Unlocking the Dynamic Impact of Economic and Non-Economic Factors on Tourism Demand in BRICS Economies

Unlocking the Dynamic Impact of Economic and Non-Economic Factors on Tourism Demand in BRICS Economies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unlocking the Dynamic Impact of Economic and Non-Economic Factors on Tourism Demand in BRICS Economies Mir Alam, Jamal Hussain, Faiza Kiran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4143234/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent decades, economic and non-economic factors have significantly shaped tourism demand, affirming attention from researchers and policymakers. This study employs advanced econometrics techniques, including cross-sectional dependency, slope homogeneity, unit root, cointegration and long-run estimation, to explore the impact of economic determinants (world GDP per capita, exchange rate, interest rate, and relative price) and non-economic factors (political stability and climate change) on tourism demand in BRICS economies from 1996 to 2022. Findings indicate that exchange rates, interest rates, political stability, and world GDP per capita positively influence tourism demand, while climate change and relative price negatively impact it. Moreover, bidirectional causality exists between world GDP per capita and tourism demand, and one-way causation is observed for exchange rate, relative price, and political stability. In conclusion, the economic determinants significantly impact tourism demand more than non-economic factors. The study provides policy guidelines for enhancing tourism demand. Economic and non-economic factors relative price tourist arrivals BRICS countries Figures Figure 1 Figure 2 Figure 3 1. Introduction Given its substantial impact on the Global economy, the tourism sector has grown up to be one of the biggest industries worldwide. This pattern will keep expanding since the global economic recovery is driving demand from nearly all source markets, which in turn is driving the industry (UNWTO, 2022 ). The entirety of the occurrences and connections resulting from visitors' travels and stays, as long as they don't lead to permanent residence or have anything to do with work. (Leiper, 1979 ). Tourism is the act of travelers traveling for less than a year to a place other than their home base for any reason—leisure, business, religion, health, or any other personal reason—as long as they are not employed by the locals (UN, 2010 ). International tourism has qualified for sustained growth and extended diversification over the last 60 years, making it one of the global economic sectors with the fastest rate of growth, and it has emerged as a major force behind socioeconomic development. Efficacious tourism can increase the income from international tourists, employment, and government revenue. 2019 saw continued robust growth, however, the extraordinary rates of growth for foreign arrivals in 2017 (+ 7%) and 2018 (+ 6%) were not reached (WTO, 2021 ). Although, it is the most fragile sector. There are plenty of factors that affect the tourism industry. The tourism industry has been most affected by COVID-19. But then again, recovered rapidly, based on the latest report of the UNWTO, the number of foreign visitors increased by nearly 172% between January and July 2022 compared to the same period in 2021 (UNWTO, 2022 ). Numerous factors affect the flow of international travelers. The determinants of international tourism may be economic and non-economic. Consequently, the identification of the factors responsible for such a remarkable growth in tourist arrivals is essential. By focusing on most results drawn from previous literature; Political stability in the absence of violence and Climate change are considered as non-economic determinants, while nominal exchange rate, world GDP per capita, and relative price are examined as economic factors have been considered in this study. The important determinants of tourism demand at BRICS countries (Brazil, Russia, India, China and South Africa) are emphasized in this study. BRICS countries are the fastest growing emerging economies with diverse attraction for international visitors. These countries consist of almost half of the world population with rich histories of civilizations. Another important feature of BRICS countries is they cover almost all continents, like Asia, Europe, Africa and South America. To the best of our knowledge, this is the first study in the literature on tourism to pinpoint the critical elements influencing the demand for inbound travel for such a significant cluster of nations. Furthermore, an international tourism arrival is used as the proxies for tourism demand in the model. Obviously, the decision of including exchange rate as an economic determinant is perceptible. Meanwhile, a rise in nominal exchange rate (i.e. depreciation of a given currency) can decrease the domestic prices as compare to imports prices. Consequently, increases in demand of international tourists (Chi, 2015 ; Kim & Lee, 2017 ). An extensive amount of prior research has examined the connection between these two variables and got somewhat positive and robust relationship. Moreover, the literature has mostly concentrated on the nation's consumer price index (CPI), with little emphasis placed on comparing it to the CPIs of other countries. We believe that comparing prices of tourism goods is more significant in influencing consumer (or tourist) decisions than simply outlining the nation's price range. Detailed calculation and formula is discussed in the methodology part of this study. Additionally, relative price, or the difference between local and foreign prices, is also taken into account. Relative price compares the cost of living in any nation to that of the United States. As such, it calculates the buying capacity in the state that is visited. Still, the likelihood of having a large number of tourists is predicted to decrease with increasing relative. Therefore, this variable has a negative expected sign (Kim & Lee, 2017 ). Additionally, the average of the world income, i.e. WGDP, is another important tourism determinant in the study, which reflects the over-all economic situation of the world and wealth. Being a luxury service, tourism is highly income elastic; therefore, this variable is expected to have a positive sign. The higher WGDP causes to increase the demand of international tourism. Subsequently, tourism is a high income elastic luxury service. One significant feature of luxury goods and services is that demand for them rises as consumer income does. Consequently, individuals would spend more money travelling abroad. Both domestic and foreign travel are not perfect substitutes for one another because there are some domestic tourist attractions that may not be as good as those found abroad, particularly when it comes to attractions related to natural resources, cuisine, and cultural heritage. The cost and income of international travel can influence a person's decision to go overseas (Crouch, 1994 ; Martins et al., 2017 ). In contrast to majority of the previous studies, we include both economic determinants and non-economic determinants in our model. Because non-economic determinants; like political stability/absence of violence and climate change at tourists’ destinations countries can play a major role in the attraction of international visitors. Political stability/absence of violence one of important non-economic variable that has an impact on tourism demand. Safe and peaceful conditions in a country are disrupted by political instability. Since safety and security of tourist in the host country have a significant impact on tourist arrivals. Even though, there are a number of features that can influence the decision of selecting a destination for a tourist. Tourists keep the protection of life on the top priority because contrasting the other factors terrorism is not under the control of an international visitor (Alam & Mingque, 2018 ). Unfortunately, almost a couple of decades have seen a substantial rise of terrorist incidents across the world. Domestic political issues are usually related to these terrorist attacks. Although we have noticed recent incidents of terrorists’ incidence related to geopolitical differences internationally and also the power games of the global super powers. Most of the Asian countries have been suffering for last few decades. Furthermore, terrorism is an impediment for the flow of international tourism in the current years. Insecurity is one of the major hurdles in worldwide tourism (Alam & Mingque, 2018 ; P. J. Buckley & Klemm, 1993 ). Individual security is a main concern for global tourists. Nobody can compromise on safety. Travelers from abroad will look for safe and secure locations rather than ones that have been impacted by terrorism. The tourism industry, travelers, and host communities now seriously worry about terrorism on a global scale (Mansfeld & Pizam, 2006 ). Additionally, terrorist’s incidences have long-term effects on any country or region. Its effects are more than the immediate fatalities, but terrorists’ objective is to achieve social or political causes through violence. Climate change is another important non-economic determinant that affects the tourist arrivals. The nexus between tourism demand and climate change is pivotal. While section of tourist destination, climatic conditions of the tourists’ destination is an important factor. There are plenty of previous studies that discussed the impact of tourism on climate change. The increase in tourism-related emissions defies public opinion and trends in other industries. According to polls, people still regarded climate change as the biggest threat even during the pandemic (EIB, 2020 ). However, other industries are following the Paris Climate Agreement and striving for zero emissions by 2050 (UNFCCC, 2015 ), during that time, emissions from tourism are predicted to triple (Gössling & Scott, 2018 ). Even though since the 1990s there have been a significant number of scholarly articles discussing sustainable tourism, studies on sustainable tourism: The travel industry has not done much to slow down climate change (R. Buckley, 2012 ). The sector has not been sufficiently prepared by the actions we have taken in the last 30 years for the upcoming 30 years of increasing climate change influences and the transition to a decarbonized universal economy (Scott et al., 2021 ). As a result, there is no evidence in the Paris Agreement that has changed the way that tourism policies and planning take climate change into account or changed the sector's emissions growth trajectory. Analyzing the determinants of international tourism demand is important from numerous points of views. First, it helps the policy makers to bring reforms in tourism industry to boost up tourism demand and bring the equilibrium in demand and supply of tourism. Second, marketing management needs knowledge of the determinants that affects type of tour and tourist destination choice and forecast of international tourism flows in long-run and short-run to formulate strategic planning and marketing decisions. This study is organized as follows: Second section describe the literature review discussing on the relationship among the economic, non-economic tourism determinants and tourism demand. The third section describe the data and methodology to be used in the study. Section four describe results and discussions. Lastly, conclusion and policy recommendations are described. 