journal of tourism and development

Journal of Tourism and Development

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Tourism and Sustainable Economic Development: Evidence from Belt and Road Countries

• Published: 22 January 2022
• Volume 14 , pages 503–516, ( 2023 )

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• Uktam Umurzakov 1 ,
• Shakhnoza Tosheva 2 &
• Raufhon Salahodjaev 3 , 4 , 5  

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This study is aimed to fill an existing gap in the empirical literature by exploring the effect of tourism on sustainable economic development across Belt and Road Initiative (BRI) countries. BRI is one of the paramount global partnerships as it covers more than 60 percent of the global population and 30 percent of the world’s GDP. Using data from 57 nations over the period of 2000–2018 and applying two-step generalized method of moments (GMM) estimator, we show that tourism has a positive and significant effect on sustainable development, measured by adjusted net national income. In particular, moving from a country with the lowest to highest number of per capita tourist arrivals leads to a 15.2% increase in adjusted per capita income. The results also remain valid using alternative econometric estimation techniques.

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Uktam Umurzakov

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Shakhnoza Tosheva

Tashkent State University of Economics, Tashkent, Uzbekistan

Raufhon Salahodjaev

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Umurzakov, U., Tosheva, S. & Salahodjaev, R. Tourism and Sustainable Economic Development: Evidence from Belt and Road Countries. J Knowl Econ 14 , 503–516 (2023). https://doi.org/10.1007/s13132-021-00872-0

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Received : 22 January 2021

Accepted : 01 December 2021

Published : 22 January 2022

Issue Date : March 2023

DOI : https://doi.org/10.1007/s13132-021-00872-0

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• Published: 24 February 2024

Modeling the link between tourism and economic development: evidence from homogeneous panels of countries

• Pablo Juan Cárdenas-García   ORCID: orcid.org/0000-0002-1779-392X 1 ,
• Juan Gabriel Brida 2 &
• Verónica Segarra 2  

Humanities and Social Sciences Communications volume  11 , Article number:  308 ( 2024 ) Cite this article

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• Development studies

Having previously analyzed the relationship between tourism and economic growth from distinct perspectives, this paper attempts to fill the void existing in scientific research on the relationship between tourism and economic development, by analyzing the relationship between these variables using a sample of 123 countries between 1995 and 2019. The Dumistrescu and Hurlin adaptation of the Granger causality test was used. This study takes a critical look at causal analysis with heterogeneous panels, given the substantial differences found between the results of the causal analysis with the complete panel as compared to the analysis of homogeneous country groups, in terms of their dynamics of tourism specialization and economic development. On the one hand, a one-way causal relationship exists from tourism to development in countries having low levels of tourism specialization and development. On the other hand, a one-way causal relationship exists by which development contributes to tourism in countries with high levels of development and tourism specialization.

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Introduction.

Across the world, tourism is one of the most important sectors. It has undergone exponential growth since the mid-1900s and is currently experiencing growth rates that exceed those of other economic sectors (Yazdi, 2019 ).

Today, tourism is a major source of income for countries that specialize in this sector, generating 5.8% of the global GDP (5.8 billion US$) in 2021 (UNWTO, 2022 ) and providing 5.4% of all jobs (289 million) worldwide. Although its relevance is clear, tourism data have declined dramatically due to the recent impact of the Covid-19 health crisis. In 2019, prior to the pandemic (UNWTO, 2020 ), tourism represented 10.3% of the worldwide GDP (9.6 billion US$), with the number of tourism-related jobs reaching 10.2% of the global total (333 million). With the evolution of the pandemic and the regained trust of tourists across the globe, it is estimated that by 2022, approximately 80% of the pre-pandemic figures will be attained, with a full recovery being expected by 2024 (UNWTO, 2022 ).

Given the importance of this economic activity, many countries consider tourism to be a tool enabling economic growth (Corbet et al., 2019 ; Ohlan, 2017 ; Xia et al., 2021 ). Numerous works have analyzed the relationship between increased tourism and economic growth; and some systematic reviews have been carried out on this relationship (Brida et al., 2016 ; Ahmad et al., 2020 ), examining the main contributions over the first two decades of this century. These reviews have revealed evidence in this area: in some cases, it has been found that tourism contributes to economic growth while, in other cases, the economic cycle influences tourism expansion. Moreover, other works offer evidence of a bi-directional relationship between these variables.

Distinct international organizations (OECD, 2010 ; UNCTAD, 2011 ) have suggested that not only does tourism promote economic growth, it also contributes to socio-economic advances in the host regions. This may be the real importance of tourism, since the ultimate objective of any government is to improve a country’s socio-economic development (UNDP, 1990 ).

The development of economic and other policies related to the economic scope of tourism, in addition to promoting economic growth, are also intended to improve other non-economic factors such as education, safety, and health. Improvements in these factors lead to a better life for the host population (Lee, 2017 ; Todaro and Smith, 2020 ).

Given tourism’s capacity as an instrument of economic development (Cárdenas-García et al., 2015 ), distinct institutions such as the United Nations Conference on Trade and Development, the United Nations Economic Commission for Africa, the United Nations World Tourism Organization and the World Bank, have begun funding projects that consider tourism to be a tool for improved socio-economic development, especially in less advanced countries (Carrillo and Pulido, 2019 ).

This new trend within the scientific literature establishes, firstly, that tourism drives economic growth and, secondly, that thanks to this economic growth, the population’s economic conditions may be improved (Croes et al., 2021 ; Kubickova et al., 2017 ). However, to take advantage of the economic growth generated by tourism activity to boost economic development, specific policies should be developed. These policies should determine the initial conditions to be met by host countries committed to tourism as an instrument of economic development. These conditions include regulation, tax system, and infrastructure provision (Cárdenas-García and Pulido-Fernández, 2019 ; Lejárraga and Walkenhorst, 2013 ; Meyer and Meyer, 2016 ).

Therefore, it is necessary to differentiate between the analysis of the relationship between tourism and economic growth, whereby tourism boosts the economy of countries committed to tourism, traditionally measured through an increase in the Gross Domestic Product (Alcalá-Ordóñez et al., 2023 ; Brida et al., 2016 ), and the analysis of the relationship between tourism and economic development, which measures the effect of tourism on other factors (not only economic content but also inequality, education, and health) which, together with economic criteria, serve as the foundation to measure a population’s development (Todaro and Smith, 2020 ).

However, unlike the analysis of the relationship between tourism and economic growth, few empirical studies have examined tourism’s capacity as a tool for development (Bojanic and Lo, 2016 ; Cárdenas-García and Pulido-Fernández, 2019 ; Croes, 2012 ).

To help fill this gap in the literature analyzing the relationship between tourism and economic development, this work examines the contribution of tourism to economic development, given that the relationship between tourism and economic growth has been widely analyzed by the scientific literature. Moreover, given that the literature has demonstrated that tourism contributes to economic growth, this work aims to analyze whether it also contributes to economic development, considering development in the broadest possible sense by including economic and socioeconomic variables in the multi-dimensional concept (Wahyuningsih et al., 2020 ).

Therefore, based on the results of this work, it is possible to determine whether the commitment made by many international organizations and institutions in financing tourism projects designed to improve the host population’s socioeconomic conditions, especially in countries with lower development levels, has, in fact, resulted in improved development levels.

It also presents a critical view of causal analyses that rely on heterogeneous panels, examining whether the conclusions reached for a complete panel differ from those obtained when analyzing homogeneous groups within the panel. As seen in the literature review analyzing the relationship between tourism and economic development, empirical works using panel data from several countries tend to generalize the results obtained to the entire panel, without verifying whether, in fact, they are relevant for all of the analyzed countries or only some of the same. Therefore, this study takes an innovative approach by examining the panel countries separately, analyzing the homogeneous groups distinctly.

Therefore, this article presents an empirical analysis examining whether a causal relationship exists between tourism and economic development, with development being considered to be a multi-dimensional variable including a variety of factors, distinct from economic ones. Panel data from 123 countries during the 1995–2019 period was considered to examine the causal relationship between tourism and economic development. For this, the Granger causality test was performed, applying the adaptation of this test made by Dumistrescu and Hurlin. First, a causal analysis was performed collectively for all of the countries of the panel. Then, a specific analysis was performed for each of the homogeneous groups of countries identified within the panel, formed according to levels of tourism specialization and development.

This article provides information on tourism’s capacity to serve as an instrument of development, helping to fill the gap in scientific research in this area. It critically examines the use of causal analyses based on heterogeneous samples of countries. This work offers the following main novelties as compared to prior works on the same topic: firstly, it examines the relationship between tourism and economic development, while the majority of the existing works only analyze the relationship between tourism and economic growth; secondly, it analyzes a large sample of countries, representing all of the global geographic areas, whereas the literature has only considered works from specific countries or a limited number of nations linked to a specific country in a specific geographical area, and; thirdly, it analyzes the panel both individually and collectively, for each of the homogenous groups of countries identified, permitting the adoption of specific policies for each group of countries according to the identified relationship, as compared to the majority of works that only analyze the complete panel, generalizing these results for all countries in the sample.

Overall, the results suggest that a relationship exists between tourism and development in all of the analyzed countries from the sample. A specific analysis was performed for homogeneous country groups, only finding a causal relationship between tourism and development in certain country groups. This suggests that the use of heterogeneous country samples in causal analyses may give rise to inappropriate conclusions. This may be the case, for example, when finding causality for a broad panel of countries, although, in fact, only a limited number of panel units actually explain this causal relationship.

The remainder of the document is organized as follows: the next section offers a review of the few existing scientific works on the relationship between tourism and economic development; section three describes the data used and briefly explains the methodology carried out; section four details the results obtained from the empirical analysis; and finally, the conclusions section discusses the main implications of the work, also providing some recommendations for economic policy.

Tourism and economic development

Numerous organizations currently recognize the importance of tourism as an instrument of economic development. It was not until the late 20th century, however, when the United Nations World Tourism Organization (UNWTO), in its Manila Declaration, established that the development of international tourism may “help to eliminate the widening economic gap between developed and developing countries and ensure the steady acceleration of economic and social development and progress, in particular of the developing countries” (UNWTO, 1980 ).

From a theoretical point of view, tourism may be considered an effective activity for economic development. In fact, the theoretical foundations of many works are based on the relationship between tourism and development (Ashley et al., 2007 ; Bolwell and Weinz, 2011 ; Dieke, 2000 ; Sharpley and Telfer, 2015 ; Sindiga, 1999 ).

The link between tourism and economic development may arise from the increase in tourist activity, which promotes economic growth. As a result of this economic growth, policies may be developed to improve the resident population’s level of development (Alcalá-Ordóñez and Segarra, 2023 ).

