Revealing the Effectiveness of Tourism Development on Health in Asian Economies

Most Asian economies consist of tourism attraction destinations. The traditional literature explores the cultural, social, and economic effects of tourism; thus, there exists a vacuum related to the impacts of tourism development on the human health of local people. Hence, the current study examines the impact of tourism development on health outcomes of the tourism-based selected Asian economies. The panel autoregressive distributed lag (ARDL) methodology is used to deduce the short-run and long-run impacts of tourism development on health outcomes. The results disclose that tourism activities insignificantly influence health outcomes in the short run. However, tourism development brings improvement in health outcomes in the long run. This research offers a new approach highlighting the significance of tourism development for human health and emphasizes the importance of tourism development for destination management and marketing.

Introduction

Tourism development plays an imperative role in several countries' cultural, social, and economic progress. UNWTO (2020) recorded ~US$1.5 trillion global tourism expenditures and 1.5 billion tourist arrivals during 2019. The economic benefits of tourism include job creation, expansion of local economies, and tax revenues generation (1, 2). Meanwhile, the negative effects of tourism mostly offset its benefits due to environmental, cultural, and social issues (3, 4). Most recently, the COVID-19 pandemic negatively impacted traveling on public health. COVID-19 pandemic led to closures of borders, cancelation of flights, and limitation of movement of people (5). There are various possibilities of the positive effects of tourism on human health through positive social and emotional interactions between residents and tourists (6). Tourism activities are inseparable from the emotions of tourists and can significantly influence longevity and human health (2, 7). Literature suggests that positive emotions lead to a lesser chance of cardiovascular issues, headaches, congestions, inflammation, and weakness (8). Positive emotions also result in following health outcomes such as good quality sleep, healthy eating, and exercise (9, 10).

Positive emotions can alleviate harmful memories of stressful events of life by producing optimism, endurance, and resilience (11). Thus, the nexus between tourism and health outcomes needs a thorough examination. Literature discloses that social interactions directly influence the health outcomes of people (12). Tourism involves connections between residents and tourists that lead to positive emotional experiences for both (13). Additionally, tourism activities imply combined reinforcement of positive emotions and social interactions that lead to positive health outcomes (14). Prevailing literature reports positive impacts of tourism development on tourists and residents (13). Few studies have reported the effect of tourism development on the wellbeing of residents, quality of their life, and satisfaction of life through objective and subjective indicators (15).

Our research particularly focuses on the association between tourism development and health outcomes. Tourism development affects the health outcomes of a country through direct and indirect channels (16). The direct impact is described as the pragmatic tourism development, and the tourism practices are mostly described as the outcomes of tourism activities (17). Tourism activities produce positive and pleasant emotions that increase wellbeing and health outcomes (18). Various studies highlighted the importance of examining the nexus between tourism activities and positive emotions and suggested developing the tourism field to improve human health (19). Followers of tourism also stress the significance of association between residents and tourists in the context of places, self-improvement, social context, and communities (13). Some studies reported positive emotions generated from social contexts and tourism activities (13). Tourism activities positively affect the interactions between tourists and residents (20). However, the tourism-related positive impact on residents' wellbeing and health outcomes has not been explored yet (21).

A bulk of psychology literature also suggested that tourism positively influences mental health, longevity, and physical health (22). Some studies report that those positive emotions cause lesser cold complaints, reduced inflammation, and less chance of cardiovascular diseases (23). The study done by Zsarnoczky (24) argued that positive emotional episodes reduce congestion, weakness, headaches, and chest pain. Positive emotions episodes also improve sleeping quality, balancing stress hormones, and healthy eating (25). Tourism activities generate positive emotions that develop optimism, endurance, and resilience (26). In brief, tourism activities generate positive emotions that positively influence health outcomes and wellbeing (27). Salehi-Esfahani et al. (28) reported that the tourism industry increased Canada's health- spending, which indirectly improves health outcomes. The study of Dwyer (29) noted that tourism development significantly impacts resident human wellbeing by improving capital stocks and green infrastructure (30).