2. Literature Review In this section we investigate the most significant earlier studies related to the economic and non-economic determinants of international tourism, which are the explanatory variables of this study. The literature on tourism variables paints a clear picture of the variables influencing demand for travel worldwide. These elements have been assessed as both non-economic and economic determinants. Consequently, we will dig deeper into previous literature regarding tourism demand determinants. Additionally, World incomes per capita, relative price and nominal exchange are the main economic factors. While the political stability and climate change are the non-economic factors. 2.1 Economic determinants In previous literature different economic determinants have been focused, which affects international tourism directly and indirectly. Concerning the economic determinants, number of tourist arrivals has been affected differently by different determinants. Moreover, we examine the previous literature on the connections among demand for tourism and income levels as well as between demand for tourism and exchange rate. Number of tourist arrivals and relative price also reviewed in this study. There is a sufficient number of literature on forecasting, modeling the factors that influence tourism demand, and defining its elasticities. Numerous surveys that use the meta-analysis method are justified by the volume of research on these topics, specifically (Crouch, 1995 ; Lim, 1999 ; Peng et al., 2015 ). Oh and Ditton ( 2005 ) examine the use of the real exchange rate in international tourism demand models, as well as the application of relative prices and the nominal exchange rate to estimations. According to their findings, estimates that employ the exchange rate and relative prices independently have better forecasting abilities and more desirable characteristics (Chang & Mcaleer, 2012 ). Researchers use daily information on fluctuations in exchange rates as well as arrivals of tourists to Taiwan from the US, Japan, and other nations between January 1990 and December 2008. Using a heterogeneous autoregressive model, in the series of tourist arrivals, the approximate long-memory properties are recorded. Martins et al. ( 2017 ) examine the association among some macroeconomic determinants and tourism demand. The authors used tourist expenditures and tourist arrivals as the proxies for tourist demand. According to their results, the proxy of tourist arrivals is more subjective by the world income while, the exchange rate is more important for tourist expenditures. Chao et al. ( 2013 ) examined how domestic pricing and exchange rates affected the travel and tourism sector. They have demonstrated that exchange rate is a key factor in the volume of travelers that a States have and revenue collected from international visitors. Moreover, devaluation in the local currency may reduce the tourism receipt. While appreciation in the currency may cause the inflation for inbound tourist that leap towards decrease in the number of tourist arrivals. Furthermore, tourist arrival is also different in the developed countries from the developing world. Cheng et al. ( 2013 ) different results: international tourists to America are more delicate to exchange rate while American tourists are affected by income. Similarly, the cost of living at the destination of a tourist depends on the exchange rate, which defines the purchasing power and price of tourism goods and services (Crouch, 1994 ; Webber, 2001 ). Consequently, inbound tourism is likely to be affected by the exchange rate volatility. They further propose that the instability in exchange rate affects the decision regarding a tourist’s destination, as the exchange rate has the same impact on relative price at the destination country. Song et al. ( 2010 ) examined the key factors that influence demand for tourism when estimating demand using visitor arrivals and spending. The authors analyze the years 1981 to 2006 using America, Australia, and the UK as the origin states and Hong Kong as the destination. The authors' findings indicate that while tourism demand, as measured by travel expenses, is impacted more by the real exchange rate, tourism demand, as measured by visitor arrivals, is more prejudiced by income. Additionally, they discover that aggregate models produce better results than per capita models. Morley et al. ( 2014 ) have argued that a growth in the WGDP, a decrease in the value of the domestic currency, and a drop down of comparative domestic prices does help to increase the number of tourist arrivals. And tourism receipt or expenditure level has been affected by the WGDP, because international tourism has been categorized as a luxury service which is influenced by income. They stated that on average, tourist arrivals increased by 1.2% annually and international tourism receipt decreased by 2% annually. Song and Witt ( 2000 ) explored the growth in international tourism through examine the general-to-specific modeling and forecasting approach for tourism demand, taking into account the application of error correction models, autoregressive distributed lag processes, and cointegration analysis. They found that income and price of tourism goods are two main variables in the research of tourism demand. Lim ( 1999 ) performed a meta-analysis of 70 studies, related to relative price, income and transportation cost and tourism demand. He found 6.2%, 2.8% and 8.3% effect on tourism demand by transportation cost, income and relative price respectively. Findings for relative price and world income are strong and more robust then the transport cost. But magnitude effects are strongly significant, which is an evidence of its importance in tourism demand. In an another, meta-analysis study, (Peng et al., 2015 ) review 195 research studies published from 1961 to 2011, examining the influence and characteristic of the income and price on the tourism demand elasticity. Almost all the authors find the significant influence of the explanatory variables. The sample size, the time period, the frequency of the data and the methodology have also influence on the assessments. Crouch ( 1992 ) analyzed 44 countries to examine the influence of price and income on inbound tourism demand. He observed that the methodology used in the papers, the data type, the variables involved, the country considered and the time period are the causes of variation of impact of price and income on demand of international tourism in previous studies. Nevertheless, the variables listed above are unable to account for the volatility in the price elasticities. The problem might lie only in how the price is defined. However, the variations across the multiple studies can be used to explain the volatility in the results for the income elasticities of tourism demand. 2.2 Non-economic factors In addition to monetary factors, some non-economic factors also affect international tourism. This section examines the most significant body of prior research on the non-economic factors that influence international travel. An abundance of research studies done on the relationship between non-economic factors and tourism demand. The connection between political stability and international tourism was pioneered through the effort of studies such as, Kosters ( 1984 ), Mathews ( 1975 ), and Matthews and Richter ( 1991 ). Impact of noneconomic determinants is always a hot topic in tourism research. Moreover, Saha and Yap ( 2014 ) examine how political unrest and terrorism interact to impact the growth of tourism using panel data from 139 nations between 1999 and 2009. The study assesses how much terrorism and political unrest can harm a nation's travel and tourism sector. The findings show that political unrest has a far more negative impact on travel than do isolated terrorist incidents. In the most recent study of Aydin ( 2022 ) examined the nexus among political stability, renewable energy, and tourism demand in turkey from 1996 to 2018, utilizing ARDL (Autoregressive Distributed Lag) approach. The results indicate that long-term estimates indicate that tourism is significantly and positively impacted by both political stability and the use of renewable energy. Ingram et al. ( 2013 ) investigated how tourism and political unrest are related and sensitive to each other in Thailand. According to the study, political unrest might only have a short-term impact on nations with a positive reputation, like Thailand. It is possible that tourist spots with a strong reputation and high rates of return business can withstand the negative media attention better than other locations with a weaker reputation. The further suggested that the duration of the political unrest may also be related to the decline in tourism. Causevic and Lynch ( 2013 ) investigated by 52 in-person interviews with research participants connected to state, entity, cantonal, and local public and private sectors. The cities of Sarajevo, Banja Luka, Mostar, and Bihać hosted the interviews in the summers of 2006, 2008, and 2010. Additionally, a few participants were questioned more than once. Their findings are the main issues preventing tourism from developing further are the complicated political and economic systems, rules, and regulations left over from the war, as well as their effects on political instability. Additionally, researchers have consistently focused on the connection between the demand for tourism and climate change during the last decade. The most pertinent quantitative methods for assessing how climate change is affecting tourism (Rosselló-Nadal, 2014 ; Scott, 2011 ; Scott et al., 2012 , 2016 ). This analysis demonstrates that the effects of climate change can be measured in three ways: (1) by examining changes in the physical conditions that are critical to tourism; (2) by evaluating the allure of tourist destinations using climate indexes; and (3) by modeling demand for tourism that takes climate determinants into account. Michailidou et al. ( 2016 ) revealed that in addition to being one of the economic sectors most vulnerable to climate change, tourism also contributes to it. As the effects of climate change become more and more concerning, the travel and tourism industry needs to act quickly to mitigate its emissions and modify its travel destinations and businesses to better accommodate the changing climate. As a result, after carefully reviewing the literature on tourism, it is noted that there is disagreement regarding the direction and influence of various factors on demand for tourism. Subsequently, a significant research gap exists in investigating the influence and direction of economic and non-economic determinants on tourism demand of BRICS states. 