Therefore, it is essential to identify the key variables permitting the measurement of the level of economic development and, therefore, those variables that serve as a basis for analyzing whether tourism results in improved the socioeconomic conditions of the host population (Croes et al., 2021 ). Since economic development refers not only to economic-based variables, but also to others such as inequality, education, or health (Todaro and Smith, 2020 ), when analyzing the economic development concept, it has been frequently linked to human development (Pulido-Fernández and Cárdenas-García, 2021 ). Thus, we wish to highlight the major advances resulting from the publication of the Human Development Index (HDI) when measuring economic development, since it defines development as a multidimensional variable that combines three dimensions: health, education, and income level (UNDP, 2023 ).

However, despite the importance that many organizations have given to tourism as an instrument of economic development, basing their work on the relationship between these variables, a wide gap continues to exist in the scientific literature for empirical studies that examine the existence of a relationship between tourism and economic development, with very few empirical analyses analyzing this relationship.

First, a group of studies has examined the causal relationship between tourism and economic development, using heterogeneous samples, and without previously grouping the subjects based on homogeneous characteristics. Croes ( 2012 ) analyzed the relationship between tourism and economic development, measured through the HDI, finding that a bidirectional relationship exists for the cases of Nicaragua and Costa Rica. Using annual data from 2001 to 2014, Meyer and Meyer ( 2016 ) performed a collective analysis of South African regions, determining that tourism contributes to economic development. For a panel of 63 countries worldwide, and once again relying on the HDI to define economic development, it was determined that tourism contributes to economic development. Kubickova et al. ( 2017 ), using annual data for the 1995–2007 period, analyzed Central America and Caribbean nations, determining the existence of this relationship by which tourism influences the level of economic development and that the level of development conditions the expansion of tourism. Another work examined nine micro-states of America, Europe, and Africa (Fahimi et al., 2018 ); and 21 European countries in which human capital was measured, as well as population density and tourism income, analyzing panel data and determining that tourism results in improved economic development. Finally, within this first group of works, Chattopadhyay et al. ( 2022 ), using a broad panel of destinations, (133 countries from all geographic areas of the globe) determined that there is no relationship between tourism and economic development.

Studies performed with large country samples that attempt to determine the causal relationship between tourism and economic development by analyzing countries that do not necessarily share homogeneous characteristics, may lead to erroneous conclusions, establishing causality (or not) for panel sets even when this situation is actually explained by a small number of panel units.

Second, another group of studies have analyzed the causal relationship between tourism and economic development, considering the previous limitation, and has grouped the subjects based on their homogeneous characteristics. Cárdenas-García et al. ( 2015 ) used annual data from 1990–2010, in a collective analysis of 144 countries, making a joint panel analysis and then examining two homogeneous groups of countries based on their level of economic development. They determined that tourism contributes to economic development, but only in the most developed group of countries. They determined that tourism contributes to economic development, both for the total sample and for the homogeneous groups analyzed. Pulido-Fernández and Cárdenas-García ( 2021 ), using annual data for the 1993–2017 period, performed a joint analysis of 143 countries, followed by a specific analysis for three groups of countries sharing homogeneous characteristics in terms of tourism growth and development level. They determined that tourism contributes to economic development and that development level conditions tourism growth in the most developed countries.

Finally, another group of studies has analyzed the causal relationship between tourism and economic development in specific cases examined on an individual basis. In a specific analysis by Aruba et al. ( 2016 ), it was determined that tourism contributes to human development. Analyzing Malaysia, Tan et al. ( 2019 ) determined that tourism contributes to development, but only over the short term, and that level of development does not influence tourism growth. Similar results were obtained by Boonyasana and Chinnakum ( 2020 ) in an analysis carried out in Thailand. In this case of Thailand (Boonyasana and Chinnakum, 2020 ), which relied on the HDI, the relationship with economic growth was also analyzed, finding that an increase in tourism resulted in improved economic development. Finally, Croes et al. ( 2021 ), in a specific analysis of Poland, determined that tourism does not contribute to development.

As seen from the analysis of the most relevant publications detailed in Table 1 , few empirical works have considered the relationship between tourism and economic development, in contrast to the numerous works from the scientific literature that have examined the relationship between tourism and economic growth. Most of the works that have empirically analyzed the relationship between tourism and economic development have determined that tourism positively influences the improved economic development in host destinations. To a lesser extent, some studies have found a bidirectional relationship between these variables (Croes, 2012 ; Kubickova et al., 2017 ; Pulido-Fernández and Cárdenas-García, 2021 ) while others have found no relationship between tourism and economic development (Chattopadhyay et al., 2022 ; Croes et al., 2021 ).

Furthermore, in empirical works relying on panel data, the results have tended to be generalized to the entire panel, suggesting that tourism improves economic development in all countries that are part of the panel. This has been the case in all of the examined works, with the exception of two studies that analyzed the panel separately (Cárdenas-García et al., 2015 ; Pulido-Fernández and Cárdenas-García, 2021 ).

Thus, it may be suggested that the use of very large country panels and, therefore, including very heterogeneous destinations, as was the case in the works of Biagi et al. ( 2017 ) using a panel of 63 countries, as well as that of Chattopadhyay et al. ( 2022 ) working with a panel of 133 countries, may lead to error, given that this relationship may only arise in certain destinations of the panel, although it is generalized to the entire panel.

This work serves to fill this gap in the literature by analyzing the panel both collectively and separately, for each of the homogenous groups of countries that have been previously identified.

The lack of relevant works on the relationship between tourism and development, and of studies using causal analyses to examine these variables based on heterogeneous panels, may lead to the creation of rash generalizations regarding the entirety of the analyzed countries. Thus, conclusions may be reached that are actually based on only specific panel units. Therefore, we believe that this study is justified.

Methodological approach

Given the objective of this study, to determine whether a causal relationship exists between tourism and socio-economic development, it is first necessary to identify the variables necessary to measure tourism activity and development level. Thus, the indicators are highly relevant, given that the choice of indicator may result in distinct results (Rosselló-Nadal and He, 2020 ; Song and Wu, 2021 ).

Table 2 details the measurement variables used in this work. Specifically, the following indicators have been used in this paper to measure tourism and economic development:

Measurement of tourist activity. In this work, we decided to consider tourism specialization, examining the number of international tourists received by a country with regard to its population size as the measurement variable.

This information on international tourists at a national level has been provided annually by the United Nations World Tourism Organization since 1995 (UNWTO, 2023 ). This variable has been relativized based on the country’s population, according to information provided by the World Bank on the residents of each country (WB, 2023 ).

Tourism specialization is considered to be the level of tourism activity, specifically, the arrival of tourists, relativized based on the resident population, which allows for comparisons to be made between countries. It accurately measures whether or not a country is specialized in this economic activity. If the variable is used in absolute values, for example, the United States receives more tourists than Malta, so based on this variable it may be that the first country is more touristic than the second. However, in reality, just the opposite happens, Malta is a country in which tourist activity is more important for its economy than it is in the United States, so the use of tourist specialization as a measurement variable classifies, correctly, both Malta as a country with high tourism specialization and to the United States as a country with low tourism specialization.

Therefore, most of the scientific literature establishes the need to use the total number of tourists relativized per capita, given that this allows for the determination of the level of tourism specialization of a tourism destination (Dritsakis, 2012 ; Tang and Abosedra, 2016 ); furthermore, this indicator has been used in works analyzing the relationship between tourism and economic development (for example, Biagi et al., 2017 ; Boonyasana and Chinnakum; 2020 ; Croes et al., 2021 ; Fahimi et al., 2018 ).

Although some works have used other variables to measure tourism, such as tourism income, exports, or tourist spending, these variables are not available for all of the countries making up the panel, so the sample would have been significantly reduced. Furthermore, the data available for these alternative variables do not come from homogeneous databases, and therefore cannot be compared.

Measurement of economic development. In this work, the Human Development Index has been used to measure development.

This information is provided by the United Nations Development Program, which has been publishing it annually at the country level since 1990 (UNDP, 2023 ).

The selection of this indicator to measure economic development is in line with other works that have defended its use to measure the impact on development level (for example, Jalil and Kamaruddin, 2018 ; Sajith and Malathi, 2020 ); this indicator has also been used in works analyzing the relationship between tourism and economic development (for example, Meyer and Meyer, 2016 ; Kubickova et al., 2017 ; Pulido-Fernández and Cárdenas-García, 2021 ).

Although some works have used other variables, such as poverty or inequality, to measure development, these variables are not available for all of the countries forming the panel. Therefore the sample would have been considerably reduced and the data available for these alternative variables do not come from homogenous databases, and therefore comparisons cannot be made.

These indicators are available for a total of 123 countries, across the globe. Thus, these countries form part of the sample analyzed in this study.

As for the time frame considered in this work, two main issues were relevant when determining this period: on the one hand, there is an initial time restriction for the analyzed series, given that information on the arrival of international tourists is only available as of 1995, the first year when this information was provided by the UNWTO. On the other hand, it was necessary to consider the effect of the Covid-19 pandemic and the resulting tourism sector crisis, which also affected the global economy as a whole. Therefore, our time series ended as of 2019, with the overall time frame including data from 1995 to 2019, a 25-year period.

Previous considerations

Caution should be taken when considering causality tests to determine the relationships between two variables, especially in cases in which large heterogeneous samples are used. This is due to the fact that generalized conclusions may be reached when, in fact, the causality is only produced by some of the subjects of the analyzed sample. This study is based on this premise. While heterogeneity in a sample is clearly a very relevant aspect, in some cases, it may lead to conclusions that are less than appropriate.

In this work, a collective causal analysis has been performed on all of the countries of the panel, which consists of 123 countries. However, given that it is a broad sample including countries having major differences in terms of size, region, development level, or tourism performance, the conclusions obtained from this analysis may lead to the generalization of certain conclusions for the entire sample set, when in fact, these relationships may only be the case for a very small portion of the sample. This has been the case in other works that have made generalized conclusions from relatively large samples in which the sample’s homogeneity regarding certain patterns was not previously verified (Badulescu et al., 2021 ; Ömer et al., 2018 ; Gedikli et al., 2022 ; Meyer and Meyer, 2016 ; Xia et al., 2021 ).

Therefore, after performing a collective analysis of the entire panel, the causal relationship between tourism and development was then determined for homogeneous groups of countries that share common patterns of tourism performance and economic development level, to analyze whether the generalized conclusions obtained in the previous section differ from those made for the individual groups. This was in line with strategies that have been used in other works that have grouped countries based on tourism performance (Min et al., 2016 ) or economic development level (Cárdenas-García et al., 2015 ), prior to engaging in causal analyses. To classify the countries into homogeneous groups based on tourism performance and development level, a previous work was used (Brida et al., 2023 ) which considered the same sample of 123 countries, relying on the same data to measure tourism and development level and the same time frame. This guarantees the coherence of the results obtained in this work.

From the entire panel of 123 countries, a total of six country groups were identified as having a similar dynamic of tourism and development, based on qualitative dynamic behavior. In addition, an “outlier” group of countries was found. These outlier countries do not fit into any of the groups (Brida et al., 2023 ). The three main groups of countries were considered, discarding three other groups due to their small size. Table 3 presents the group of countries sharing similar dynamics in terms of tourism performance and economic development level.