Existing literature reveals that there exists a positive association between income and physical and mental health (31). It is argued that healthy people have greater employment opportunities and high wage rates that lead to high-income levels. Previous literature reported the positive impact of tourism on the economy (32). Tourism results in increasing income levels of residents (1). Hence, income is directly associated with residents' psychological and physical health (33). In the light of this background, the present study examines the impact of tourism development on health outcomes in Asian economies (34). The sample of the study is chosen based on the highest tourism attractions (27). This study has important empirical and theoretical contributions. This study makes a theoretical contribution by evaluating tourism's long-run and short-run outcomes (34). Previous studies focused on the short-run outcomes of tourism activities (35). However, long-term influences of tourism have not been explored in literature. Thus, our study employs panel ARDL for extracting the long-run and short-run impact of tourism development on human health outcomes (36). This study inspects the effects of tourism development on health for Asian nations over the period 1992–2019. This study also assesses the transmission channels between tourism development and health (37). The study delivers policy implications by suggesting that policymakers should distinguish the significance of tourism development with respect to the health outcomes of resident people.

Model And Methods

The tourism industry plays a vital role in many nations' social, economic, and health development. Tourism development has a direct and indirect impact on health. Tourism-health nexus is the fastest growing research debate internationally in the recent era. Following the studies of (author?) (38, 39), we explore the effects of tourism and other control variables on health in Asian countries; the econometric model framework in the study is as follows:

$\begin{array}{l}{\text{Health}}_{\text{it}}\text{=}{\alpha }_{\text{0}}\text{+}{\alpha }_{\text{1}}{\text{Tourism}}_{\text{it}}\text{+}{\alpha }_{\text{2}}{\text{GDP}}_{\text{it}}\\ \text{ + }{\alpha }_{\text{3}}{\text{HE}}_{\text{it}}\text{+}{\alpha }_{\text{4}}{\text{Internet}}_{\text{it}}{\text{+μ}}_{\text{it}}& (1)\end{array}$

Where, Health_it is the health outcome dependent on tourism development (tourism), GDP per capita (GDP), health expenditure (HE), and internet users (Internet), and μ_itis the error term. While, i is the country dimension (i = 1, 2,…5) and t is the time periods (t = 1992, 1991, …, 2019). Lastly, α₀represents the constant term in the equation. Tourism development has direct and indirect potential impacts on health; thus α₁ will have a positive effect on health. Expectedly, α₂ has a positive effect on health. Similarly, both α₃ and α₄ are expected to carry positive coefficients. Estimates of Equation (1) by OLS will yield long-run impacts only of all the four exogenous variables on health outcomes (40). Short-run impacts are generally inferred by estimating an error-correction equation. Thus, we can rewrite Equation (1) in an error-correcting format for short-run estimates as follows:

$\begin{array}{l}\Delta {\text{Health}}_{\text{it}}\text{ = }\pi \text{+}{\displaystyle \sum _{\text{p=1}}^{\text{n1}}{\pi }_{\text{1p }}}\Delta {\text{Health}}_{\text{2},\text{it-p}}\text{+}{\displaystyle \sum _{\text{P=0}}^{\text{n2}}{\pi }_{\text{2p }}}\Delta {\text{Tourism}}_{\text{it-p}}\\ \text{ +}{\displaystyle \sum _{\text{p=0}}^{\text{n3}}{\pi }_{\text{3p }}}\Delta {\text{GDP}}_{\text{it-p}}\text{+ }{\displaystyle \sum _{\text{p=0}}^{\text{n4}}{\pi }_{\text{4p }}}\Delta {\text{HE}}_{\text{it-p}}\\ \text{ + }{\displaystyle \sum _{\text{p=0}}^{\text{n5}}{\pi }_{\text{5p }}}\Delta {\text{Internet}}_{\text{it-p}}\text{+ }{\beta }_{\text{1}}{\text{Health}}_{\text{it-1}}\\ \text{ + }{\beta }_{\text{2}}{\text{Tourism}}_{\text{it-1}}\text{+ }{\beta }_{\text{3}}{\text{GDP}}_{\text{it-1}}\\ \text{ + }{\beta }_{\text{4}}{\text{HE}}_{\text{it-1}}\text{+ }{\beta }_{\text{5}}{\text{Internet}}_{\text{it-1}}\text{+}{\mu }_{\text{it}}& (2)\end{array}$