3. Data and Econometric Estimation 3.1 Data and variables For the five BRICS nations [Brazil, Russia, India, China, and South Africa] a balanced annual data covering the years 1996–2022 were used in this study. All the variables gathered from the various sources are displayed in Table 1 . In line with the previous literature of tourism like (Corne & Peypoch, 2020 ; Martins et al., 2017 ) tourist arrival (TA) is used as proxies of tourism demand. Following (Rasool et al., 2021 ), for the proxy of the world income, the real per capita GDP (WGDP) of the world is used in this study. Additionally, the nominal exchange rate (XR) is also introduced as one of the main explanatory variables. XE is described in terms of the American dollar, the most leading medium of exchange in the world economic system. All TA,WGDP, and EX are drawn from the World Bank's database (WDI, 2023 ). Table 1 and Fig. 1 shows the variable description and definition. Furthermore, a relative price (RP) is found another important factor for tourism demand in tourism literature. Consumer price index (CPI) of a tourist destination state is commonly used as a proxy for RP in tourism literature. CPI measures the prices level of a basket of goods and services in a country. We will calculate the relative prices using the following formula $${RP}_{it}=\frac{{CPI}_{it}}{{CPI}_{jt}}*{ER}_{it}$$ Where, \({CPI}_{it}\) is the consumer price index of the tourist destination country, while \({CPI}_{jt}\) represents the US price level, this has been used as the proxy for the world consumer price index (CPI). Both \({CPI}_{it}\) and \({CPI}_{jt}\) have been gathered from the World Bank database (WDI, 2023 ). Moreover, political stability and absence of violence (PS), is used as one of the important non-economic factors of world tourism demand in many studies in tourism literature, such as (Aydin, 2022 ; Ingram et al., 2013 ; Saha & Yap, 2014 ). The data of PS is collected from the World Governance Indicators (WGI) database (WGI, 2023 ). While Climate change (CC), A baseline climatology covering the years 1951–1980 is used to calculate annual estimates of mean surface temperature change. (Unit: Degree Celsius). The data of CC is collected from Food and Agriculture Organization of United States (FAOSTATE, 2023 ). Our inquiry seeks to determine the impact of economic and noneconomic determinants that influence tourism demand. In the current study, in order to help us to analyze the effects of these variables, we constructed the empirical model using the standard Kaya Identity. $${LnTA}_{it}={\omega }_{it}+{\pi }_{1,it}{LnWGDP}_{1,it}+{\pi }_{2,it}{LnXR}_{it}+{\pi }_{3,it}{LnRP}_{,it}+{\pi }_{4,it}{LnPS}_{it}+{\pi }_{5,it}Ln{CC}_{it}+{\mu }_{it} \left(1\right)$$ $${LnTR}_{it}={\omega }_{it}+{\pi }_{1,it}{LnWGDP}_{1,it}+{\pi }_{2,it}{LnXR}_{it}+{\pi }_{3,it}{LnRP}_{,it}+{\pi }_{4,it}{LnPS}_{it}+{\pi }_{5,it}Ln{CC}_{it}+{\mu }_{it} \left(2 \right)$$ The following notation is used in the models mentioned above: The logarithm, or Ln, is the regressand \(TA\) stands for number of international tourists arrivals, while \(WGDP, XR, RP,PS and CC\) stands for world GDP per capita, nominal exchange rate (national currency in term of $ US), relative price with respect to US, Political Stability and Absence of Violence/Terrorism. Similarly, \(\omega , and {\pi }_{1,\dots \dots , 5}\) are slope and the regressor coefficients that need to be calculated. In the regression equations, µ represents the error term, and i, t, and time represent the cross-sectional units and time, respectively. In the following sections, we detail econometric approaches to the in-depth examination of the aforementioned models. Table 1 Variables, sources, and definitions of the data Variable Symbol Measured Source Tourist Arrivals TA International tourism, number of arrivals WDI, 2023 World GDP per capita WGP World GDP per capita current US $ WDI, 2023 Exchange rate EX Nominal exchange rate in term of $ US WDI, 2023 Relative price with respect to US RP Calculate the relative prices using the following formula \({RP}_{it}=\frac{{CPI}_{it}}{{CPI}_{jt}}*{ER}_{it}\) Where, \({CPI}_{it}\) is the consumer price index of the tourist destination country, while \({CPI}_{jt}\) represents the US price level, this has been used as the proxy for the world consumer price index (CPI). Both \({CPI}_{it}\) and \({CPI}_{jt}\) WDI, 2023 Political stability PS Political Stability and Absence of Violence/Terrorism WGI, 2023 Climate change CC Annual estimates of mean surface temperature change are measured with respect to a baseline climatology, corresponding to the period 1951–1980(Unit: Degree Celsius) FAOSTATE,23 3.2 Econometric Approaches Figure 2 shows the methodological framework of our study. The study begins by outlining six econometric approaches for evaluating the pre-designed models. Where initially, slope homogeneity and cross-sectional dependence are estimated. In the second a unit root test was utilized to verify the stationarity of the data utilized in this investigation. The models are checked for cointegration relationships in the third stage. In the fourth step, a long-run link of the modelled variables are tested using the Fully Modified Least Square (FMOLS) technique. To determine the causal measurements of the modelled variables have been done in the fifth step. Lastly, Dynamic Ordinary Least Square (DOLS) model is used to investigates the robustness of the long-run results. 3.2.1 Cross-sectional dependence and slope homogeneity tests Cross-sectional dependencies can result from economic and noneconomic factors as well as demand for tourism across national boundaries. To prevent irrational results, modern researchers like (Hussain & Zhou, 2022 ; Qi et al., 2023 ) advise addressing this matter prior to formal data analysis. This research focuses on the BRICS countries—Brazil, Russia, India, China, and South Africa—as a group of five nations. Given the possibility of interdependencies economics, noneconomic determinants and tourism demand among these states, we plan to use the (Pesaran, 2015 ) cross-sectional reliance test to determine whether cross-sectional dependence exists. We can determine that the countries show cross-sectional dependency if we reject the null hypothesis of the test, which states that there is no cross-sectional dependence. $${CD}_{pesaran}= \sqrt{\frac{2T}{N(N-1)}}\left[\sum _{i=1}^{N-1}\sum _{j=1+1}^{N}\widehat{{r}_{ij}^{2}}\right] \left(3\right)$$ To address potential slope heterogeneities between the BRICS economy panels, this study additionally used the panel slope homogeneity (SH) test in addition to the cross-sectional dependence (CD) test. This stage is crucial since estimation is predicated on the supposition of slope homogeneity may provide false results if the slopes are heterogeneous, heterogeneous (Sheng Yin & Hussain, 2021 ; Yin et al., 2021 ). The slope homogeneity (SH) test, which was developed by Hashem Pesaran and Yamagata ( 2008 ), was employed in this study. This test investigates the reliability of slopes throughout the panels using the null hypothesis of slope homogeneity. 3.2.2 Panel unit root tests Various unit root tests with distinct benefits can be found in the literature. These tests assess if the series exhibits time-averaged stationary behavior. Using the method of Hussain et al. ( 2021 ), if there are non-stationary features in the variables at the level, to confirm the equilibrium relationship between them, a cointegration test is carried out. There is a considerable chance that cross-sectional dependence from first-generation stationarity tests will result from false-positive regression. To address this problem, this study used Cross-sectional Augmented IPS (CIPS) test, (Pesaran, 2007 ), which was developed using the Cross-sectional Augmented Dickey-Fuller (CADF) technique: $$CIPS=\frac{1}{N}\sum _{i=1}^{N}{CADF}_{i} \left(4\right)$$ If, at a 5% significance level, the estimated value of CIPS is less than or greater than the critical values, the test's null hypothesis of non-stationarity can be rejected. After determining that the variables demonstrated stationarity and evaluating the degree of stationarity, we ran multiple cointegration tests. We will be able to ascertain the long-run equilibrium relationship between the variables by using these estimators. To find a long-term, non-spurious correlation between the variables, two-panel cointegration tests are used: the (Westerlund, 2005 ) bootstrapped panel cointegration test and the (Pedroni, 2004 ) panel cointegration test. Outline for panel cointegration testing, suggested by (Pedroni, 2004 ) forms the Engle and Granger two-step procedure. Prior to addressing data heterogeneity, this method eliminates individual-specific deterministic trends and short-run parameters (Karmaker et al., 2021 ). 3.2.3 Panel cointegration tests After confirming the unit root test, this study used Westerlund ( 2005 ) to explore the long run association among the study underlying variables. These techniques advantageous by easing the shared factor limits normally used to residual based tests for cointegration. These recently proposed tests concentrate on structural dynamics, unlike residual dynamics, which can be constrained by common factors. This distinction is crucial because common factor limitations have the potential to significantly lower the influence of residual-based cointegration analysis, underscoring the need of taking structural dynamics into account when conducting these kinds of analyses. The requirement that the short-term and long-term adjustment processes be the same is eliminated by doing away with this restriction. By using the bootstrap procedure developed by (Westerlund, 2005 ), we can obtain reliable critical values and minimize cross-section-dependent distortion effects. The findings displayed in Table 4 demonstrate that the presence of cointegration is strongly supported by the results of the (Westerlund, 2005 ) and (Pedroni, 2004 ) bootstrapped cointegration tests. The fact that the null hypothesis was rejected in both tests suggests that the models are cointegrated over a long time horizon. 