Australian East Coast Temperate and Subtropical Rainforest Park".

name changed 2007 from 'Central Eastern Rainforest Reserves (Australia)'

Renomination of "Uluru-Kata Tjuta National Park" under cultural criteria.

The “Belfries of Flanders and Wallonia” which were previously inscribed on the World Heritage List, are part of the transnational property “The Belfries of Belgium and France”.

Extension of "Jaú National Park".

Extension of the "Glacier Bay/Wrangell/St Elias/Kluane" property.

The "Burgess Shale" property, which was previously inscribed on the World Heritage List, is part of the "Canadian Rocky Mountain Parks".

Extension of "The Potala Palace and the Jokhang Temple Monastery, Lhasa" to include the Norbulingka area.

The "Chateau and Estate of Chambord", which was previously inscribed on the World Heritage List, is part of the "Loire Valley between Sully-sur-Loire and Chalonnes".

The “Hadrian’s Wall” which was previously inscribed on the World Heritage List, is part of the transnational property “Frontiers of the Roman Empire”.

At the time the property was extended, cultural criterion (iv) was also found applicable.

The "Brihadisvara Temple, Tanjavur", which was previously inscribed on the World Heritage List, is part of the "Great Living Chola Temples".

At the time the property was extended, criteria (iii) and (v) were also found applicable.

The Committee decided to extend the existing cultural property, the "Temple of Ggantija", to include the five prehistoric temples situated on the islands of Malta and Gozo and to rename the property as "The Megalithic Temples of Malta".

The Westland and Mount Cook National Park and the Fiordland National Park, which were previously inscribed on the World Heritage List, are part of the "Te Wahipounamu - South West New Zealand".

The "Convent Ensemble of San Francisco de Lima", which was previously inscribed on the World Heritage List, is part of the "Historic Centre of Lima".

Extension de « Sites d'art rupestre préhistorique de la vallée de Côa », Portugal

Extension of "Biertan and its Fortified Church".

At the time the property was extended, natural criterion (iv) was also found applicable.

Extension of the "Alhambra and the Generalife, Granada", to include the Albayzin quarter.

Extension of the "Mosque of Cordoba".

The property “Parque Güell, Palacio Güell and Casa Mila in Barcelona”, previously inscribed on the World Heritage List, is part of the “Works of Antoni Gaudí”.

Extension of the "Churches of the Kingdom of the Asturias", to include monuments in the city of Oviedo.

Extension of the "Mudejar Architecture of Teruel".

Following a survey of ownership carried out in the late 1960s, ownership of the totality of the walls was vested in 1973 in the Spanish State, through the Ministry of Education and Science. It was transferred to the Xunta de Galicia by Royal Decree in 1994.

 The Spanish Constitution reserves certain rights in relation to the heritage to the central government. However, these are delegated to the competent agencies in the Autonomous Communities, in this case the Xunta de Galicia. For the Lugo walls the Xunta is in the position of both owner and competent agency. Under the Galician Heritage Law the Xunta is required to cooperate with the municipal authorities in ensuring the protection and conservation of listed monuments, and certain functions are delegated down to them. The Xunta operates through its General Directorate of Cultural Heritage (Dirección General de Patrimonio Cultural), based in Santiago de Compostela.

The Master Plan for the Conservation and Restoration of the Roman Walls of Lugo (1992) covered proposals for actions to be taken in respect of research and techniques of restoration. This was followed in 1997 by the Special Plan for the Protection and Internal Reform of the Fortified Enceinte of the Town of Lugo, which is concerned principally with the urban environment of the historic town. However, it has a direct impact on the protection afforded to the walls, in terms of traffic planning, the creation of open spaces, and regulation of building heights. Another planning instrument which affects the walls is the Special Plan for the Protection of the Miño [river], approved by the municipality at the beginning of 1998.