Applied methodology

As indicated above, this work uses the Tourist Specialization Rate (TIR) and the Human Development Index (HDI) to measure tourism and economic development, respectively. In both cases, we work with the natural logarithm (l.TIR and l.HDI) as well as the first differences between the variables (d.l.TIR and d.l.HDI), which measure the growth of these variables.

A complete panel of countries is used, consisting of 123 countries. The three main groups indicated in the previous section are also considered (the first of the groups contains 36 countries, the second contains 29 and the last group contains 43).

The Granger causality test ( 1969 ) is used to analyze the relationships between tourism specialization and development level; this test shows if one variable predicts the other, but this should not be confused with a cause-effect relationship.

In the context of panel data, different tests may be used to analyze causality. Most of these tests differ with regard to the assumptions of homogeneity of the panel unit coefficients. While the standard form of the Granger causality test for panels assumes that all of the coefficients are equal between the countries forming part of the panel, the Dumitrescu and Hurlin test (2012) considers that the coefficients are different between the countries forming part of the panel. Therefore, in this work, Granger’s causality is analyzed using the Dumitrescu and Hurlin test (2012). In this test, the null hypothesis is of no homogeneous causality; in other words, according to the null hypothesis, causality does not exist for any of the countries of the analyzed sample whereas, according to the alternative hypothesis, in which the regression model may be different in the distinct countries, causality is verified for at least some countries. The approach used by Dumitrescu and Hurlin ( 2012 ) is more flexible in its assumptions since although the coefficients of the regressions proposed in the tests are constant over time, the possibility that they may differ for each of the panel elements is accepted. This approach has more realistic assumptions, given that countries exhibit different behaviors. One relevant aspect of this type of tests is that they offer no information on which countries lead to the rejection of the lack of causality.

Given the specific characteristics of this type of tests, the presence of very heterogeneous samples may lead to inappropriate conclusions. For example, causality may be assumed for a panel of countries, when only a few of the panel’s units actually explain this relationship. Therefore, this analysis attempts to offer novel information on this issue, revealing that the conclusions obtained for the complete set of 123 countries are not necessarily the same as those obtained for each homogeneous group of countries when analyzed individually.

Given the nature of the variables considered in this work, specifically, regarding tourism, it is expected that a shock taking place in one country may be transmitted to other countries. Therefore, we first analyze the dependency between countries, since this may lead to biases (Pesaran, 2006 ). The Pesaran cross-sectional dependence test (2004) is used for the total sample and for each of the three groups individually.

First, a dependence analysis is performed for the countries of the sample, verifying the existence of dependence between the panel subjects. A cross-sectional dependence test (Pesaran, 2004 ) is used, first for the overall set of countries in the sample and second, for each of the groups of countries sharing homogeneous characteristics.

The results are presented in Table 4 , indicating that the test is statistically significant for the two variables, both for all of the countries in the sample and for each of the homogeneous country clusters, for the variables taken in logarithms as well as their first differences.

Upon rejecting the null hypothesis of non-cross-sectional dependence, it is assumed that a shock occurs in a country that may be transmitted to other countries in the sample. In fact, the lack of dependence between the variables, both tourism and development, is natural in this type of variables, given the economic cycle through the globalization of the economic activity, common regions visited by tourists, the spillover effect, etc.

Second, the stationary nature of the series is tested, given that cross-sectional dependence has been detected between the variables. First-generation tests may present certain biases in the rejection of the null hypothesis since first-generation unit root tests do not permit the inclusion of dependence between countries (Pesaran, 2007 ). On the other hand, second-generation tests permit the inclusion of dependence and heterogeneity. Therefore, for this analysis, the augmented IPS test (CIPS) proposed by Pesaran ( 2007 ) is used. This second-generation unit root test is the most appropriate for this case, given the cross-sectional dependence.

The results are presented in Table 5 , showing the statistics of the CIPS test for both the overall set of countries in the sample and in each of the homogeneous clusters of countries. The results are presented for models with 1, 2, and 3 delays, considering both the variables in the logarithm and their first differences.

As observed, the null hypothesis of unit root is not rejected for the variables in levels, but it is rejected for the first differences. This result is found in all of the cases, for both the total sample and for each of the homogeneous groups, with a significance of 1%. Therefore, the variables are stationary in their first differences, that is, the variables are integrated at order 1. Given that the causality test requires stationary variables, in this work it is used with the variation or growth rate of the variables, that is, the variable at t minus the variable at t−1.

Finally, to analyze Granger’s causality, the test by Dumitrescu and Hurlin ( 2012 ) is used. This test is used to analyze the causal relationship in both directions; that is, whether tourism contributes to economic development and whether the economic development level conditions tourism specialization. Statistics are calculated considering models with 1, 2, and 3 delays. Considering that cross-sectional dependence exists, the p-values are corrected using bootstrap techniques (making 500 replications). Given that the test requires stationary variables, primary differences of both variables were considered.

Table 6 presents the result of the Granger causality analysis using the Dumitrescu and Hurlin test (2012), considering the null hypothesis that tourism does not condition development level, either for all of the countries or for each homogeneous country cluster.

For the entire sample of countries, the results suggest that the null hypothesis of no causality from tourism to development was rejected when considering 3 delays (in other works analyzing the relationship between tourism and development, the null hypothesis was rejected with a similar level of delay: Rivera ( 2017 ) when considering 3–4 delays or Ulrich et al. ( 2018 ) when considering 3 delays). This suggests that for the entire panel, one-way causality exists whereby tourism influences economic development, demonstrating that tourism specialization contributes positively to improving the economic development of countries opting for tourism development. This is in line with the results of Meyer and Meyer ( 2016 ), Ridderstaat et al. ( 2016 ); Biagi et al. ( 2017 ); Fahimi et al. ( 2018 ); Tan et al. ( 2019 ), or Boonyasana and Chinnakum ( 2020 ).

However, the previous conclusion is very general, given that it is based on a very large sample of countries. Therefore, it may be erroneous to generalize that tourism is a tool for development. In fact, the results indicate that, when analyzing causality by homogeneous groups of countries, sharing similar dynamics in both tourism and development, the null hypothesis of no causality from tourism to development is only rejected for the group C countries, when considering three delays. Therefore, the development of generalized policies to expand tourism in order to improve the socioeconomic conditions of any destination type should consider that this relationship between tourism and economic development does not occur in all cases. Thus, it should first be determined if the countries opting for this activity have certain characteristics that will permit a positive relationship between said variables.

In other words, it may be a mistake to generalize that tourism contributes to economic development for all countries, even though a causal relationship exists for the entire panel. Instead, it should be understood that tourism permits an improvement in the level of development only in certain countries, in line with the results of Cárdenas-García et al. ( 2015 ) or Pulido-Fernández and Cárdenas-García ( 2021 ). In this specific work, this positive relationship between tourism and development only occurs in countries from group C, which are characterized by a low level of tourism specialization and a low level of development. Some works have found similar results for countries from group C. For example, Sharma et al. ( 2020 ) found the same relationship for India, while Nonthapot ( 2014 ) had similar findings for certain countries in Asia and the Pacific, which also made up group C. Some recent works have analyzed the relationship between tourism specialization and economic growth, finding similar results. This has been the case with Albaladejo et al. ( 2023 ), who found a relationship from tourism to economic growth only for countries where income is low, and the tourism sector is not yet developed.

These countries have certain limitations since even when tourism contributes to improved economic development, their low levels of tourism specialization do not allow them to reach adequate host population socioeconomic conditions. Therefore, investments in tourism are necessary there in order to increase tourism specialization levels. This increase in tourism may allow these countries to achieve development levels that are similar to other countries having better population conditions.

Therefore, in this group, consisting of 43 countries, a causal relationship exists, given that these countries are characterized by a low level of tourism specialization. However, the weakness of this activity, due to its low relevance in the country, prevents it from increasing the level of economic development. In these countries (details of these countries can be found in Table 3 , specifically, the countries included in Group C), policymakers have to develop policies to improve tourism infrastructure as a prior step to improving their levels of development.

On the other hand, in Table 7 , the results of Granger’s causal analysis based on the Dumitrescu and Hurlin test (2012) are presented, considering the null hypothesis that development level does not condition an increase in tourism, both in the overall sample set and in each of the homogeneous country clusters.

The results indicate that, for the entire country sample, the null hypothesis of no causality from development to tourism is not rejected, for any type of delay. This suggests that, for the entire panel, one-way causality does not exist, with level of development influencing the level of tourism specialization. This is in line with the results of Croes et al. ( 2021 ) in a specific analysis in Poland.

Once again, this conclusion is quite general, given that it has been based on a very broad sample of countries. Therefore, it may be erroneous to generalize that the development level does not condition tourism specialization. Past studies using a large panel of countries, such as the work of Chattopadhyay et al. ( 2022 ) analyzing panel data from 133 countries, have been generalized to all of the analyzed countries, suggesting that economic development level does not condition the arrival of tourists to the destination, although, in fact, this relationship may only exist in specific countries within the analyzed panel.

In fact, the results indicate that, when analyzing causality by homogeneous country groups sharing a similar dynamic, for both tourism and development, the null hypothesis of no causality from development to tourism is only rejected for country group A when considering 2–3 delays. Although the statistics of the test differ, when the sample’s time frame is small, as in this case, the Z-bar tilde statistic is more appropriate.

Thus, development level influences tourism growth in Group A countries, which are characterized by a high level of development and tourism specialization, in accordance with the prior results of Pulido-Fernández and Cárdenas-García ( 2021 ).

These results, suggesting that tourism is affected by economic development level, but only in the most developed countries, imply that the existence of better socioeconomic conditions in these countries, which tend to have better healthcare systems, infrastructures, levels of human resource training, and security, results in an increase in tourist arrivals to these countries. In fact, when traveling to a specific tourist destination, if this destination offers attractive factors and a higher level of economic development, an increase in tourist flows was fully expected.

In this group, consisting of 36 countries, the high development level, that is, the proper provision of socio-economic factors in their economic foundations (training, infrastructures, safety, health, etc.) has led to the attraction of a large number of tourists to their region, making their countries having high tourism specialization.

Although international organizations have recognized the importance of tourism as an instrument of economic development, based on the theoretical relationship between these two variables, few empirical studies have considered the consequences of the relationship between tourism and development.

Furthermore, some hasty generalizations have been made regarding the analysis of this relationship and the analysis of the relationship of tourism with other economic variables. Oftentimes, conclusions have been based on heterogeneous panels containing large numbers of subjects. This may lead to erroneous results interpretation, basing these results on the entire panel when, in fact, they only result from specific panel units.

Given this gap in the scientific literature, this work attempts to analyze the relationship between tourism and economic development, considering the panel data in a complete and separate manner for each of the previously identified country groups.