3.2.4 Fully Modified Ordinary least squares (FMOLS) The effect of the independent variables on the dependent variables is determined using long-run estimators once the cointegration test has shown that the variables have a long-term equilibrium association. This study employs FMOLS and DOLS econometric techniques. The FMOLS and DOLS are single equation estimators for both short- and long-term cointegrated associations. Due to their suitability for the current study and their capacity to address the issues of serial correlations and indigeneity in the error terms, the panel FMOLS and DOLS approaches were chosen for the current investigation (explained further in the portion on results and analysis). Additionally, because FMOLS is a non-parametric method for handling serial correlation, it is resistant to autocorrelation and indigeneity (Akpolat, 2014 ; Merlin & Chen, 2021 ). On the other hand, the DOLS approach uses parametric techniques to remove issues caused by the explanatory variables' leads and lags (Addis & Cheng, 2023 ; Merlin & Chen, 2021 ). Additionally, the DOLS technique also imposes additional requirements that all variables be integrated in the same order and yields accurate estimates. It is also more effective for small sample sizes. Above all, the DOLS approach can deal with data heterogeneity and cross-sectional dependence (CD) (Addis & Cheng, 2023 ). Results could be biased if the CD problem is not addressed. Therefore, this study obtained unbiased and trustworthy estimates by detecting the causality among the study variables and analyzing the longer run affiliation using the FMOLS and DOLS methods (Dogan & Afsar, 2023 ). The CD test is also examined in this study in addition to DOLS and FMOLS estimations. This is significant because it helps determine which panel unit root test to use the first-generation or second-generation type. The research's observed variable is either stationary or nonstationary, and in the second stage, panel unit root tests are used to judge this. It is imperative to assess the unit root tests prior to performing the panel cointegration test, as failure to do so could result in ambiguity and inaccurate results. Therefore, using a range of unit root tests predicated on the null hypothesis, the current study was conducted. Panel cointegration test is conducted in the third stage. 3.2.5 Robustness analysis This study used Dynamic Ordinary Least squares (DOLS) to verify the results obtain from FMOLS. DOLS results confirm the sign and magnitude of the estimation from FMOLS. 3.2.6 Panel causality test To provide additional information and assist policymakers in developing responsible policies, Among the underlying variables of the study, we performed a panel causality analysis (Dumitrescu & Hurlin, 2012 ). Compared to conventional Granger causality tests, the Dumitrescu and Hurlin test provides more accurate results by taking cross-sectional dependence into account. Additionally, it functions well in situations through T N and data is unbalanced (Karmaker et al., 2021 ). As opposed to the null hypothesis, which states that there are no causal relationships in the panel, the alternative hypothesis asserts that there is a causal relationship in at least one group within the sample. We take the average of each cross-sectional unit's individual Wald statistics to obtain the statistics for this test, as shown below: $$\left[{S}_{n,t}^{DH}=\frac{1}{N}\sum _{i=1}^{N}{S}_{i,t}\right] \left(6\right)$$ Where Wald statistics at time t for each group are explained by S_(i,t). 4. Results and Discussion Table 2 provides a preliminary, descriptive analysis of the selected variables prior to the formal analysis of the models. Table 2 Descriptive analysis N Mean Median Max Min Sum SD TA 135 3.03e + 07 1.30e + 07 1.60e + 08 2300000 4.09e + 09 4.24e + 07 EX 135 23.429 8.29 127.036 1.005 3162.875 26.69 PS 135 − .52 − .46 .33 -1.51 -70.257 .426 RP 135 26.396 8.291 177.626 .593 3563.442 37.88 CC 135 1.031 .969 3.691 − .15 139.179 .568 WGP 135 8269.329 9371.49 10850.2 5256.61 1120000 2039.725 4.1 Results of cross-sectional dependence and slope homogeneity tests Table 3 presents results that indicate the null hypothesis, namely the absence of cross-sectional dependence, is rejected in view of the CD findings. The findings indicate a correlation between the panel's variables in this section, suggesting that decisions made in one country may affect those in other countries. Thus, it emphasizes how important it is to put new policies into place. Additionally, the SH results demonstrate differences in slope among the panels, highlighting the necessity of important policy directives in order to deal with these issues in an efficient manner (CD & SH). Disregarding these policy distinctions could have a significant effect. Once the presence of CD and SH in the variables has been established, the series must be examined for the possibility of unit roots and their cointegration relationship. However, not every variable at the level is stationary., Table 4 's results indicate stationarity when converted to first differences. First-order stationarity is thus displayed by the chosen variable dimension, thereby achieving the study's primary goal. Table 3 , second panel: The reported results for SH show that there are heterogeneous slopes in the models and reject the null hypothesis. Table 3 Crossectional dependence test and slope homogeneity tests results Variables CD-test P-values Corr Abs (Corr) CD test TA 7.54 0.000 0.459 0.506 EX 3.80 0.000 0.231 0.914 PS 3.13 0.002 0.190 0.282 RP 4.09 0.000 0.249 0.935 CC 8.43 0.000 0.513 0.513 WGP 16.43 0.000 1.000 1.000 SH test Delta 2.214 0.027 Adj_delta 2.322 0.020 4.2 Results of panel unit root tests We first looked at the simulated variables for possible cross-sectional interdependence before determining the various integration orders between the variables. We first looked at the simulated variables to see if there might be cross-sectional interdependence, and then we went on to figure out the various integration orders between the variables. The Pesaran CIPS test results in Table 4 demonstrated that the variables EX, RP, CC and WGP are stationary at level while TA and PS display first-order integration. This demonstrates that the accepted level of the series supports the null hypothesis of unit roots. Based on these results, we investigated the potential equilibrium relationship between the regressand and regressor using cointegration analysis. Table 4 Results of Pesaran CIPS & CADF tests of unit roots Variable CIPS CADF Level 1st -difference Level 1st -difference TA -1.062 -4.092 *** 0.855 -2.671 *** EX -3.239 *** -4.858 *** -1.693 ** -6.262 *** PS -2.042 -5.570 *** 1.279 -4.938 *** RP -2.574 *** -4.344 *** 0.344 -4.912 *** CC -4.501 *** -6.163 *** -6.109 *** -8.577 *** WGP 2.610 *** 2.610 *** 2.610 -4.168 *** Note : Significance at *** p < 0.01, ** p < 0.05, * p < 0.1 4.3 Results of the panel cointegration tests Once the variables were confirmed to show stationarity at the first difference, we proceeded to determine whether cointegration was present in the models. The cointegration results for the TA model using two different cointegration methods (Westerlund, 2005 ) and (Pedroni, 2004 ) are shown in Table 5 . Existence of cointegration is strongly supported by the results of the Westerlund and Pedroni cointegration tests. This suggests a strong long-run relationship among the variables in the model, indicating a stable equilibrium relationship among the regressor and regressed in the model and bolstering the validity of our cointegration analysis. Table 5 Results of panel cointegration Westerlund ( 2005 ) cointegration TA-model Some panels are cointegrated (VR) -1.8739** All panels are cointegrated (VR) -1.3901* Pedroni ( 2004 ) cointegration Modified Phillips-Perron t -0.1871 Phillips-Perron t -1.3926* Augmented Dickey-Fuller t -2.3722*** Kao Test (1999) Modified Dickey-Fuller t -3.7947*** Dickey-Fuller t -2.2941** Augmented Dickey-Fuller t -1.5175** Unadjusted modified Dickey-Fuller t -3.8224*** Unadjusted Dickey-Fuller t -2.3013** Note : VR is variance ratio, and significance at *** p < 0.01, ** p < 0.05, * p < 0.1 4.4 Panel FMOLS results As the BRICS countries' observed variables are cointegrated, Next step involves estimating long-term association between the following variables: relative price, exchange rate, political stability, world GDP per capita, tourist arrivals, and climate change. A range of estimators, including between and within dimension groups like the FMOLS, DOLS, and ordinary least squares (OLS) estimators, are available to estimate a cointegration vector using panel data. When estimating the long-run relationship in cointegrating panels, the OLS method has at least two significant drawbacks: it may affect from heteroscedasticity and have an un-standard distribution of OLS estimations, which causes the standard testing procedure to fail. Additionally, the limits of the sample bias of the parameters are larger until and unless the regressors are severely exogenous. (Ugrinowitsch et al., 2004 ). Consequently, it is generally not feasible to use the OLS estimation for reliable inference (Kao & Chiang, 2000 ; Ozarslan Dogan & Afsar, 2023 ). On the other hand, standard errors are consistently estimated by FMOLS and DOLS, which can be utilized for assumption. Phillips et al. ( 1990 ) initially introduced the panel FMOLS estimation, a non-parametric technique, to estimate cointegration coefficients and resolve endogeneity in the regressors and further refined by (Pedroni et al., 2001) to deal with the endogeneity in the regressors and estimate the cointegration coefficients. The panel FMOLS estimation considers the possibility of a constant term and the potential relationship between the error term and the first differences of the regressors in order to handle corrections for serial correlation (Phillips et al., 1990 ). Additionally, the panel DOLS model, a fully parametric method was first put forth by (Saikkonen, 1992) and (Stock and Watson, 1993), DOLS estimator was then applied to panel datasets by (Kao & Chiang, 2000 ). As a practical alternative for the panel FMOLS cointegration estimator, the panel DOLS estimation is advised by(Mark et al., 2003 ). Additionally, the endogeneity and serial correlation can also be adjusted with the panel DOLS estimators. To account for endogenous feedback, lead and lag differences of the explanatory variables are added to the long-run regression in the panel DOLS estimation. Furthermore, the panel DOLS estimator employs parametric adjustment to the errors to produce an unbiased estimator of the long-run parameters by incorporating the past and future values of the differenced I(1) regressors (Mark et al., 2003 ; Phillips et al., 1990 ). Table 6 displays the results of the panel cointegration relationships obtained using the FMOLS and DOLS pooled estimation technique. These tables investigate the validity of long-term linear cointegration relations among observed variables for the BRICS nations. The analysis takes tourist arrivals as dependent variable. The panel FMOLS findings show that all variables are statistically significant at 1 percent level of significance with expected directions. EX, WGDP, and PS are positively influencing TA, while RP and CC are negatively influencing TA. Generally, the findings of this study indicates that there is a strong long-run relationship among EX, WGDP, RP, PS, and CC on TA. The finding in FMOLS model also indicates that, a 1% increase in EX correlate with an increase of 1.7% in TA. The study's findings are consistent with (Martins et al., 2017 )(Peng et al., 2015 ). Moreover, ER has highest impact on TA followed by WGDP. A 1% increase in WGDP and PS leads to increase in TA by 1.6% and 0.12% respectively, which is consistence with vv(Aydin, 2022 ; Chi, 2015 ; Martins et al., 2017 ; Webber, 2001 ). Conversely, a 1% increase in RP and CC correlated to decrease in TA by 0.8% and 0.17% respectively. The results of this study are coinciding with plenty of previous like (Aydin, 2022 ; Chi, 2015 ; Dogan & Afsar, 2023 ). 