There is at the present time no management plan sensu stricto for the walls in operation in Lugo: work is continuing on the basis of the 1992 plan. Nor is there a technical unit specifically responsible for the conservation and restoration of the walls. It is against this background that serious consideration is being given to the creation of an independent foundation, under royal patronage and with representatives from government, academic, voluntary, and business institutions, to work with the General Directorate of Cultural Heritage of Galicia. The work plan of this body would include the development and implementation of integrated conservation, restoration, and maintenance programmes.

The WH area is managed directly by the Divisional Forest Officer from the Forest Dept. A national steering Committee co-ordinates institutions for Sinharaja as a National Wilderness Area, Biosphere Reserve (1988), and WH site. There are two management plans, prepared in 1985/86 and 1992/94, which emphasise conservation, scientific research, buffer zone management, benefit-sharing, and community participation.

Extension of "Gough Island Wildlife Reserve".

(renomination under cultural criteria)

*: transboundary property

# : As for 19 Natural and Mixed Properties inscribed for geological values before 1994, criteria numbering of this property has changed. See Decision 30.COM 8D.1

The Nomination files produced by the States Parties are published by the World Heritage Centre at its website and/or in working documents in order to ensure transparency, access to information and to facilitate the preparations of comparative analysis by other nominating States Parties.

The sole responsibility for the content of each Nomination file lies with the State Party concerned. The publication of the Nomination file, including the maps and names, does not imply the expression of any opinion whatsoever of the World Heritage Committee or of the Secretariat of UNESCO concerning the history or legal status of any country, territory, city or area or of its boundaries.

World Heritage Online Map Platform

The World Heritage Online Map Platform, supported by the Flanders UNESCO Trustfund (FUT), is a pilot online geographic information system displaying georeferenced maps of World Heritage properties and buffer zones.

Official World Heritage List in other formats

Official World Heritage List Statistics

Order World Heritage List Wall Map

A large format full-colour map is available in English, French and Spanish . The dimensions of the map are 78cm by 50cm (31 in. by 20 in.).

• Share full article

The World Through a Lens

A Photographer’s View of Jordan’s Many Splendors

For 10 days, a photojournalist drove across Jordan from north to south, visiting several of the country’s most treasured sites. Here’s what he saw.

Petra’s Ad Deir, or the Monastery, at sunset. Credit...

Supported by

Photographs and Text by Daniel Rodrigues

• Published Feb. 7, 2022 Updated April 3, 2022

In September 2021, after more than two years without traveling, my girlfriend and I decided to take a trip to Jordan — mainly to see the ancient city of Petra.

For 10 days we traveled through the country from north to south in a rental car, tallying around 760 miles. Our itinerary took us along nearly the entire length of Highway 35, also called the King’s Highway, which stretches from the northern city of Irbid to a point some 25 miles north of Wadi Rum, the famed desert valley to the south.

Along the way, we visited many of Jordan’s most treasured tourist destinations: the city of Jerash, with its stunning Greco-Roman ruins; Amman, the capital, with its cosmopolitan rhythms; the market town of Madaba, with its renowned Byzantine-era mosaics; the Dana Biosphere Reserve , with its rich diversity of plant life.

Our road trip started near the Dead Sea, though our stay there was relatively short. The environment near the surface — which sits more than 1,400 feet below sea level — is arid and suffocating. The water itself is so salty as to feel caustic; a single drop near our eyes or lips sent us rushing to the shore to rinse our faces.

But it was Petra — stunning in its scale, dazzling in its grandeur — that captured our imaginations. Tucked away in the mountains between the Dead Sea and Aqaba, and just miles from Highway 35, the ancient city defies all expectations.

Its many temples, tombs and altars — including its best-known structure, the Treasury, or Al Khazneh — left us breathless. No matter how many photographs you may have seen, nothing can ever prepare you for the feeling of standing in front of those incredible structures.

Carved into the wall of a narrow canyon and reaching some 130 feet high, the Treasury is thought to have been built as a mausoleum some 2,000 years ago. Though undoubtedly Petra’s most famous structure, the Treasury is not its largest. Ad Deir, a monastery that reaches some 154 feet, claims that title.