The results highlight the need to adopt economic policies that consider the uniqueness of each of the countries that use tourism as an instrument to improve their socioeconomic conditions, given that the results differ according to the specific characteristics of the analyzed country groups.

This work provides precise results regarding the need for policymakers to develop public policies to ensure that tourism contributes to the improvement of economic development, based on the category of the country using this economic activity to achieve greater levels of economic development.

Specifically, this work has determined that tourism contributes to economic development, but only in countries that previously had a lower level of tourism specialization and were less developed. This highlights the need to invest in tourism to attract more tourists to these countries to increase their economic development levels. Countries having major natural attraction resources or factors, such as the Dominican Republic, Egypt, India, Morocco, and Vietnam, need to improve their positioning in the international markets in order to attain a higher level of tourism specialization, which will lead to improved development levels.

Furthermore, the results of this study suggest that a greater past economic development level of a country will help attract more tourists to these countries, highlighting the need to invest in security, infrastructures, and health in order for these destinations to be considered attractive and increase tourist arrival. In fact, given their increased levels of development, countries such as Spain, Greece, Italy, Qatar, and Uruguay have become attractive to tourists, with soaring numbers of visitors and high levels of tourism specialization.

Therefore, the analysis of the relationship between tourism and economic development should focus on the differentiated treatment of countries in terms of their specific characteristics, since working with panel data with large samples and heterogenous characteristics may lead to incorrect results generalizations to all of the analyzed destinations, even though the obtained relationship in fact only takes place in certain countries of the sample.

Conclusions and policy implications

Within this context, the objective of this study is twofold: on the one hand, it aims to contribute to the lack of empirical works analyzing the causal relationship between tourism and economic development using Granger’s causality analysis for a broad sample of countries from across the globe. On the other hand, it critically examines the use of causality analysis in heterogeneous samples, by verifying that the results for the panel set differ from the results obtained when analyzing homogeneous groups in terms of tourism specialization and development level.

In fact, upon analyzing the causal relationship from tourism to development, and the causal relationship from development to tourism, the results from the entire panel, consisting of 123 countries, differ from those obtained when analyzing causality by homogeneous country groups, in terms of tourism specialization and economic development dynamics of these countries.

On the one hand, a one-way causality relationship is found to exist, whereby tourism influences economic development for the entire sample of countries, although this conclusion cannot be generalized, since this relationship is only explained by countries belonging to Group C (countries with low levels of tourism specialization and low development levels). This indicates that, although a causal relationship exists by which tourism contributes to economic development in these countries, the low level of tourism specialization does not permit growth to appropriate development levels.

The existence of a causal relationship whereby the increase in tourism precedes the improvement of economic development in this group of countries having a low level of tourism specialization and economic development, suggests the appropriateness of the focus by distinct international organizations, such as the United Nations Conference on Trade and Development or the United Nations Economic Commission for Africa, on funding tourism projects (through the provision of tourism infrastructure, the stimulation of tourism supply, or positioning in international markets) in countries with low economic development levels. This work has demonstrated that investment in tourism results in the attracting of a greater flow of tourists, which will contribute to improved economic development levels.

Therefore, both international organizations financing projects and public administrations in these countries should increase the funding of projects linked to tourism development, in order to increase the flow of tourism to these destinations. This, given that an increase in tourism specialization suggests an increased level of development due to the demonstrated existence of a one-way causal relationship from tourism to development in these countries, many of which form part of the group of so-called “least developed” countries. However, according to the results obtained in this work, this relationship is not instantaneous, but rather, a certain delay exists in order for economic development to improve as a result of the increase in tourism. Therefore, public managers must adopt a medium and long-term vision of tourism activity as an instrument of development, moving away from short-term policies seeking immediate results, since this link only occurs over a broad time horizon.

On the other hand, this study reveals that a one-way causal relationship does not exist, by which the level of development influences tourism specialization level for the entire sample of countries. However, this conclusion, once again, cannot be generalized given that in countries belonging to Group A (countries with a high development level and a high tourism specialization level), a high level of economic development determines a higher level of tourism specialization. This is because the socio-economic structure of these countries (infrastructures, training or education, health, safety, etc.) permits their shaping as attractive tourist destinations, thereby increasing the number of tourists visiting them.

Therefore, investments made by public administrations to improve these factors in other countries that currently do not display this causal relationship implies the creation of the necessary foundations to increase their tourism specialization and, therefore, as shown in other works, tourism growth will permit economic growth, with all of the associated benefits for these countries.

Therefore, to attract tourist flows, it is not only important for a country to have attractive factors or resources, but also to have an adequate level of prior development. In other words, the tourists should perceive an adequate level of security in the destination; they should be able to use different infrastructures such as roads, airports, or the Internet; and they should receive suitable services at the destination from personnel having an appropriate level of training. The most developed countries, which are the destinations having the greatest endowment of these resources, are the ones that currently receive the most tourist flows thanks to the existence of these factors.

Therefore, less developed countries that are committed to tourism as an instrument to improve economic development should first commit to the provision of these resources if they hope to increase tourist flows. If this increase in tourism takes place in these countries, their economic development levels have been demonstrated to improve. However, since these countries are characterized by low levels of resources, cooperation by organizations financing the necessary investments is key to providing them with these resources.

Thus, a critical perspective is necessary when considering the relationship between tourism and economic development based on global causal analysis using heterogeneous samples with numerous subjects. As in this case, carrying out analyses on homogeneous groups may offer interesting results for policymakers attempting to suitably manage population development improvements due to tourism growth and tourism increases resulting from higher development levels.

One limitation of this work is its national scope since evidence suggests that tourism is a regional and local activity. Therefore, it may be interesting to apply this same approach on a regional level, using previously identified homogeneous groups.

And given that the existence of a causal relationship (in either direction) between tourism and development has only been determined for a specific set of countries, future works could consider other country-specific factors that may determine this causal relationship, in addition to the dynamics of tourism specialization and development level.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Tourism and Development Concepts and Issues

Journal of Tourism Futures

ISSN : 2055-5911

Article publication date: 15 December 2017

Issue publication date: 15 December 2017

• Development
• Developing countries

Webster, C. (2017), "Tourism and Development Concepts and Issues", Journal of Tourism Futures , Vol. 3 No. 2, pp. 194-195. https://doi.org/10.1108/JTF-09-2017-066

Emerald Publishing Limited

Copyright © 2017, Craig Webster

Published in the Journal of Tourism Futures. 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

One of the recurring themes in tourism studies is the notion that tourism is a vehicle for economic development. This edited book makes a valuable contribution by exploring the important concepts and issues that are linked with the perception that tourism can be a vehicle for leading countries out of poverty. While edited volumes, in my opinion, are not usually the most readable or useful types of books, this edited volume is thorough and extensive, giving anyone who is interested in the issue of tourism and economic development issues a great deal to read through.

The book is divided into three different parts. Part 1 deals with the conceptual perspectives of the relationship between tourism and development. This first part is composed of only two chapters, one exploring the relationship between tourism and development and the other exploring the evolution of development theory and tourism. Part 2 is composed of seven chapters and deals with the relationship between development and tourism. Part 3 is also composed of seven chapters and deals with barriers and challenges to tourism development. In addition, there is an introduction, explaining the intention of the book and explaining the logic of the delineation of the book into three different parts. The last chapter, one chapter in Part 3, is a conclusion authored by the editors, explaining a great deal about the need for this second edition, mentioning the improvements to the new edition, and highlighted how this book contributes to the discussion of tourism and development.

The book has many of the features one would expect, such as biographical information about the 14 contributors to the book as well as an index and extensive reference list. For those who would like an extensive list of resources on the topic of development and tourism, this 71-page long reference list may be in and of itself a helpful resource. The 16 chapters contain 21 figures and 21 tables, adding to the visual information that breaks up the monotony of the prose in the chapters and summarizes a great deal of information or illustrates a point in an easy-to-understand way. In addition, the front cover is attractive, dominated by green and blue with what appears to be a stock photo of several cruise ships.

There are some definite strengths to this book. First, it is a very thorough book that includes the perspectives of many respective researchers in the field. Anyone doing serious research in the field would recognize many of the names that appear as authors, including Richard Sharpley, Dallen Timothy, and C. Michael Hall, just to name a few. To have these respected and accomplished authors to contribute to the book is nothing to sneeze at. In addition, by having so many different authors look into the relationship between tourism and development in a very mature and deep way and from different perspectives is quite helpful. For example, the chapter on human rights and tourism development could be used as a good primer on human rights, explaining the concept of human rights and then going on to explain the relationship between human rights and tourism development. There are several of the chapters that contain information that would make the chapter serve as a useful primer for other topics such as international studies or development studies, since it is presumes that most of us who research tourism do not have a background in international relations, development, or human rights.

There are some critical comments that could be made about this book but they tend not to be very deep. One thing that seems to be missing is a chapter that would deal with the history of tourism as a vehicle for development. While the book does have chapters that occasionally deal with this, the book mostly looks at tourism and development from a theoretical perspective. In addition, I am not sure what the cover photo has to do with the topic of the book and I feel that a more relevant cover photo would have been a better choice. Although it is pleasant to see cruise ships and blue skies, I am not sure what this has to do with the topic and would imagine that there would be a better way to convey the concept of the book in a visual way. Also, the decision to place references at the end of the work in one large reference section rather than have references at the end of each chapter is sometimes a bit inconvenient for readers such as myself, as I prefer to look at a shorter list that is more compact in terms of subject matter covered and is only a few pages long rather than 71 pages long.

All-in-all, the book has a great deal of value, although I would think that it would only be accessible to a fairly limited audience. For those of us interested in the future, it is hard to get a great deal of value out of this book, as the frame of reference is largely historical and theoretical, although the value of this is somewhat rectified by the fact that the content gives thorough and intelligent background information for the many political and ecological things that we are concerned about in the future (poverty, environment, climate change, and sustainability). While many of the chapters are wonderful introductions to subject matter that most people in the field know little to nothing about, the complexity of the language and depth of discussion would make it hard for most people who have not completed a bachelor’s level education to comprehend. As such, though, it would be a very useful and helpful book for those teaching in related issues at the MA level or PhD level. If I were teaching a course at the post-graduate level in tourism and development, I would want this and would use it as a central text, as deals with such issues as human rights, sustainability, and poverty reduction in a sophisticated and intelligent manner, appropriate for that level. However, the language and sophistication of the concepts would likely make the book inaccessible for most people. This is not light reading. But it is a nifty and thorough reference book containing primers on topics/areas of study in which most in the field do not have a strong background.