4.5 Robustness analysis results The estimated results from FMOLS are re-estimated using DOLS estimation to verify the robustness of the findings. The empirical results in Table 6 shows that similar results with consistent magnitudes and directions are produced by the variables. We may therefore state with confidence that these trustworthy estimates are important for theory, policy, and the actual application of policy by policymakers. Table 6 Long-run estimation with FMOLS and DOLS estimators (TA-model) FMOLS DOLS Variables Coefficients t-Statistics P-values Coefficients t-Statistics P-values LnEX 1.7013*** 6.5253 0.0000 6.1019*** 5.1937 0.0000 LnWGDP 1.6025*** 43.338 0.0000 1.6089*** 3.3035 0.0020 RP -0.0787*** -4.1472 0.0001 -0.5429*** -4.5475 0.0000 PS 0.1213*** 2.0505 0.0425 1.2143*** 5.7033 0.0000 LnCC -0.1717** -1.9243 0.0567 -0.4527*** -2.8483 0.0068 Note: *, * *, and *** indicate the level of significance at 1%, 5%, and 10%, respectively. 4.6 Results of panel causality test (Sheng Yin & Hussain, 2021 ) highlighted the substantive policy significance of causal dimensions in modern econometric research. Table 7 and Fig. 3 present the findings of panel causality testing that was done for the study. The empirical results show the EX, RP and PS have unidirectional causalities on TA. Contrary, TA has unidirectional casualty on CC, while WGP and TA validate the presents of bidirectional links. Moreover, RP has the strongest causality on TA followed by WGP. However, non-economic factors like PS and CC demonstrated a weak causal link. Table 7 D&H-Granger non-causality test results. Causality direction W-statistics \(\stackrel{-}{\varvec{Z}}\) -statistics p -value CO 2 e-model TA ≠ EX 3.41842 1.07657 0.2817 EX ≠ TA 4.14561* 1.73105 0.0834 TA ≠ RP 2.37037 0.13333 0.8939 RP ≠ TA 5.20147*** 2.68133 0.0073 TA ≠ PS 2.57297 0.31567 0.7523 PS ≠ TA 4.21860* 1.74326 0.0834 TA ≠ WGP 1.9716** 2.5705 0.0102 WGP ≠ TA 4.81515** 2.33363 0.0196 TA ≠ CC 4.43259** 2.03956 0.0414 CC ≠ TA 3.26786 0.94107 0.3467 Note : α≠𝛽 indicates that α does homogeneously cause 𝛽, D&H is Dumitrescu & Hurlin ( 2012 ), and Significance at * p < 0.1, ** p < 0.05, *** p < 0.01 5. Conclusion This study examines the influence of some important economic determinants and non-economic factors, such as political stability/ absence of violence, climate change, WGDP, relative price and exchange rate on inbound number of tourist arrivals for the panel of BRICS countries over the period of 1996 to 2022. The model was considered, Tourist Arrivals (TA), measuring for the demand of the tourism. quantity. Prior to using a panel unit root test to account for cross-sectional dependency, the study looks at cross-national dependency. Correspondingly, cointegration relationships are frequently supported by controlling cross-sectional dependence because of the presence of non-stationary variables. Following the establishment of cointegration relations, the study applies FMOLS and DOLS estimators to confirm long run relationships among the variables. To sum up the findings, TA increase due to rise in WGDP, ER and PS. While, CC and RP are adversely affecting TA. The results reveal that an upsurge in WGDP, a depreciation of the domestic currency, a decline of relative indigenous price, decrease of climate change and increase in the political stability encourages boosting up the international tourism demand. Moreover, the exchange rate is the most influencing factor followed by WGDP for tourism demand. Relative price has the least impact among all three economic determinants. In addition, both economic and non-economic determinants are also statistically significant. The proxy of climate change is negatively affecting the tourism demand while political stability has a minor positive impact on number of arrivals. All the estimates are in line with (Aydin, 2022 )(Li et al., 2017 )(Martins et al., 2017 ). In conclusion, the findings reveal that economic factors are more influential than non-economic factors to upsurge the tourism sector of BRICS country. 5.1 Policy recommendations It is just an effective increase in tourism and economics literature. Policy makers who are developing strategic tourism policies can benefit from our work. Additionally, prior to evaluating the influence of economic and non-economic variables, they ought to focus on the selection and characterization of the explanatory and dependent variables. Second, both Arrivals and Expenditures are important proxies for the demand for tourism may respond differently to global income, relative prices, the nominal exchange rate, political stability and climate change. Consequently, Policymakers should take this into account when determining which target variables to include in their policies. Since the ultimate objective is to increase the tourism demand to boost up the income from tourism sector. 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Introduction","content":"\u003cp\u003eGiven its substantial impact on the Global economy, the tourism sector has grown up to be one of the biggest industries worldwide. This pattern will keep expanding since the global economic recovery is driving demand from nearly all source markets, which in turn is driving the industry (UNWTO, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The entirety of the occurrences and connections resulting from visitors' travels and stays, as long as they don't lead to permanent residence or have anything to do with work. (Leiper, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Tourism is the act of travelers traveling for less than a year to a place other than their home base for any reason\u0026mdash;leisure, business, religion, health, or any other personal reason\u0026mdash;as long as they are not employed by the locals (UN, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). International tourism has qualified for sustained growth and extended diversification over the last 60 years, making it one of the global economic sectors with the fastest rate of growth, and it has emerged as a major force behind socioeconomic development. Efficacious tourism can increase the income from international tourists, employment, and government revenue. 2019 saw continued robust growth, however, the extraordinary rates of growth for foreign arrivals in 2017 (+\u0026thinsp;7%) and 2018 (+\u0026thinsp;6%) were not reached (WTO, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although, it is the most fragile sector. There are plenty of factors that affect the tourism industry. The tourism industry has been most affected by COVID-19. But then again, recovered rapidly, based on the latest report of the UNWTO, the number of foreign visitors increased by nearly 172% between January and July 2022 compared to the same period in 2021 (UNWTO, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous factors affect the flow of international travelers. The determinants of international tourism may be economic and non-economic. Consequently, the identification of the factors responsible for such a remarkable growth in tourist arrivals is essential. By focusing on most results drawn from previous literature; Political stability in the absence of violence and Climate change are considered as non-economic determinants, while nominal exchange rate, world GDP per capita, and relative price are examined as economic factors have been considered in this study. The important determinants of tourism demand at BRICS countries (Brazil, Russia, India, China and South Africa) are emphasized in this study. BRICS countries are the fastest growing emerging economies with diverse attraction for international visitors. These countries consist of almost half of the world population with rich histories of civilizations. Another important feature of BRICS countries is they cover almost all continents, like Asia, Europe, Africa and South America. To the best of our knowledge, this is the first study in the literature on tourism to pinpoint the critical elements influencing the demand for inbound travel for such a significant cluster of nations.