Equation (2) is driven by Pesaran et al. (41), who include the short and long-run impacts of tourism development, GDP, health expenditure, and internet users on health outcomes. The key advantage of this approach is that the long-run and short-run estimates can be measured in one step (42). The short-run impacts are reflected in the estimates attached to π_1p, π_1p, π_1p, π_1p, and π_1pvariables. While long-run impacts are inferred by the estimates of β₁, β₂, β₃, β₄, and β₅. Pesaran et al. (41) propose that two cointegration tests are applied. There is no requisite for unit-root testing in this approach because macroeconomic variables could be a mixture of I(0) and I(1), which is another key benefit of this approach. We used first- and second-generation panel unit root tests to check the unit-root process of the variables. This approach is captured the dynamic impacts of tourism development on health at different lag orders (43). ARDL-PMG extends the ARDL method (44), which is a cointegration approach of panel data. Thus, the next section is reported the short and long-run results of health outcomes.

Data

The study explores the impact of tourism development on health outcomes of top tourism-based Asian economies. These economies include China, Thailand, Japan, Malaysia, and India. Our sample covers five Asian countries over the period 1992–2019. Table 1 displays the information regarding symbols and definitions of variables and sources of data. The health outcome is determined by life expectancy at birth (in total years). The focused variable, tourism development, is measured by two proxy variables: tourist receipts and tourist arrivals. Tourism receipts are taken in terms of current US$ while tourist arrivals are taken in terms of the total number of arrivals. This study's control variables are GDP per capita, health expenditure, and internet users. GDP per capita is taken in terms of constant 2015 US$. Health expenditures are determined as percent of GDP. Internet uses are taken as a percent of the population. The data for all these variables have been obtained from the World Bank. TR, TA, and GDP variables are expressed in logarithm form. Table 2 contains descriptive statistics for all of the variables.

TABLE 1

Table 1. Variables definition and sources.

TABLE 2

Table 2. Data description.

Results and Discussion

As the study is dealing with panel data, panel unit root tests have been employed to detect the integration properties of data. LLC, ADF, and IPS panel unit root test approaches have been employed for performing this task. Table 3 reports the results of these panel unit root tests. The LLC test reports that LE, TR, and Internet are I(0) integrated variables, while TA, GDP, and HE are I(1) integrated variables. In the ADF test, it is found that LE and TA are integrated at I(0). However, TR, GDP, HE, and Internet are integrated at I(1). The results of the IPS model report that all variables are integrated at I(1) except TA, i.e., integrated at I(0). Based on the results of unit root testing, the study used the PMG-ARDL approach for empirical analysis. Table 4 provides the human health model's long-run and short-run coefficient estimates.

TABLE 3

Table 3. Panel unit root testing.

TABLE 4

Table 4. Long and short-run estimates of human health.

In the PMG-ARDL model, the long-run findings report a positive and significant impact of tourist receipts on human health. These findings reveal that tourism development can improve the human health of the local public in selected Asian economies. The coefficient estimates disclose that a 1% rise in tourist receipts improves human health by 1.066% in the long run.

Our findings report that tourism development has long-term impacts on the human health of local people. In the long-run, tourism development improves the health outcome. However, tourism development has an insignificant impact on human health in the short-run. The insignificant impact in the short-run can be justified as the tourism development results in overcrowding traffic congestions and escalates the crime level that may negatively impact human health and increase the level of stress (39). The long-run positive association between tourism development and human health is supported by Coghlan (33), who stated that tourism development increases social interactions that influence longevity and physical health. The long-run impact of tourism on longevity is further supported by the following studies, e.g., (18). Literature also argued that social associations lead to premature mortality and lower morbidity (13). Traditional researchers estimated the impact of tourist arrivals through inbound overnight stays, new jobs, taxes, and tourism expenditures (35). However, most recent studies argued that it could be more important to examine the health effects as tourist receipts bring positive change in the income levels of local people (3). Another study justified our findings by claiming that tourism accompanied by higher income provides better health-related opportunities to local people (45). Lenhart (45) also argued that tourism improves health outcomes up to a specific level after that, the negative short-term impacts related to stress level outweigh the long-term impacts in the form of positive social interactions and emotions.