Acknowledgements

© Craig Webster. Published in the Journal of Tourism Futures . 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

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Analyzing the tourism efficiency and its influencing factors of China’s coastal provinces

Roles Conceptualization, Data curation, Investigation, Methodology, Resources, Software, Validation, Writing – original draft, Writing – review & editing

Affiliation School of Economics, Fujian Normal University, Fuzhou, China

Roles Conceptualization, Resources, Writing – review & editing

Roles Conceptualization, Funding acquisition, Project administration

* E-mail: [email protected] (JFIL); [email protected] (HC)

Affiliation Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, P R China

Roles Formal analysis, Visualization, Writing – review & editing

Affiliation International College, Ulaanbaatar Erdem University, Ulaanbaatar, Mongolia

Roles Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

• Changping Yang, 
• Yongxing Xia, 
• Johnny F. I. Lam, 
• Hongxi Chen, 
• Huangxin Chen

• Published: May 17, 2024
• https://doi.org/10.1371/journal.pone.0299772
• Reader Comments

Tourism efficiency has become an important role in promoting tourism competitiveness and driving sustainable development. It is particularly important to identify and agnalyze the factors and mechanisms that affect efficiency. This paper firstly evaluates the tourism efficiency of 11 coastal provinces regions in China from 2010 to 2020 by using the DEA-BBC model that includes undesirable outputs. After that, it investigates the internal driving mechanism of the efficiency change through the Malmquist index and its decomposition. Finally, it analyzes the external influencing elements of tourist efficiency by the Tobit model. The results show that: (1) Although the average value of the tourism efficiency was changed from 0.727 to 0.707, it does not achieve the target. Its trend shows fluctuating from 2010–2020, which indicates that the tourism efficiency of most provincial regions is not optimal. The main factor that restricts tourism efficiency is scale efficiency. (2) By analyzing the dynamic trend, it is found that the average increase of technical efficiency is 14.0%, the average increase of technical change is 9.5%, and the average increase of MI index is 25.4%. It indicates that the overall tourism efficiency of 11 coastal provinces region in China is on the rise. (3) The spatial difference of tourism efficiency is significant, but there is no obvious spatial correlation. (4) The influencing factors of tourism efficiency are consumer demand, industrial structure, labor force and urbanization.

Citation: Yang C, Xia Y, Lam JFI, Chen H, Chen H (2024) Analyzing the tourism efficiency and its influencing factors of China’s coastal provinces. PLoS ONE 19(5): e0299772. https://doi.org/10.1371/journal.pone.0299772

Editor: Youssef El Archi, Abdelmalek Essaadi University: Universite Abdelmalek Essaadi, MOROCCO

Received: September 24, 2023; Accepted: February 14, 2024; Published: May 17, 2024

Copyright: © 2024 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant datasets used and/or analysed during the current study are within the paper.

Funding: This work was supported by the “GF Securities Social Welfare Foundation Teaching and Research Fund for National Finance and Mesoeconomics”, and the “Research Project of Macao Polytechnic University”.

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

1. Introduction

Tourism, a general term for enterprises and institutions that provide services for tourists or various activities related to tourists, plays an important role in promoting the economy. As we know, with the gradual progress of reform and opening, Chinese living standard has been significantly improved, and the tourism demand is constantly growing. The tourism industry has also been significantly developed. Now, China has become the largest tourism market in the world. The rapid economy and social development are benefited from tourism as a pillar industry. The tourism industry already accounted for 10% of China’s GDP. In 2019, the number of Chinese tourists exceeded 6 billion and the total tourism revenue exceeded 6 trillion yuan. It has also been suggested to increase the contribution of tourism to the economic development and quality of resident life [ 1 ]. We can see that tourism efficiency has received more attention with the upgrading of tourism. Tourism, as a modern service industry, has interactivity and influence on natural geographical and cultural resources. Therefore, more and more provinces generally focus on tourism efficiency and take advantage of their natural and cultural resources through promoting investment, transportation and star-related hotels. It has formed an extensive growth dominated by scale expansion.

The sample includes Liaoning, Hebei, Tianjin, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Guangxi, and Hainan. The tourism development status of 11 coastal provinces regions in 2020 is shown in Table 1 . It uses five indicators to measure the development status: the number of tourists (10,000 people), the number of star-rated hotels, the number of A-level scenic spots, the number of travel agencies, and tourism revenue (100 million yuan).

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https://doi.org/10.1371/journal.pone.0299772.t001

In China, when evaluating tourism efficiency, they always take all regions as a whole, or focus on a specific province. These are unfavorable development levels and uneven growth among regions. This research ignores the difference in development among regions, resulting in certain bias in the conclusions. As the most important economic regions, the GDP of these 11 coastal provinces region accounts for more than half of total GDP in China and it also leads to the development of the national economy. The tourism economy in these 11 coastal provinces region has been growing from 1978, based on excellent tourism resources and infrastructure. In view of the rapid economic growth, the provinces have adopted various measures to enhance their tourism competitiveness which relies on capital and labor input. Tourism also attracts more tourists, brings objective incomes which have reached 9.39 trillion yuan in 2019 and accounted for 22.18% of the total GDP. Even under the influence of the COVID-19, the tourism income also exceeds 10% of the total regional GDP. As developed regions, the transformation, upgrading, quality and efficiency improvement become critical factors to promote 11 coastal provinces’ tourism. But few researchers pay attention to tourism-developed areas. Most of the studies have only analysed phenomena and do not explore the cause. Even if some scholars do research on this, it is based on data from many years ago, and the research results are not timely. Comprehensive and effective measurement of tourism efficiency and exploration of strategies to improve tourism efficiency contribute much to facilitate the transformation of its development mode. The innovation of this paper is to focus on the tourism efficiency in these 11 coastal provinces region of China, with the highest level of tourism economy. By comparing the values from 2010 to 2020, this paper studying in 11 coastal provinces region shows the trends in tourism, to help people get a better understanding of the true picture of China’s tourism.

The main issues to be resolved in this paper include: (1) Constructing a provincial-level regional tourism efficiency evaluation index system to fully reflect the input and output of China’s tourism industry and the main influencing factors; (2) Revealing the present situation of tourism development in different parts of China and analyzing the influence of external environmental factors on tourism efficiency by determining the tourism efficiency of 11 coastal provinces regions in China from 2010 to 2020; (3) Studying tourism efficiency in these provincial regions. We can not only understand the allocation of tourism resources, but also understand how to utilize resource allocation to promote tourism’s development and expand the scale. Moreover, we can further and better understand the entire Chinese economy.

2. Literature review

As an economic phenomenon, tourism efficiency means that the inputs will bring a certain output in periods. The results are used to evaluate the sustainability of tourism. When the input is stable, the higher efficiency means the more output. The tourism efficiency of other famous tourist countries is often discussed. Many scholars pay more attention to tourism efficiency, focusing on travel agencies [ 2 – 4 ], tourist hotels [ 5 – 7 ], and tourist attractions [ 8 , 9 ]. For example, R and Widodo [ 10 ] discussed that the knowledge quality had positive influence on tourism’s competitive advantage. Morrison and Buhalis [ 11 ] provided insights on the differences among domestic and foreign markets, acknowledged that the supply sub-sectors of tourism were diverse and highlighted the variations by geographic regions. With a focus on the Indian subcontinent, Chowdhary and Prakash [ 12 ] explored various frameworks in relation to the tourism and hospitality industry. Álvaro, Quintero and Carol [ 13 ] discussed that heritage and tourism were strongly related to each other. Heritage gave rise to tourist attractions and activities, and tourism enhanced the designation of heritage sites. Todd and E [ 14 ] explored the daily experiences of local tourism workers in the expansion of the tourism industry. Gutberlet [ 15 ] explored the socio-cultural, economic, and spatial challenges faced in tourism development. Fuarros, Paiva and Calvo [ 16 ] took some examples such as a traditional Italian marketplace, a jungle park in Kuala Lumpur, a slum in the Colombian city of Medellín, or the "sun and sand" tourism destinations in Southern Spain, in order to affirm the significance of culture ambiance for tourist consumption. Koščak and O’Rourke [ 17 ] explained how the recent global events impacted on local tourism, such as the Covid-19 health crisis and the war in Ukraine. Raana [ 18 ] proposed and expounded a structural model that depicted the tripartite relationships among sense of place, attractions and satisfaction by using the data of experiences of a sample of 396 foreign tourists in Shiraz city, Iran. It showed the importance of tourist experiences in boosting the tourism industry and the importance of the attractions on tourist satisfaction. With the persistence of low labor productivity in tourism, Kim, Williams, Park and Chen [ 19 ] discussed that there was an urgent need to increase spatial spillover effects of agglomeration economies. Cuffy, Bakas and Coetzee [ 20 ] expounded how attractions, music festivals, events and wanderlust affected the tourism industry. Chaabouni [ 21 ] used DEA-model to investigate the tourism efficiency in China. The results showed that the tourism efficiency in China was low. At the regional level, the average tourism efficiency in east China was higher than central and west.

In China, Xing Fumin [ 22 ], Wang Zhaofeng [ 23 ], Deng [ 24 ] and other scholars focus their research on one certain province. For example, Dan, Xianzong, Fayyaz, Nabila and Zulqarnain [ 25 ] valued the tourism efficiency of Gansu Province, and then investigated the internal driving mechanism of the efficiency change. Wenhua [ 26 ] conducted some research on the tourism efficiency in Guangxi. The results showed that the improvement of technological progress was the most effective way to promote the efficiency growth of tourism in Guangxi. Yaobin, Meizhen, Kongming and Jinhang [ 27 ] analyzed the relationships between tourism efficiency and transport. It showed improving the spatial match of tourism efficiency and transport could enhance the sustainability of tourism development. While for other scholars, Dandan [ 28 ], Lu Xiaojing and etc [ 29 ], paid more attention to the dynamic changes of tourism efficiency in some regions of China. For example, Songsong, Tai and Jianchao [ 30 ] took the Yangtze River as a case to analyze the evolutionary process of regional tourism efficiency. Bin, Li and Li [ 31 ] measured the environmental pollution and tourism efficiency. It revealed the spatial difference between regional tourism efficiency and tourism scale was obvious, so environmental problems were raised. On the national level, some scholars such as Rui [ 32 ], Fang Yelin [ 33 ], Zifang, Jiaqi and Weiwei [ 34 ] and Yan, Yeqin [ 35 ] analyzed the tourism efficiency values of the whole 31 and cities in China from different perspectives. For example, Junli, Chaofeng and Sihan [ 36 ] used SBM-DEA model to measure the tourism efficiency of 30 provinces and analyzed the factors and mechanisms that affected efficiency. Zhiliang et al [ 37 ] discussed spatial–temporal heterogeneity and the related influencing factors of tourism efficiency in China. The results revealed that low-efficiency regions were mainly concentrated in northern China, while high-efficiency regions were concentrated in southern China. Zhaofeng, Qingfang, Jianhui and Yousuke [ 38 ] explored the evolution characteristics of the spatial network structure of tourism efficiency in China at the provincial level from the years 2011–2016.

To sum up, most scholars can use quantitative methods such as DEA model to estimate tourism efficiency. They do not only focus on a certain province, but also on a region or even the whole country. However, few scholars pay attention to the tourism efficiency of coastal areas. At present, there is not a complete and universal evaluation system in China. Combined with the views of the above scholars, this paper takes 11 coastal provincial regions in China as the research cases, uses DEA model to calculate their tourism efficiencies, and innovates the evaluation system of tourism efficiency to make it more in line with the goal of sustainable tourism development in China’s tourism industry.