\u003c/p\u003e \u003cp\u003eFurthermore, an international tourism arrival is used as the proxies for tourism demand in the model. Obviously, the decision of including exchange rate as an economic determinant is perceptible. Meanwhile, a rise in nominal exchange rate (i.e. depreciation of a given currency) can decrease the domestic prices as compare to imports prices. Consequently, increases in demand of international tourists (Chi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kim \u0026amp; Lee, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). An extensive amount of prior research has examined the connection between these two variables and got somewhat positive and robust relationship. Moreover, the literature has mostly concentrated on the nation's consumer price index (CPI), with little emphasis placed on comparing it to the CPIs of other countries. We believe that comparing prices of tourism goods is more significant in influencing consumer (or tourist) decisions than simply outlining the nation's price range. Detailed calculation and formula is discussed in the methodology part of this study. Additionally, relative price, or the difference between local and foreign prices, is also taken into account. Relative price compares the cost of living in any nation to that of the United States. As such, it calculates the buying capacity in the state that is visited. Still, the likelihood of having a large number of tourists is predicted to decrease with increasing relative. Therefore, this variable has a negative expected sign (Kim \u0026amp; Lee, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, the average of the world income, i.e. WGDP, is another important tourism determinant in the study, which reflects the over-all economic situation of the world and wealth. Being a luxury service, tourism is highly income elastic; therefore, this variable is expected to have a positive sign. The higher WGDP causes to increase the demand of international tourism. Subsequently, tourism is a high income elastic luxury service. One significant feature of luxury goods and services is that demand for them rises as consumer income does. Consequently, individuals would spend more money travelling abroad. Both domestic and foreign travel are not perfect substitutes for one another because there are some domestic tourist attractions that may not be as good as those found abroad, particularly when it comes to attractions related to natural resources, cuisine, and cultural heritage. The cost and income of international travel can influence a person's decision to go overseas (Crouch, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Martins et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast to majority of the previous studies, we include both economic determinants and non-economic determinants in our model. Because non-economic determinants; like political stability/absence of violence and climate change at tourists\u0026rsquo; destinations countries can play a major role in the attraction of international visitors. Political stability/absence of violence one of important non-economic variable that has an impact on tourism demand. Safe and peaceful conditions in a country are disrupted by political instability. Since safety and security of tourist in the host country have a significant impact on tourist arrivals. Even though, there are a number of features that can influence the decision of selecting a destination for a tourist. Tourists keep the protection of life on the top priority because contrasting the other factors terrorism is not under the control of an international visitor (Alam \u0026amp; Mingque, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Unfortunately, almost a couple of decades have seen a substantial rise of terrorist incidents across the world. Domestic political issues are usually related to these terrorist attacks. Although we have noticed recent incidents of terrorists\u0026rsquo; incidence related to geopolitical differences internationally and also the power games of the global super powers. Most of the Asian countries have been suffering for last few decades. Furthermore, terrorism is an impediment for the flow of international tourism in the current years. Insecurity is one of the major hurdles in worldwide tourism (Alam \u0026amp; Mingque, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; P. J. Buckley \u0026amp; Klemm, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Individual security is a main concern for global tourists. Nobody can compromise on safety. Travelers from abroad will look for safe and secure locations rather than ones that have been impacted by terrorism. The tourism industry, travelers, and host communities now seriously worry about terrorism on a global scale (Mansfeld \u0026amp; Pizam, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Additionally, terrorist\u0026rsquo;s incidences have long-term effects on any country or region. Its effects are more than the immediate fatalities, but terrorists\u0026rsquo; objective is to achieve social or political causes through violence.\u003c/p\u003e \u003cp\u003eClimate change is another important non-economic determinant that affects the tourist arrivals. The nexus between tourism demand and climate change is pivotal. While section of tourist destination, climatic conditions of the tourists\u0026rsquo; destination is an important factor. There are plenty of previous studies that discussed the impact of tourism on climate change. The increase in tourism-related emissions defies public opinion and trends in other industries. According to polls, people still regarded climate change as the biggest threat even during the pandemic (EIB, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, other industries are following the Paris Climate Agreement and striving for zero emissions by 2050 (UNFCCC, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), during that time, emissions from tourism are predicted to triple (G\u0026ouml;ssling \u0026amp; Scott, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Even though since the 1990s there have been a significant number of scholarly articles discussing sustainable tourism, studies on sustainable tourism: The travel industry has not done much to slow down climate change (R. Buckley, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The sector has not been sufficiently prepared by the actions we have taken in the last 30 years for the upcoming 30 years of increasing climate change influences and the transition to a decarbonized universal economy (Scott et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a result, there is no evidence in the Paris Agreement that has changed the way that tourism policies and planning take climate change into account or changed the sector's emissions growth trajectory. Analyzing the determinants of international tourism demand is important from numerous points of views. First, it helps the policy makers to bring reforms in tourism industry to boost up tourism demand and bring the equilibrium in demand and supply of tourism. Second, marketing management needs knowledge of the determinants that affects type of tour and tourist destination choice and forecast of international tourism flows in long-run and short-run to formulate strategic planning and marketing decisions.\u003c/p\u003e \u003cp\u003eThis study is organized as follows: Second section describe the literature review discussing on the relationship among the economic, non-economic tourism determinants and tourism demand. The third section describe the data and methodology to be used in the study. Section four describe results and discussions. Lastly, conclusion and policy recommendations are described.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eIn this section we investigate the most significant earlier studies related to the economic and non-economic determinants of international tourism, which are the explanatory variables of this study. The literature on tourism variables paints a clear picture of the variables influencing demand for travel worldwide. These elements have been assessed as both non-economic and economic determinants. Consequently, we will dig deeper into previous literature regarding tourism demand determinants. Additionally, World incomes per capita, relative price and nominal exchange are the main economic factors. While the political stability and climate change are the non-economic factors.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Economic determinants\u003c/h2\u003e \u003cp\u003eIn previous literature different economic determinants have been focused, which affects international tourism directly and indirectly. Concerning the economic determinants, number of tourist arrivals has been affected differently by different determinants. Moreover, we examine the previous literature on the connections among demand for tourism and income levels as well as between demand for tourism and exchange rate. Number of tourist arrivals and relative price also reviewed in this study. There is a sufficient number of literature on forecasting, modeling the factors that influence tourism demand, and defining its elasticities. Numerous surveys that use the meta-analysis method are justified by the volume of research on these topics, specifically (Crouch, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Lim, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOh and Ditton (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) examine the use of the real exchange rate in international tourism demand models, as well as the application of relative prices and the nominal exchange rate to estimations. According to their findings, estimates that employ the exchange rate and relative prices independently have better forecasting abilities and more desirable characteristics (Chang \u0026amp; Mcaleer, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Researchers use daily information on fluctuations in exchange rates as well as arrivals of tourists to Taiwan from the US, Japan, and other nations between January 1990 and December 2008. Using a heterogeneous autoregressive model, in the series of tourist arrivals, the approximate long-memory properties are recorded. Martins et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) examine the association among some macroeconomic determinants and tourism demand. The authors used tourist expenditures and tourist arrivals as the proxies for tourist demand. According to their results, the proxy of tourist arrivals is more subjective by the world income while, the exchange rate is more important for tourist expenditures.\u003c/p\u003e \u003cp\u003eChao et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) examined how domestic pricing and exchange rates affected the travel and tourism sector. They have demonstrated that exchange rate is a key factor in the volume of travelers that a States have and revenue collected from international visitors. Moreover, devaluation in the local currency may reduce the tourism receipt. While appreciation in the currency may cause the inflation for inbound tourist that leap towards decrease in the number of tourist arrivals. Furthermore, tourist arrival is also different in the developed countries from the developing world.\u003c/p\u003e \u003cp\u003eCheng et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) different results: international tourists to America are more delicate to exchange rate while American tourists are affected by income. Similarly, the cost of living at the destination of a tourist depends on the exchange rate, which defines the purchasing power and price of tourism goods and services (Crouch, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Webber, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Consequently, inbound tourism is likely to be affected by the exchange rate volatility. They further propose that the instability in exchange rate affects the decision regarding a tourist\u0026rsquo;s destination, as the exchange rate has the same impact on relative price at the destination country.