Petra, which lay along important trade routes between the Middle East and northern Africa, was built by the Nabataeans, a Bedouin tribe who lived in the area between the seventh century B.C. and the second century A.D. It remained entirely unknown to Westerners until 1812, when Johann Ludwig Burckhardt, a Swiss traveler and geographer who had disguised himself as an Arab pilgrim, was led to the city by a local guide.

Throughout our trip, and especially while at Petra, we were reminded of how devastating the pandemic has been for those who work in the tourism industry.

According to data from the Petra Development and Tourism Region Authority, the ancient city received some 1.1 million visitors in 2019 — an average of more than 3,000 people per day. During our visit, there were no more than 40 tourists in the city. As pleasant as it was to share the site with so few fellow visitors, we felt great concern for the locals whose business has evaporated: tour operators, camel and donkey owners, artisans, souvenir sellers.

From Petra we traveled farther south, eventually making our way to the desert landscape of Wadi Rum, also known as the Valley of the Moon, whose spectacular scenery includes towering sand dunes, vast mesas and narrow canyons, all covered in rich shades of orange and red.

We chose to explore the area in a pickup truck whose bed had been outfitted with bench seats — a convenient way of coping with temperatures in excess of 100 degrees Fahrenheit.

We lingered in the desert until well past the sunset, when a vivid color palette emerged across the dunes.

And after a mythical journey along Highway 35, we drove farther south to visit the Gulf of Aqaba, the northeastern arm of the Red Sea. There, we took in the fresh, briny air and donned snorkel masks to explore the clear waters.

Perhaps our most surprising experience was at Aqaba’s underwater military museum, where a variety of war machines — tanks, troop carriers, a helicopter — have been scuttled near a coral reef, providing habitats for marine life and a fascinating point of exploration for divers.

During the day, it felt like there was little movement within the city of Aqaba. But at night everything came alive: The city’s streets were full of sounds and excitement, with crowds of people gathering to play games, chat and smoke hookah by the sea.

While returning to the airport in Amman, wending our way north on Highway 35, we had a chance to reflect on our trip. Jordan had offered us a perfect opportunity — after years of stasis — to discover a new place with a rich history and culture. I also felt real pleasure in photographing again: the people, the colors, the aromas, the landscapes. All of it had inspired my creativity.

Daniel Rodrigues is a photographer based in Portugal. You can follow his work on Instagram .

Follow New York Times Travel on Instagram , Twitter and Facebook . And sign up for our weekly Travel Dispatch newsletter to receive expert tips on traveling smarter and inspiration for your next vacation. Dreaming up a future getaway or just armchair traveling? Check out our 52 Places list for 2022 .

Open Up Your World

Considering a trip, or just some armchair traveling here are some ideas..

52 Places:  Why do we travel? For food, culture, adventure, natural beauty? Our 2024 list has all those elements, and more .

The Alaska Highway:  On an epic road trip, a family plots a course from Alaska to the Lower 48, passing through some of Canada’s most spectacular scenery .

Minorca:  Spend 36 hours on this slow-paced Spanish island , which offers a quieter and wilder retreat than its more touristy neighbors.

Japan:  A new high-speed train stop unlocks Kaga, a destination for hot springs, nourishing food and traditional crafts , as an easy-to-reach getaway from Tokyo.

London:  The Victoria and Albert Museum is a treasure trove of art and design. Here’s one besotted visitor’s plan for taking it all in .

Advertisement

• Publications

The Travel & Tourism Competitiveness Report 2019

World Economic Forum reports may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License , and in accordance with our Terms of Use .

Further reading All related content

Travel and tourism recovery: a perspective for South Asia and lessons for other regions in the age of COVID-19

What's in store for travel and tourism in South Asia after the coronavirus pandemic? What does recovery of the sector look like in the region and world?

Antarctica: the final coronavirus-free frontier. But will it stay that way?

The region has had close calls, including a cruise ship that was en route in April before 60% of the people on board tested positive for the coronavirus.

How global tourism can be more sustainable

One of the world’s biggest economic activities, tourism drives wealth, employment, and regional development.