3. Research design

3.1 methodology, 3.1.1 dea-bbc model..

Above all, scholars mainly use the DEA model to evaluate the tourism efficiency. DEA model, an important method to evaluate tourism efficiency at present [ 39 ], is an efficiency measurement method proposed by Charnes, Cooper, and Rhodes [ 40 ] in the 1970s. It is a linear programming model obtained by relevant theories of operational research under the assumption that the return to scale remains unchanged. In 1984, Banker et al. [ 41 ] proposed an efficiency measurement model with variable returns to scale, the BBC model, which decomposed the overall efficiency in the CCR model into pure technical efficiency and scale efficiency.

The DEA model is subdivided into input and output. Input-oriented refers to minimizing the required input variables and maximizing the output by controlling the weight coefficient of input variables under the given conditions of output. It is a non-parametric analysis method based on mathematical programming models. The characteristic is that decision making units can evaluate multi-input and multi-output indicators without estimating and testing parameters, so its conclusion has strong objectivity and scientific. DEA models are adopted in such as: Gu Jiang’s Appraisal and Model Foundation of the Efficiency in Tourism Production in China [ 42 ], and Liang Mingzhu’s An Evaluation and Analysis of Tourism Efficiency in Different Cities and Regions of Guangdong Province [ 43 ].

The results indicate the technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE). The relation between TE, PTE and SE is TE = PTE*SE. When TE = 1, if and only if PTE = 1& SE = 1, it indicates tourism is effective.

3.1.2 Malmquist index.

The TFP index from T period to T+1, is the change index of productivity. When TFP>1, it means that the productivity is increasing. When TFP<1, it means that the productivity is decreasing. When TFP = 1, it means that the productivity remains unchanged. TFP is decomposed into technical efficiency change index (TEC) and technical progress change index (TC), and TFP = EC*TC. It will help us to understand the relationship between various changes. When TEC>1, it indicates an improvement in relative technical efficiency and that a certain region is closer to the production frontier. When TC>1, it indicates progress in production technology. TEC can be further decomposed into scale efficiency change (SEC) and pure technical efficiency change (PTEC). If PTEC or SEC is greater than 1, it means that it has a positive effect on tourism efficiency.

3.1.3 Coordination model.

C ranges from 0 to 1. If the result is closer to 1, it indicates that tourism is more coordinated with the macroeconomy, and tourism is more sustainable. When C = 1, it means tourism is fully in line with the macroeconomy.

3.1.4 Tobit model.

In the expression, β is the unknown parameter estimator vector, x i is the explanatory vector and y i represents the tourism efficiency, which measured by DEA are [0,1]. And ε is the random error term.

3.2 Input-output variables

The DMUs need to have inputs in order to obtain corresponding outputs. Labor, capital and land are considered as the main driving force for economic growth. Compared with the primary and secondary industries, tourism is less dependent on land. So, scholars do not consider land factors when studying tourism efficiency. LSY Han estimates the use and preservation values of natural and/or cultural resources in five distinctive national parks. The empirical results show that natural and/or cultural resources of the sample national parks possessed considerable use and preservation values [ 49 ]. Michaela Stanickova, K Skokan used part of the Country Competitiveness Index (CCI) to competitiveness evaluation as input variables to analyse a degree of efficiency in Austria and Germany [ 50 ]. Hospitality is one of the key sectors in tourism. In order to attract customers, hotels must be competitive, so Aurélie Corne measures hospitality efficiency as an important aspect of tourism research [ 51 ]. Tourism expenses, number of employees and number of beds are used as input variables; tourism receipts, tourist arrivals and number of nights spent are used as output variables in HS Kurt’s study [ 52 ]. I Ili & I Petrevska used tourism expenses and the number of beds as input parameters, while using the number of arrivals, the number of nights spent and tourism revenue in 2016 as output parameters in order to determine tourism efficiency of Serbia and the surrounding countries [ 53 ]. Z Wang, S Xu take labor, assets, attraction of tourism resources and transport as the input indicator while arrivals of tourists and income of tourism as the output indicator to evaluate tourism efficiency in Zhangjiajie, China [ 54 ]. Fei Lu, HuaiGuo Ren take the number of direct working people, the attractiveness of the cultural tourism industry, technological progress and energy consumption as the input indicator while the added value of cultural industry and tourism revenue as the output indicator to evaluate the culture and tourism integration efficiency [ 55 ]. Moaaz Kabil used the length of shoreline, area, investments, quality of coral reefs, hotels number and accommodation capacity as input parameter, while using the employees numbers and tourist numbers as output parameter to estimate the efficiency of tourism centers in the Egyptian Southern Red Sea region for applying the blue economy conceptual kernel [ 56 ].

So, this paper also chooses input factors from the perspective of labor, capital and resources. Drawing on the previous research results, this paper innovatively chooses macro indicators to evaluate these three factors, so as to reflect the macro efficiency, among which: (1) The number of people employed [ 52 ] in the tourism industry is taken as labor vector. The fixed assets of the tourism industry [ 54 ] and passenger volume [ 57 ] are on behalf of capital. As for tourism resources richness, we give A-level scenic spots [ 49 ], star-rated hotel [ 57 ] and travel agencies [ 53 ] respectively, multiplied by the corresponding number. Above all is taken as input vectors. Total income of tourism and the number of tourists [ 54 ] are taken as output vectors. Based on the input-output theory, an evaluation index system for tourism efficiency in various provincial regions is constructed from the angle of economy and human utilization (shown as Table 2 ).

https://doi.org/10.1371/journal.pone.0299772.t002

Any slight change in either the source or the host destination can have a large impact on tourism demand, and the impact of major emergencies on the tourism industry is even more self-evident [ 58 ]. The outbreak of the COVID-19 has hit the pause button for the tourism industry. The three-year-long outbreak has also had a huge impact on tourism, adding to the uncertainty of the Chinese economy in transition. The tourism efficiency cannot be truly reflected. So, this paper uses the data for 2010–2020 as input-output variables. The data for each indicator are all official data from 2010–2020, the National Bureau of Statistics of China ( http://www.stats.gov.cn/ ).

4. Analysis of the empirical results

4.1 dea analysis.

This paper calculates the tourism efficiency of 11 coastal provinces regions in China by using DEAP2.1 software (shown as Table 3 ).

https://doi.org/10.1371/journal.pone.0299772.t003

The TE of 11 coastal provinces regions in China from 2010 to 2019 is: 0.727, 0.774, 0.784, 0.831, 0.838, 0.855, 0.954, 0.919, 0.926, 1.000. The overall efficiency of tourism in 2010 was effective in only 2 provinces, with an overall average efficiency of 0.727 and the lowest of 0.333 (Hebei). In 2019, overall provincial regions have reached the optimal level. It means the overall tourism efficiency is on the rise, except for the impact of the epidemic in 2020, which led to a decrease in tourists and revenue. Among them, the average TE has reached more than 85% of the optimal level. Though it indicates that the overall tourism efficiency is at a high level, it needs to be improved in the future. And the tourism efficiency of 11 coastal provinces regions is uneven. Guangdong, Shandong and Fujian have reached the optimal level, while Tianjin, Hainan, Guangxi and Hebei are lower than the average. And the “irs” indicates that the efficiency shows an increasing trend from 2010 to 2020. As a result, the tourism efficiency of 11 coastal provinces regions is showing a positive trend, and higher output can be obtained by increasing input. This also shows that tourism is developing as a good target.

The PTE of 11 coastal provinces regions in China is higher than TE. PTE has all reached the optimal level in most years from 2011 to 2020, and there is no obvious change in the urban pattern. As the regions open to the world, the overall provincial regions easily obtain more on advanced management and technology to promote the development of tourism.

The SE of 11 coastal province regions in China is slightly higher than the TE, while the PTE scatter mostly concentrates on a straight line, indicating that the change trend of the SE is consistent with the change trend of TE. It indicates the SE plays a leading role in the comprehensive efficiency, while PTE plays a supplementary role. There are significant differences among the 11 coastal provinces regions. Southern areas outperform the northern areas, and Hebei is lowest, indicating southern provinces have high utilization of tourism inputs. This is due to the differences in regional development patterns and tourism resources. Regions such as Hebei with a focus on industry are struggling to develop tourism. Hainan lacks cultural resources which are important in tourism. So this tourism does not achieve the optimal output. They need to expand production capacity and invest more human, material and financial resources to develop tourism.

4.2 Malmquist analysis

In order to evaluate the change of tourism efficiency accurately in overall provincial regions from 2010 to 2020, this paper applies the Malmquist index model to study the dynamic change trend (shown as Table 4 ).

https://doi.org/10.1371/journal.pone.0299772.t004

As for regional average, the technical efficiency change increased by 14.0%, and technical change increased by 4.8%. Such an increase prompts an increase of 25.4% in the MI index. The results show that the overall tourism operation efficiency of 11 coastal provinces regions has a growing trend. Comparing Tables 1 and 2 , it is easy to find that overall regions’ tourism efficiency shows increasing trend, indicating its high input-output efficiency level. It is proved that the tourism of the 11 coastal provinces regions has done relatively well in management and technology.

Guangdong, Jiangsu and Shandong all performed well, and the MI index ranked the top three among 11 coastal provinces regions, indicating that the tourism industry in these three provinces has achieved a high level in tourism management and development orientation. They have also reached the best level of efficiency in most years, because they enjoy unique advantages in tourism resource allocation. In particularly Guangdong outperforms tourism in other provinces, which takes tourism as the foundation and attracts a large number of high-level tourism talents.

Among them, the pure technical efficiency of Guangxi and Hebei shows a declining trend. This indicates these provinces have low input-output efficiency level. Hebei emphasizes industrial development over tourism investment, which makes up Hebei’s flaws in tourism technical efficiency. The tourism operation efficiency of Hebei is the worst, and the pure technical efficiency is only 0.982, which is at the lowest level in 11 coastal provinces regions. The reason why the MI of Guangxi is too low lies in the insufficient change of scale efficiency. Compared to other regions, Guangxi is in the central part of China, whose economy is underdeveloped. It relies on extensive growth, resulting in excessive waste of tourism. Its growth trend shows as V-shaped, indicating its tourism is not stable, and its development is greatly influenced by internal or external factors.

Overall, if Hebei or Guangxi wants to achieve higher output, it could not only improve resource utilization efficiency, but also need to maintain to bring its tourism operation scale back to the right track. Meanwhile, they also need to pay more attention to improving the management and technology.