\u003c/p\u003e \u003cp\u003eSong et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) examined the key factors that influence demand for tourism when estimating demand using visitor arrivals and spending. The authors analyze the years 1981 to 2006 using America, Australia, and the UK as the origin states and Hong Kong as the destination. The authors' findings indicate that while tourism demand, as measured by travel expenses, is impacted more by the real exchange rate, tourism demand, as measured by visitor arrivals, is more prejudiced by income. Additionally, they discover that aggregate models produce better results than per capita models. Morley et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) have argued that a growth in the WGDP, a decrease in the value of the domestic currency, and a drop down of comparative domestic prices does help to increase the number of tourist arrivals. And tourism receipt or expenditure level has been affected by the WGDP, because international tourism has been categorized as a luxury service which is influenced by income. They stated that on average, tourist arrivals increased by 1.2% annually and international tourism receipt decreased by 2% annually. Song and Witt (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) explored the growth in international tourism through examine the general-to-specific modeling and forecasting approach for tourism demand, taking into account the application of error correction models, autoregressive distributed lag processes, and cointegration analysis. They found that income and price of tourism goods are two main variables in the research of tourism demand.\u003c/p\u003e \u003cp\u003eLim (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) performed a meta-analysis of 70 studies, related to relative price, income and transportation cost and tourism demand. He found 6.2%, 2.8% and 8.3% effect on tourism demand by transportation cost, income and relative price respectively. Findings for relative price and world income are strong and more robust then the transport cost. But magnitude effects are strongly significant, which is an evidence of its importance in tourism demand. In an another, meta-analysis study, (Peng et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) review 195 research studies published from 1961 to 2011, examining the influence and characteristic of the income and price on the tourism demand elasticity. Almost all the authors find the significant influence of the explanatory variables. The sample size, the time period, the frequency of the data and the methodology have also influence on the assessments.\u003c/p\u003e \u003cp\u003eCrouch (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) analyzed 44 countries to examine the influence of price and income on inbound tourism demand. He observed that the methodology used in the papers, the data type, the variables involved, the country considered and the time period are the causes of variation of impact of price and income on demand of international tourism in previous studies. Nevertheless, the variables listed above are unable to account for the volatility in the price elasticities. The problem might lie only in how the price is defined. However, the variations across the multiple studies can be used to explain the volatility in the results for the income elasticities of tourism demand.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Non-economic factors\u003c/h2\u003e \u003cp\u003eIn addition to monetary factors, some non-economic factors also affect international tourism. This section examines the most significant body of prior research on the non-economic factors that influence international travel. An abundance of research studies done on the relationship between non-economic factors and tourism demand. The connection between political stability and international tourism was pioneered through the effort of studies such as, Kosters (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1984\u003c/span\u003e), Mathews (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1975\u003c/span\u003e), and Matthews and Richter (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImpact of noneconomic determinants is always a hot topic in tourism research. Moreover, Saha and Yap (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) examine how political unrest and terrorism interact to impact the growth of tourism using panel data from 139 nations between 1999 and 2009. The study assesses how much terrorism and political unrest can harm a nation's travel and tourism sector. The findings show that political unrest has a far more negative impact on travel than do isolated terrorist incidents. In the most recent study of Aydin (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) examined the nexus among political stability, renewable energy, and tourism demand in turkey from 1996 to 2018, utilizing ARDL (Autoregressive Distributed Lag) approach. The results indicate that long-term estimates indicate that tourism is significantly and positively impacted by both political stability and the use of renewable energy. Ingram et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) investigated how tourism and political unrest are related and sensitive to each other in Thailand. According to the study, political unrest might only have a short-term impact on nations with a positive reputation, like Thailand. It is possible that tourist spots with a strong reputation and high rates of return business can withstand the negative media attention better than other locations with a weaker reputation. The further suggested that the duration of the political unrest may also be related to the decline in tourism.\u003c/p\u003e \u003cp\u003eCausevic and Lynch (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) investigated by 52 in-person interviews with research participants connected to state, entity, cantonal, and local public and private sectors. The cities of Sarajevo, Banja Luka, Mostar, and Bihać hosted the interviews in the summers of 2006, 2008, and 2010. Additionally, a few participants were questioned more than once. Their findings are the main issues preventing tourism from developing further are the complicated political and economic systems, rules, and regulations left over from the war, as well as their effects on political instability.\u003c/p\u003e \u003cp\u003eAdditionally, researchers have consistently focused on the connection between the demand for tourism and climate change during the last decade. The most pertinent quantitative methods for assessing how climate change is affecting tourism (Rossell\u0026oacute;-Nadal, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Scott, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Scott et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This analysis demonstrates that the effects of climate change can be measured in three ways: (1) by examining changes in the physical conditions that are critical to tourism; (2) by evaluating the allure of tourist destinations using climate indexes; and (3) by modeling demand for tourism that takes climate determinants into account. Michailidou et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) revealed that in addition to being one of the economic sectors most vulnerable to climate change, tourism also contributes to it. As the effects of climate change become more and more concerning, the travel and tourism industry needs to act quickly to mitigate its emissions and modify its travel destinations and businesses to better accommodate the changing climate.\u003c/p\u003e \u003cp\u003eAs a result, after carefully reviewing the literature on tourism, it is noted that there is disagreement regarding the direction and influence of various factors on demand for tourism. Subsequently, a significant research gap exists in investigating the influence and direction of economic and non-economic determinants on tourism demand of BRICS states.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data and Econometric Estimation","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data and variables\u003c/h2\u003e \u003cp\u003eFor the five BRICS nations [Brazil, Russia, India, China, and South Africa] a balanced annual data covering the years 1996\u0026ndash;2022 were used in this study. All the variables gathered from the various sources are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In line with the previous literature of tourism like (Corne \u0026amp; Peypoch, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Martins et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) tourist arrival (TA) is used as proxies of tourism demand. Following (Rasool et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), for the proxy of the world income, the real per capita GDP (WGDP) of the world is used in this study. Additionally, the nominal exchange rate (XR) is also introduced as one of the main explanatory variables. XE is described in terms of the American dollar, the most leading medium of exchange in the world economic system. All TA,WGDP, and EX are drawn from the World Bank's database (WDI, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the variable description and definition.\u003c/p\u003e \u003cp\u003eFurthermore, a relative price (RP) is found another important factor for tourism demand in tourism literature. Consumer price index (CPI) of a tourist destination state is commonly used as a proxy for RP in tourism literature. CPI measures the prices level of a basket of goods and services in a country. We will calculate the relative prices using the following formula\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${RP}_{it}=\\frac{{CPI}_{it}}{{CPI}_{jt}}*{ER}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CPI}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the consumer price index of the tourist destination country, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CPI}_{jt}\\)\u003c/span\u003e\u003c/span\u003erepresents the US price level, this has been used as the proxy for the world consumer price index (CPI). Both \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CPI}_{it}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CPI}_{jt}\\)\u003c/span\u003e\u003c/span\u003e have been gathered from the World Bank database (WDI, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, political stability and absence of violence (PS), is used as one of the important non-economic factors of world tourism demand in many studies in tourism literature, such as (Aydin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ingram et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Saha \u0026amp; Yap, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The data of PS is collected from the World Governance Indicators (WGI) database (WGI, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While Climate change (CC), A baseline climatology covering the years 1951\u0026ndash;1980 is used to calculate annual estimates of mean surface temperature change. (Unit: Degree Celsius). The data of CC is collected from Food and Agriculture Organization of United States (FAOSTATE, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur inquiry seeks to determine the impact of economic and noneconomic determinants that influence tourism demand. In the current study, in order to help us to analyze the effects of these variables, we constructed the empirical model using the standard Kaya Identity.