GDP is significantly and positively associated with human health in the long-run. It is reported that a 1% upsurge in GDP tends to improve human health by 0.397% in the long-run. The impact of health expenditure is found significant and positive on health outcomes in the long-run. The results show that a 1% escalation in health expenditure improves health outcomes by 0.560% in the long-run. The use of the internet reports a significant and positive impact on health outcomes in the long-run. The findings disclose that 1% intensification in internet use improves health outcomes by 0.102% in the long-run. The findings report a statistically insignificant association between tourism development and human health in the short-run. The impact of GDP is significant and positive on human health in the short-run depicting that improvement in GDP tends to improve health outcomes. In the short-run, an increase in health expenditure significantly and positively increases human health. Lastly, the use of the internet also exerts a significant and positive impact on human health in the short-run.

In the variable-based robustness testing, the long-run findings disclose that tourist arrivals are positively and significantly linked with human health. It demonstrates that a 1% rise in tourist arrivals increases human health by 0.903% in the long-run. The study also tests the robustness of said association by employing the CS-ARDL technique. In the CS-ARDL model, long-run findings display a significant and positive association between tourism development and human health. It is found that a 1% escalation in tourist receipts tends to improve human health by 1.448% in selected economies. Thus, it is concluded that tourism development significantly promotes human health in selected economies. There exists no association between tourism development and health outcome as depicted by statistically insignificant coefficient estimates in both variable-based and technique-based robustness models. The findings of control variables are consistent in terms of sign and magnitude in both variable-based and technique-based robustness models. The findings of the error correction model confirm the existence of a long-run cointegration association among variables. The statistically significant and negative coefficient estimate of the ECM term is 0.451 in a basic model, which reveals that almost 45% convergence will be achieved in 1 year.

Conclusion And Implications

According to World Health Organization (WHO), health is not just the absence of illness but is a complete physical, mental, and social wellbeing of the people. Previous literature on the determinants of health quality has highlighted various socio-economic factors (e.g., per capita income, health expenditures, environment, etc.) that can affect people's mental and physical wellbeing. However, not many have considered the role of tourism as a determinant of health status. Literature suggests that a rise in tourism directly or indirectly improves health status. For instance, the rise in tourism increases the income of the local people and gives them more employment opportunities which improves their health status. According to some evidence, tourism can also directly and positively affect tourists' physical and mental health. Hence, we aim to analyze the relationship between tourism and health status in Asian economies.

Results from the Panel-ARDL model confirm the positive impact of tourism receipts and tourism arrival on life expectancy in the long run. Similarly, the estimated coefficient of tourism arrival is positive and significant in the robust model as well. In general, our findings imply that a positive change in tourism development promotes the health status in polluted economies. Among the control variables, the estimated coefficients of GDP, HE, and the Internet are positively significant in all three models confirming the positive impact of affluence, health expenditures, and internet on life expectancy. In the short run, the results are unconvincing and mixed.

Based on these outcomes, we can provide some important policy implications for the concerned stakeholders. For instance, tourism can improve the health status of the people living in Asian economies; therefore, policymakers can use tourism as a tool to enhance the life expectancy of the people. Development of tourism facilities such as hotels, restaurants, roads, and infrastructure around the tourist spot can attract more tourism which will upgrade the health status of the people. Tourism development brings earnings and employment opportunities for the residents of the area. As a result, a rise in income of the local people allows them to enjoy more modern health care facilities, which will improve the health status of the people. Further, the policymakers should focus on building healthcare facilities near the important tourist spot that not only improves the local people's health status but also attracts more tourists to the area, which will further improve the life expectancy of the people. Integrating tourism development policy into health policy can bring improvement in both the health and tourism sectors.