4.3 Differences in tourism of 11 coastal provinces regions

In order to understand the trend of tourism efficiency among 11 coastal provinces regions in China, this paper selected the DEA data in 2010, 2015 and 2020, using ArcGIS 10.5 software to describe the time evolution of tourism in 11 coastal provinces. According to the rules, it is divided into 5 intervals: lowest, low, average, high, highest. The results are shown in Figs 1 – 3 :

https://doi.org/10.1371/journal.pone.0299772.g001

https://doi.org/10.1371/journal.pone.0299772.g002

https://doi.org/10.1371/journal.pone.0299772.g003

According to Figs 1 – 3 , the tourism efficiency of 11 coast provinces regions is varied in the period:

• In 2010, there were large differences among 11 provinces regions, and Guangdong, Shandong, and Liaoning had best value. With abundant resources and effective investment, they stand out.
• In 2015, Guangdong, Shandong and Liaoning were still ahead of the rest of the regions. In contrast, other regions had reduced tourism efficiency due to excessive investment, especially in Jiangsu, Fujian and Zhejiang. The difference in tourism efficiency among regions had increased, so the spatial pattern had been changing.
• COVID-19 has affected China’s tourism. With a high degree of marketization, 11 provinces regions had been hit, and the tourism efficiency decreased significantly. Especially in Guangdong and Liaoning, the tourism efficiency had fallen down to the bottom. Because the demand is declining, which leads to the input factors cannot meet the demand. But the tourism efficiency of Guangxi, Zhejiang and Hainan remained stable.

From the changes in the spatial pattern, it can be seen that the tourism efficiency of 11 provinces regions are not similar. If tourism efficiency improves, the gap between regions will narrow. Once the tourism efficiency is reduced, the imbalance between regions will widen.

This paper also draws the spatial distribution of mean MI of 11 provinces regions. The result is shown in Fig 4 . According to the numerical size, the mean MI are also divided into five parts. From the perspective of geographical distribution, Guangdong and Jiangsu are centers of 11 provinces regions, with highest MI. Fujian and Shandong, which are neighboring the two provinces, also have a high value. Due to the growth in scale efficiency, the MI of 11 provinces regions is above 1.00, indicating that it is all on an upward trend. Tourism is playing an active role. But we need to pay attention to Guangxi and Hebei because of the low MI.

https://doi.org/10.1371/journal.pone.0299772.g004

Locations have an impact on tourism. Because tourism has an agglomeration effect, a high level of regional economy will drive tourism efficiency. In order to clarify the difference of tourism efficiency, this paper also divides 11 provinces regions according to the analysis of regional distribution.

It can be found that the tourism in the Pearl River Delta Economic zone has the highest TE and its value is 0.866. The second one is the Yangtze River Delta Economic zone and its value is 0.865. Finally, the Bohai Economic zone is the lowest and its value is 0.8235 (shown as Table 5 ). The gap between each other is small and the degree of development is high.

https://doi.org/10.1371/journal.pone.0299772.t005

It can be drawn from Table 5 that the first one is the Yangtze River Delta Economic Zone. Its tourism infrastructure is well-established, and it also has a high level of urbanization with more abundant, concentrated natural and cultural landscape resources. Meanwhile, the industrial scale advantage of the Yangtze River Delta Economic Zone is obvious. The GDP of the Yangtze River Delta Economic Zone was 20.66 trillion yuan in 2020, accounting for 43.41% of overall 11 coastal provinces regions in China. The Yangtze River Delta Economic Zone has the most abundant resource, with the number of star rated hotels being 1186, accounting for 31.02%; the number of travel agencies was 7759, accounting for 38.39%; the number of A-level scenic spots was 1572, accounting for 28.6%; and the tourism revenue was 1920.9 billion yuan, accounting for 37.86%, the number of tourists was 1277.58 million, accounting for 31.92%.

The second zone is the Pearl River Delta Economic Zone. Its tourism benefits in terms of location, economic foundation, and resident income. The Pearl River Delta Economic Zone has advantages in convenient transportation. It does not only reflect on attracting national travelers, but also foreign travelers, indicating that the area has a large domestic and foreign market. The GDP of the Pearl River Delta Economic Zone had already exceeded 18.24 trillion yuan in 2020. accounting for 34% of overall 11 coastal provinces regions in China. The Pearl River Delta Economic Zone had perfect tourism facilities and resources too, with the number of star rated hotels being 1432, accounting for 37.46%; the number of travel agencies was 6177, accounting for 30.57%; the number of A-level scenic spots was 1567, accounting for 28.5%. and the tourism revenue was 1775.3 billion yuan, accounting for 34.99%. The number of tourists was 325.87 million, accounting for 33.13%.

The last one is the Bohai Economic Zone. As the birthplace of Chinese culture, it has a long history and the richest in tourism resources among three areas, with the number of A-level scenic spots being 2359 in 2020. At the same time, it is mostly located in plains with superior transportation conditions. There were also 6271 travel agencies and 1205 star-rated hotels, bringing a large number of tourists. Compared to the above two areas, its tourism revenue was only 1376.3 billion yuan in 2020, accounting for 27.13% of overall 11 coastal provinces regions in China.

In summary, the three zones are developed areas and the level of development is approximately the same. But the spatial difference of tourism efficiency is significant, southern areas outperform the northern area. Specific to certain provinces, these 11 coastal provinces regions have spatial differences too. In 2010, the provinces with optimum technical efficiency were only Guangdong and Liaoning. And the overall provincial regions are optimum in 2020, indicating Guangdong and Liaoning are the regions with stable tourism; Shandong, Fujian, Shanghai, Jiangsu, Tianjin and Zhejiang are relatively developed regions with higher technical efficiency; Hainan, Guangxi and Hebei are relatively underdeveloped regions, while these tourism developments are unstable. The proportion of 11 coastal provinces regions for developed, relatively developed and relatively underdeveloped is 2:6:3. The degree of development in 11 coastal provinces regions is mainly concentrated in relatively developed and relatively underdeveloped. Its spatial distribution shows an oval pattern, and there is no obvious spatial correlation.

4.4 Coordination analysis

The role of the economy in accelerating tourism is self-evident [ 59 ]. To better understand the tourism development in 11 coastal provinces regions, this paper calculates the coordination between tourism efficiency and macroeconomic in these 11 coastal provinces regions by using each provincial GDP growth rate to represent the macroeconomic (shown as Table 6 ).

https://doi.org/10.1371/journal.pone.0299772.t006

This is an interesting phenomenon. From the experience of the world, the tourism development trend is consistent with the macroeconomic with the increase in the proportion of the economy. But the results indicate that the tourism development and macroeconomic in 11 coastal provinces regions are not coordinated. There are three reasons: Firstly, the current situation has significant changes. The Chinese economy has been upgrading, and the government pays more attention to reform and quality. As a result, the traditional tourism industry is being replaced by the Internet, and growth is sluggish. At the same time, China and the USA have continuous frictions since 2016, which affects the international economy. Foreign tourism has been greatly impacted. And the COVID-19 has also hit tourism deeply, slowing down the economic recovery. Above all, tourism has affected efficiency. It shows the instability of tourism in 11 provinces’ regions. And, there are also spatial differences in 11 coastal provinces regions. The rapid economic growth in southern areas has promoted the development of tourism, presenting a higher level of coordination.

From Spatial Differences of the Coordination (shown as Fig 5 ), it presents a characteristic of "Intermittent sorting" in terms of coordination. Tourism in Fujian has the most coordination. Hebei, Jiangsu, Guangxi and Hainan have also been more coordinated with macroeconomic. As the reforms deepen, the overall area has a slower economic growth rate, which affects tourism development. And the overall provincial regions tend to be consistent. Comparing the mean, the Pearl River Delta Economic Zone with 0.22 is a little higher than the Yangtze River Delta Economic Zone with 0.21. And the Bohai Rim Economic Zone is the lowest with 0.19.

https://doi.org/10.1371/journal.pone.0299772.g005

Additionally, there is also a time difference. The tourism development had a higher degree of coordination with the macro economy, that transfers between the southern area and central area from 2010 to 2017. Then, the northern area began to develop into a highly coordinated dispatch system after 2017. At the provincial level, there are different trends: the degree of coordination has been declining in Tianjin, Shanghai, Fujian and Hainan from 2010 to 2020. Guangxi, Guangdong, Hebei and Shandong maintained an increase. Jiangsu, Zhejiang, and Liaoning remain stable, with Jiangsu, Zhejiang is at a high level, while Liaoning is at a low level (shown as Figs 6 and 7 ).

https://doi.org/10.1371/journal.pone.0299772.g006

https://doi.org/10.1371/journal.pone.0299772.g007

To sum up, although the regional tourism development gap in 11 coastal provinces regions is narrowed, the overall tourism efficiency shows a trend of differentiation, and the spatial distribution also shows an uneven distribution.

With the central provinces as the core, the spatial coordination in neighboring provincial regions should be expanded to form a tourism concentration zone. The neighboring provincial regions can rely on the proximity advantage, actively develop cross-border tourism and realize the integration of tourism. At the same time, enhancing the convenience of transportation infrastructure and industrial cooperation will help increase the tourism coordination [ 60 ].

4.5 Driving factors analysis

The driving factors of efficiency mainly include: resources, infrastructure, location, human support, economic, industrial structure, urbanization, informatization, marketization, openness, policy and so on. The factors varied between studies, most scholars focus more on the driving factors of a certain sub-industry. For example, Buhalis observe the main changes in e-Tourism, analyzing the strategic lines that are driving its evolution. He expounded on the significance of linking information and tourism [ 61 ]. Figueroa examined Chile as a case study, a country with a growing number of tourists and increased investment in tourist and cultural infrastructures. Empirical results show that cultural endowments and activities together with natural resources determine Chilean regional efficiency in optimizing tourist flow [ 62 ]. Using the West Coast of the Strait urban agglomeration, China, as an example, Y Li uses DEA to analyze the nonlinear relationship between tourism economic contact intensity and tourism industry efficiency by constructing a mixed effect model. The result shows that the regional economic level harms the efficiency of the tourism industry. And the urbanization level has a positive effect on the efficiency of the tourism industry [ 63 ]. The literature review is shown as Table 7 .

https://doi.org/10.1371/journal.pone.0299772.t007

Based on the analysis of the literature, this paper selects economic level, consumers and demand, urbanization, general tourism wages and the degree of opening up as the influencing factors to construct the model. (1) Economic level. GDP is an important indicator of the economic development level of a certain province. The high level of economic development can promote the development of tourism. It provides more financial funds for the construction, and also brings more talents, promoting the sustainable development of tourism. So this paper uses GDP as a driving factor of tourism efficiency. (2) Consumers and demand. The consumer demand is an important driver for tourism’s development. With incomes rising, there is more demand for travel, leading to the increase in factor input, the improvement of the industrial sector, and the enriches the tourism experience. (3) Urbanization. Urbanization is the positive factor for promoting the development of the services industry. Urbanization can drive regional economic growth, promote the development of the service industry and enhance the level of marketization. There is also a clear impact on tourism. providing financial, labor, technical and policy support. (4) General tourism wages. Tourism is a labor-intensive industry, and the demand for labor is large. So, labor is an indispensable influencing factor in tourism. Not only the number of labor, but also the quality of labor, the demand for labor in tourism is comprehensive. High-quality labor plays an irreplaceable role in improving efficiency and promoting tourism. General tourism wages can represent the attractiveness of the labor force, and can also reflect the whole picture of tourism. (5) The degree of opening up. The level of opening up is a key factor in the regional economy. As an important part of the service industry, tourism will accelerate its development with the level of opening. The level of opening up plays an important role in the regional economy, and this role will also have an impact on tourism, attracting tourism talent and technology, so as to improve tourism efficiency.

According to the measurement results of DEA efficiency, the comprehensive technical efficiency is taken as the interpreted variable. And the restricted dependent variables are divided into five parts, including economic level, consumer and demand, urbanization, general tourism wages and the degree of opening up. The overall data is derived from the websites of the National Bureau of Statistics (shown as Table 8 ).

https://doi.org/10.1371/journal.pone.0299772.t008

Economic level, consumer and demand are positively correlated with tourism efficiency. Among them, consumer and demand have more significant and positive impacts on tourism efficiency than economic level. The development of tourism is a weather vane for a better life [ 67 ]. And consumption upgrading of tourism meets the needs for people’s better lives and drives people’s demand in return. As the economic level improves, and residents’ incomes rise, the consumer demand for tourism is the major factor in tourism’s economic vitality. At present, policies targeting the stimulation of tourism consumption have been introduced across China. The demand for tourism is also growing rapidly and the tourism market is sufficient. Now, the government pays more and more attention to tourism, which drives tourism as a “head industry [ 68 ]” and gives full play to the huge value of tourism. The importance in the industrial structure has been highlighted. But the degree of opening up only has a slight correlation with tourism efficiency. The reason is that the 11 coastal provinces regions have a high level of opening up, and there is no difference in talent and technology, so the impact cannot be significant.

Urbanization is significantly negatively correlated with tourism efficiency. It can be said that the promotion of urbanization has improved tourism construction with high return, and high returns can absorb more capital investment. But large-scale investment in urbanization may result in excessive waste of tourism resources and flawing in tourism efficiency. To avoid homogenization of tourism, each provincial region should make use of the differences in resources, actively innovate in the period of demand increasing, and improve tourism efficiency.

General tourism wages cannot pass the test. The labor force is the basic driving factor of tourism. This could be that tourism has a large number of employees, but the low level of knowledge of employees has a negative impact on tourism efficiency and general tourism wages are also affected by many factors, which cannot represent the real level of labor force quality, so it is impossible to evaluate efficiency.

5. Conclusion

In this paper, the DEA model is used to measure and analyze the tourism efficiency in 11 coastal provinces regions from 2010 to 2020, and the following conclusions are drawn.

• This is different from others that the tourism efficiency of 11 provinces regions is not optimistic. As developed regions, the tourism efficiency of 11 provinces regions reached optimal value of 84.68% from 2010 to 2020. Although, it showed a rapid growth trend before 2019. While on the impact of the COVID-19, the scale efficiency declined suddenly and it led to low productivity. This indicates that there is a certain instability in tourism. And the pure technical efficiency in each province stays stable that is closed to the optimal level. The change trend of scale efficiency and comprehensive efficiency is roughly the same. So this paper finds the scale efficiency dominates the value of the efficiency. If the 11 coastal provinces regions expand the tourism scale, the tourism efficiency will be significantly improved.
• By analyzing the dynamic trend from 2010 to 2020, it is found that the average increase of technical efficiency is 14.0%, the average increase of technical change is 9.5%, and the average increase of MI index is 25.4%. These values remain at a relatively high level. This indicates that tourism of 11 coastal provinces regions has a growing trend and has done relatively well in management and technology. We find that it plays a more important role in driving the growth with the continuous improvement of technology. The reason is that overall 11 coastal provinces regions which are located in a coastal area are able to play the talent effect, and also have advantages in technology application and development.
• This paper also finds that the spatial difference of tourism efficiency is significant. This means that there are disparities even within developed regions. The overall tourism efficiency of 11 coastal provinces regions in China is on the rise. But due to the differences in conditions, resources, and investment, there is also a significant difference in scale efficiency among them. There is a coordinated relationship between tourism efficiency and macroeconomy, which showed a downward trend from 2010 to 2020. The southern area is better than the northern area. Some provinces such as Guangdong are close to the optimal, while Fujian, Shandong, Liaoning, and Jiangsu also achieve high input-output level. Few provinces have poor performance, especially for Hebei is lowest, its average scale efficiency is only 63.15%, restricting its tourism efficiency. After analyzing the data samples, it was found that there is no obvious spatial correlation, which is shown as an "elliptical" pattern.
• The most important conclusion distinguishes from others: tourism efficiency is deeply affected by consumer and demand. Economy level, consumer and demand and the level of opening up are positively correlated with tourism efficiency. Consumer demand has a more significant positive impact on tourism efficiency than economy level and the level of opening up. The growth of consumer demand will expand the market scale and improve tourism efficiency. Urbanization has a negative impact on tourism. The promotion of urbanization may result in excessive waste of tourism resources, and the low level of knowledge of employees restricts tourism efficiency.

Based on the above analysis and empirical results, the following suggestions are proposed:

• To promote the development of tourism at an appropriate scale. The low scale efficiency restricts the development of 11 coastal provinces’ tourism regions. Therefore, the input of tourism should be expanded, including travel agencies, star-rated hotels and other tourism resources, so as to increase the scale of the tourism. But the 11 coastal provinces regions should rationally adjust input-output resources according to their own conditions in order to avoid homogenization of tourism. Once investment is easily increased, it may result in excessive waste of tourism and restrict tourism development in return.
• Economic capacity for development should be cultivated, and efforts should be made to raise the income level of residents and provide economic support for the development of tourism. The reason for the regional differences of tourism efficiency in these 11 coastal provinces regions is the unreasonable allocation and utilization of resources. The transformation of tourism is the requirement for high-quality development. Overall, provincial regions should dig deeper into tourism resources, explore more potential of tourism resources, and improve the tourism appeal [ 69 ]. This will bring more consumer demands.
• It is necessary to innovate technological and management. The development of science and technology also contributes a lot to the improvement of tourism efficiency. The 11 coastal provinces regions should improve technological and management with integrate and optimize resources, driving the development of tourism towards innovation. Technology in digitalization, internet, statistics and artificial intelligence should be introduced to effectively improve the management ability and improve the satisfaction of tourists. And the government can better tap into resource potential, draw on various tourism festivals to create multiple distinctive tourism routes and develop targeted tourism creative products based on the connotation characteristics of each tourism resource [ 70 ].
• Accelerating institutional and regional integration, integrating and optimizing resources to promote the deep development of tourism. The tourism industry is open, and it is also a necessary choice to strengthen regional synergy. At present, the tourism market is gradually transitioning to industrial integration. The regional correlation of tourism is strong. Overall, 11 coastal provinces regions that have tourism resources with great local characteristics have advantages in urbanization, transportation, and conditions for tourism integration. They should improve institutional synergy, give support to the integration of tourism, culture, sports, music, and other related industries, and achieve coordination within industrial regions.
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Largest tourism trade show in Canada is in Edmonton this week

The largest tourism trade show in the country, Rendez-vous Canada, is in Edmonton this week for its annual conference, connecting tourism partners from across Canada and the world.

From May 14-17, more than 1,500 people in the tourism industry will descend on the Edmonton Convention Centre, creating partnerships for Canadian tourism for the next few years. Explore Edmonton’s vice-president of destination development and marketing, Paul Hawes, discussed the importance of the event and what it means for Edmonton tourism.

“This is really one of those once-in-a-decade opportunities to showcase the destination,” said Hawes.

What is Rendez-vous Canada

Rendez-vous Canada is a trade show for the tourism industry. It’s a joint venture between Destination Canada and the Tourism Industry Association of Canada (TIAC).

Destination Canada works to market Canada as a four-season destination for visitors within the country and abroad. The group identified a few key areas of the world where Canada’s tourism is an attractive option for travellers, including those from Australia, China, France, Germany, Japan, Mexico, South Korea, the United Kingdom and the United States.

The Tourism Industry Association of Canada is the nation’s largest national tourism advocate established in 1930.

RVC runs annually, but it’s been 10 years since Edmonton last hosted the event, which Explore Edmonton is looking forward to capitalizing on. In the last decade, Hawes discussed how much Edmonton has changed, highlighting the Downtown core and the Ice District as two shining examples.

Over the next few days, Edmonton will be home base for a broad range of tourism-affiliated people and businesses, putting the city in a strong position to not just reap the direct economic benefits of the show estimated at $5.3 million, but also set the course for long-term tourism attraction for years to come.

“It’s a much longer story than just the immediate impact,” said Hawes.

While Edmonton may get some of the benefits this week, RVC is mostly about tourism buyers and sellers.

• Explore Edmonton granted one-time $6 million cash infusion from reserve funds
• Photos: Explore Edmonton launches Indigenous Tourism Development Strategy

The buyers could be from Canada or another country. With more than 1,500 people attending, there is a wide variety of countries represented in the buyer pool. The buyers, Hawes said, are a mix of product managers and travel agents.

Their jobs include assembling itineraries and travel packages for customers. Some may be assembling packages for tourism within Canada, while others might be looking to create an experience for visitors from another country.

Over the last few days and running until the end of the show, RVC offers familiarization tours for buyers.

“We want these buyers to experience products and experiences first hand,” said Hawes.

The sellers

The sellers are nominated by the TIAC, Destination Canada, and tourism industry partners. Essentially, the sellers offer everything that the buyers need to put together a travel package. From hotels to airlines or restaurants to experiences, the sellers have it all.

International buyers come and go frequently, but Hawes said that the benefit of an event like RVC is that it gets everyone in the same room at the same time.

With 917 sellers to choose from, Hawes said there were already more than 50,000 meetings set up between the buyers and sellers.

“Tourism speed dating is the best way to describe it,” Hawes said.

Effects on Edmonton

Apart from the direct revenue injected by attendees of the shows — as the buyers and sellers come and go, creating partnerships for future tourism, Hawes said that Edmonton sits at the center of it, both literally and figuratively.

While here, Hawes highlighted the Edmonton river valley, Fort Edmonton Park and the Alberta Art Gallery as a few key attractions within the city that tourists can take in. But he also said that Edmonton can benefit by acting as a “harbour” for some of Alberta’s other attractions.

As the buyers head to and from their tours, they will be going through Edmonton. As such, Hawes said they get the chance to see not just what Edmonton has to offer, but the access it offers to other places like Banff and Jasper, which would put Edmonton in a central arterial role in Alberta tourism.

RVC will wrap up with a social night on Thursday in the Ice District while the Oilers battle against the Vancouver Canucks , showcasing the changes in the city from when the show was last in Edmonton 10 years ago.

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Twitter/X: @ZacharyDelaney