Even though the contribution of the study to the existing literature is significant. However, a few of the shortcomings of the study are also there. For instance, the number of countries chosen for the analysis is few, which can be enhanced for more comprehensive inference in future studies. The analysis has only used one proxy of health status, i.e., life expectancy; however, there are other health status indicators such as infant mortality rate, mother's mortality rate, etc. Future studies should focus on other proxies of health status while analyzing the tourism-health status.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: https://data.worldbank.org/.

Author Contributions

YS and Z-WS: conceptualization, software, data curation, and writing-original draft preparation. RT: methodology, writing-reviewing, and editing. AU: visualization and investigation. All authors contributed to the article and approved the submitted version.

Funding

The study is supported by Study on the radiation effect of tourism industry in Shandong Province under the transformation of old and new kinetic energy (Grant no. 2019RW07).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Gursoy D, Ouyang Z, Nunkoo R, Wei W. Residents' impact perceptions of and attitudes towards tourism development: a meta-analysis. J Hosp Mark Manag. (2019) 28:306–33. doi: 10.1080/19368623.2018.1516589

CrossRef Full Text | Google Scholar

2. Jordan EJ, Spencer DM, Prayag G. Tourism impacts, emotions and stress. Ann Tourism Res. (2019) 75:213–26. doi: 10.1016/j.annals.2019.01.011

CrossRef Full Text | Google Scholar

3. Dodds R, Butler R. The phenomena of overtourism: a review. Int J Tour Cities. (2019) 5:519–28. doi: 10.1515/9783110607369

CrossRef Full Text | Google Scholar

4. Garau-Vadell JB, Gutierrez-Taño D, Diaz-Armas R. Economic crisis and residents' perception of the impacts of tourism in mass tourism destinations. J Destin Mark Manag. (2018) 7:68–75. doi: 10.1016/j.jdmm.2016.08.008

CrossRef Full Text | Google Scholar

5. Wang K-H, Su C-W. Asymmetric link between COVID-19 and fossil energy prices. Asian Econ Lett. (2021) 1:18742. doi: 10.46557/001c.18742

CrossRef Full Text | Google Scholar

6. Frontiers | Does Economic Overheating Provide Positive Feedback on Population Health? Evidence From BRICS and ASEAN Countries | Public Health. Available online at: https://internal-journal.frontiersin.org/articles/10.3389/fpubh.2021.661279/full (accessed January 25, 2022).

Google Scholar

7. Filep S, Pearce P. 13 A blueprint for tourist experience and fulfilment research. Tour Exp Fulfil. (2013) 223. doi: 10.4324/9780203134580

CrossRef Full Text | Google Scholar

8. Stokes R. Tourism strategy making: insights to the events tourism domain. Tour Manag. (2008) 29:252–62. doi: 10.1016/j.tourman.2007.03.014

CrossRef Full Text | Google Scholar

9. Alexis P. Over-tourism and anti-tourist sentiment: an exploratory analysis and discussion. Ovidius Univ Ann Econ Sci Series. (2017) 17:288–93. Available online at: https://stec.univ-ovidius.ro/html/anale/RO/2017-2/Section%20III/25.pdf

Google Scholar

10. Pyke J, Pyke S, Watuwa R. Social tourism and well-being in a first nation community. Ann Tour Res. (2019) 77:38–48. doi: 10.1016/j.annals.2019.04.013

CrossRef Full Text | Google Scholar

11. Borges AP, Vieira E, Lopes JM. Emotional intelligence profile of tourists and its impact on tourism. J Qual Assur Hosp Tour. (2021) 1–22. doi: 10.1080/1528008X.2021.1964413

CrossRef Full Text | Google Scholar

12. Kim J-H, Ritchie JR, Tung VWS. The effect of memorable experience on behavioral intentions in tourism: a structural equation modeling approach. Tour Anal. (2010) 15:637–48. doi: 10.3727/108354210X12904412049776

CrossRef Full Text | Google Scholar

13. Eusébio C, Carneiro M, Kastenholz E, Alvelos H. The impact of social tourism for seniors on the economic development of tourism destinations. Eur J Tour Res. (2016) 12:5–24. doi: 10.54055/ejtr.v12i.210

CrossRef Full Text | Google Scholar

14. Kansky J. Benefits of well-being: health, social relationships, work, and resilience. J Posit Psychol Wellbeing. (2017) 1:129–69.

Google Scholar

15. Rivera M, Croes R, Lee SH. Tourism development and happiness: a residents' perspective. J Destin Mark Manag. (2016) 5:5–15. doi: 10.1016/j.jdmm.2015.04.002

CrossRef Full Text | Google Scholar

16. Frontiers | Does Economic Overheating Provide Positive Feedback on Population Health? Evidence From BRICS and ASEAN Countries | Public Health. Available online at: https://internal-journal.frontiersin.org/articles/10.3389/fpubh.2021.661279/full (accessed January 25, 2022).

Google Scholar

17. Koo C, Ricci F, Cobanoglu C, Okumus F. Special issue on smart, connected hospitality and tourism. Inform Syst Front. (2017) 19:699–703. doi: 10.1007/s10796-017-9776-9

CrossRef Full Text | Google Scholar

18. Larsen S, Mossberg L. The diversity of tourist experiences. Scand J Hosp Tour. (2007) 7:1–6. doi: 10.1080/15022250701225990

CrossRef Full Text | Google Scholar

19. Godovykh M, Tasci AD. Customer experience in tourism: a review of definitions, components, and measurements. Tour Manag Persp. (2020) 35:100694. doi: 10.1016/j.tmp.2020.100694

CrossRef Full Text | Google Scholar

20. Mueller H, Kaufmann EL. Wellness tourism: market analysis of a special health tourism segment and implications for the hotel industry. J Vacat Mark. (2001) 7:5–17. doi: 10.1177/135676670100700101

CrossRef Full Text | Google Scholar

21. Li C, Liu X, Bai X, Umar M. Financial development and environmental regulations: the two pillars of green transformation in China. Int J Environ Res Public Health. (2020) 17:9242. doi: 10.3390/ijerph17249242

PubMed Abstract | CrossRef Full Text | Google Scholar

22. Bauer I. The health impact of tourism on local and indigenous populations in resource-poor countries. Travel Med Infect Dis. (2008) 6:276–91. doi: 10.1016/j.tmaid.2008.05.005

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Lee CG. Health care and tourism: evidence from Singapore. Tour Manag. (2010) 31:486–8. doi: 10.1016/j.tourman.2009.05.002

CrossRef Full Text | Google Scholar

24. Zsarnoczky M. Novel concepts in the health tourism industry. Vadyba. (2018) 32:67–73. doi: 10.1016/j.tourman.2017.12.017

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Pressman SD, Cohen S. Does positive affect influence health? Psychol Bull. (2005) 131:925. doi: 10.1037/0033-2909.131.6.925

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Salovey P, Rothman AJ, Detweiler JB, Steward WT. Emotional states and physical health. Am Psychol. (2000) 55:110. doi: 10.1037/0003-066X.55.1.110

CrossRef Full Text | Google Scholar

27. Qin M, Su CW. Tourism and unemployment in Hong Kong-Is there any interaction? Asian Econ Lett. (2021) 1:1–5. doi: 10.46557/001c.17222

CrossRef Full Text | Google Scholar

28. Salehi-Esfahani S, Ridderstaat J, Ozturk AB. Health tourism in a developed country with a dominant tourism market: the case of the United States' travellers to Canada. Curr Issues Tour. (2021) 24:536–53. doi: 10.1080/13683500.2020.1724081

CrossRef Full Text | Google Scholar

29. Dwyer L. Resident well-being and sustainable tourism development: the ‘capitals approach.' J Sustain Tour. (2021) 1–17. doi: 10.1080/09669582.2021.1990304

CrossRef Full Text | Google Scholar

30. Hao L-N, Umar M, Khan Z, Ali W. Green growth and low carbon emission in G7 countries: how critical the network of environmental taxes, renewable energy and human capital is? Sci Total Environ. (2021) 752:141853. doi: 10.1016/j.scitotenv.2020.141853

PubMed Abstract | CrossRef Full Text | Google Scholar

31. Carrieri V, Jones AM. The income-health relationship ‘beyond the mean': new evidence from biomarkers. Health Econ. (2017) 26:937–56. doi: 10.1002/hec.3372

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Nunkoo R, Gursoy D, Dwivedi YK. Effects of social media on residents' attitudes to tourism: conceptual framework and research propositions. J Sustain Tour. (2020) 1–17. doi: 10.1080/09669582.2020.1845710

CrossRef Full Text | Google Scholar

33. Coghlan A. Tourism and health: using positive psychology principles to maximise participants' wellbeing outcomes-a design concept for charity challenge tourism. J Sustain Tour. (2015) 23:382–400. doi: 10.1080/09669582.2014.986489

CrossRef Full Text | Google Scholar

34. Xuan X, Khan K, Su C-W, Khurshid A. Will COVID-19 threaten the survival of the airline industry? Sustainability. (2021) 13:11666. doi: 10.3390/su132111666

CrossRef Full Text | Google Scholar

35. Khan K, Su C-W, Xiao Y-D, Zhu H, Zhang X. Trends in tourism under economic uncertainty. Tour Econ. (2021) 27:841–58. doi: 10.1177/1354816620909608

CrossRef Full Text | Google Scholar

36. Xu Y, Liu Z, Su C-W, Ortiz J. Causality between actual and expected inflation in central and eastern europe: evidence using a heterogeneous panel analysis. East Europ Econ. (2021) 59:148–70. doi: 10.1080/00128775.2020.1863821

CrossRef Full Text | Google Scholar

37. Sun T-T, Tao R, Su C-W, Umar M. How do economic fluctuations affect the mortality of infectious diseases? Front Public Health. (2021) 9:411. doi: 10.3389/fpubh.2021.678213

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Godovykh M, Ridderstaat J. Health outcomes of tourism development: a longitudinal study of the impact of tourism arrivals on residents' health. J Destin Mark Manag. (2020) 17:100462. doi: 10.1016/j.jdmm.2020.100462

CrossRef Full Text | Google Scholar

39. Godovykh M, Ridderstaat J, Fyall A. The well-being impacts of tourism: long-term and short-term effects of tourism development on residents' happiness. Tour Econ. (2021) 13548166211041228. doi: 10.1177/13548166211041227

CrossRef Full Text | Google Scholar

40. Li M, Cao X, Liu D, Fu Q, Li T, Shang R. Sustainable management of agricultural water and land resources under changing climate and socio-economic conditions: a multi-dimensional optimization approach. Agric Water Manag. (2022) 259:107235. doi: 10.1016/j.agwat.2021.107235

CrossRef Full Text | Google Scholar

41. Pesaran MH, Shin Y, Smith RJ. Bounds testing approaches to the analysis of level relationships. J Appl Econometr. (2001) 16:289–326. doi: 10.1002/jae.616

CrossRef Full Text | Google Scholar

42. Yang B, Usman M. Do industrialization, economic growth and globalization processes influence the ecological footprint and healthcare expenditures? Fresh insights based on the STIRPAT model for countries with the highest healthcare expenditures. Sustain Prod Consum. (2021) 28:893–910. doi: 10.1016/j.spc.2021.07.020

CrossRef Full Text | Google Scholar

43. Bahmani-Oskooee M, Ghodsi SH, Hadzic M. Asymmetric causality between stock returns and usual hedges: an industry-level analysis. J Econ Asymmetr. (2020) 21:e00160. doi: 10.1016/j.jeca.2020.e00160

CrossRef Full Text | Google Scholar

44. Pesaran MH, Shin Y, Smith RP. Pooled mean group estimation of dynamic heterogeneous panels. J Am Stat Assoc. (1999) 94:621–34. doi: 10.1080/01621459.1999.10474156

CrossRef Full Text | Google Scholar

45. Lenhart O. The effects of state-level earned income tax credits on suicides. Health Econ. (2019) 28:1476–82. doi: 10.1002/hec.3948

PubMed Abstract | CrossRef Full Text | Google Scholar