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${LnTA}_{it}={\\omega }_{it}+{\\pi }_{1,it}{LnWGDP}_{1,it}+{\\pi }_{2,it}{LnXR}_{it}+{\\pi }_{3,it}{LnRP}_{,it}+{\\pi }_{4,it}{LnPS}_{it}+{\\pi }_{5,it}Ln{CC}_{it}+{\\mu }_{it} \\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${LnTR}_{it}={\\omega }_{it}+{\\pi }_{1,it}{LnWGDP}_{1,it}+{\\pi }_{2,it}{LnXR}_{it}+{\\pi }_{3,it}{LnRP}_{,it}+{\\pi }_{4,it}{LnPS}_{it}+{\\pi }_{5,it}Ln{CC}_{it}+{\\mu }_{it} \\left(2 \\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe following notation is used in the models mentioned above: The logarithm, or Ln, is the regressand \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\(TA\\) \u003c/span\u003e \u003c/span\u003e stands for number of international tourists arrivals, while \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\(WGDP, XR, RP,PS and CC\\) \u003c/span\u003e \u003c/span\u003e stands for world GDP per capita, nominal exchange rate (national currency in term of \u003cspan\u003e$\u003c/span\u003eUS), relative price with respect to US, Political Stability and Absence of Violence/Terrorism. Similarly, \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\(\\omega , and {\\pi }_{1,\\dots \\dots , 5}\\) \u003c/span\u003e \u003c/span\u003eare slope and the regressor coefficients that need to be calculated. In the regression equations, \u0026micro; represents the error term, and i, t, and time represent the cross-sectional units and time, respectively. In the following sections, we detail econometric approaches to the in-depth examination of the aforementioned models.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables, sources, and definitions of the data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSymbol\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMeasured\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSource\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTourist Arrivals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternational tourism, number of arrivals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWDI, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorld GDP per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWGP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorld GDP per capita current US\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWDI, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExchange rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNominal exchange rate in term of \u003cspan\u003e$\u003c/span\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWDI, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative price with respect to US\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCalculate the relative prices using the following formula\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({RP}_{it}=\\frac{{CPI}_{it}}{{CPI}_{jt}}*{ER}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CPI}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the consumer price index of the tourist destination country, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CPI}_{jt}\\)\u003c/span\u003e\u003c/span\u003erepresents the US price level, this has been used as the proxy for the world consumer price index (CPI). Both \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CPI}_{it}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CPI}_{jt}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWDI, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolitical Stability\u0026nbsp;and Absence of Violence/Terrorism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWGI, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual estimates of mean surface temperature change are measured with respect to a baseline climatology, corresponding to the period 1951\u0026ndash;1980(Unit: Degree Celsius)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFAOSTATE,23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Econometric Approaches\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the methodological framework of our study. The study begins by outlining six econometric approaches for evaluating the pre-designed models. Where initially, slope homogeneity and cross-sectional dependence are estimated. In the second a unit root test was utilized to verify the stationarity of the data utilized in this investigation. The models are checked for cointegration relationships in the third stage. In the fourth step, a long-run link of the modelled variables are tested using the Fully Modified Least Square (FMOLS) technique. To determine the causal measurements of the modelled variables have been done in the fifth step. Lastly, Dynamic Ordinary Least Square (DOLS) model is used to investigates the robustness of the long-run results.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Cross-sectional dependence and slope homogeneity tests\u003c/h2\u003e \u003cp\u003eCross-sectional dependencies can result from economic and noneconomic factors as well as demand for tourism across national boundaries. To prevent irrational results, modern researchers like (Hussain \u0026amp; Zhou, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qi et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) advise addressing this matter prior to formal data analysis. This research focuses on the BRICS countries\u0026mdash;Brazil, Russia, India, China, and South Africa\u0026mdash;as a group of five nations. Given the possibility of interdependencies economics, noneconomic determinants and tourism demand among these states, we plan to use the (Pesaran, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) cross-sectional reliance test to determine whether cross-sectional dependence exists. We can determine that the countries show cross-sectional dependency if we reject the null hypothesis of the test, which states that there is no cross-sectional dependence.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$${CD}_{pesaran}= \\sqrt{\\frac{2T}{N(N-1)}}\\left[\\sum _{i=1}^{N-1}\\sum _{j=1+1}^{N}\\widehat{{r}_{ij}^{2}}\\right] \\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo address potential slope heterogeneities between the BRICS economy panels, this study additionally used the panel slope homogeneity (SH) test in addition to the cross-sectional dependence (CD) test. This stage is crucial since estimation is predicated on the supposition of slope homogeneity may provide false results if the slopes are heterogeneous, heterogeneous (Sheng Yin \u0026amp; Hussain, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The slope homogeneity (SH) test, which was developed by Hashem Pesaran and Yamagata (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), was employed in this study. This test investigates the reliability of slopes throughout the panels using the null hypothesis of slope homogeneity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Panel unit root tests\u003c/h2\u003e \u003cp\u003eVarious unit root tests with distinct benefits can be found in the literature. These tests assess if the series exhibits time-averaged stationary behavior. Using the method of Hussain et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), if there are non-stationary features in the variables at the level, to confirm the equilibrium relationship between them, a cointegration test is carried out. There is a considerable chance that cross-sectional dependence from first-generation stationarity tests will result from false-positive regression. To address this problem, this study used Cross-sectional Augmented IPS (CIPS) test, (Pesaran, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), which was developed using the Cross-sectional Augmented Dickey-Fuller (CADF) technique:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$CIPS=\\frac{1}{N}\\sum _{i=1}^{N}{CADF}_{i} \\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIf, at a 5% significance level, the estimated value of CIPS is less than or greater than the critical values, the test's null hypothesis of non-stationarity can be rejected. After determining that the variables demonstrated stationarity and evaluating the degree of stationarity, we ran multiple cointegration tests. We will be able to ascertain the long-run equilibrium relationship between the variables by using these estimators. To find a long-term, non-spurious correlation between the variables, two-panel cointegration tests are used: the (Westerlund, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) bootstrapped panel cointegration test and the (Pedroni, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) panel cointegration test. Outline for panel cointegration testing, suggested by (Pedroni, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) forms the Engle and Granger two-step procedure. Prior to addressing data heterogeneity, this method eliminates individual-specific deterministic trends and short-run parameters (Karmaker et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Panel cointegration tests\u003c/h2\u003e \u003cp\u003eAfter confirming the unit root test, this study used Westerlund (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) to explore the long run association among the study underlying variables. These techniques advantageous by easing the shared factor limits normally used to residual based tests for cointegration. These recently proposed tests concentrate on structural dynamics, unlike residual dynamics, which can be constrained by common factors. This distinction is crucial because common factor limitations have the potential to significantly lower the influence of residual-based cointegration analysis, underscoring the need of taking structural dynamics into account when conducting these kinds of analyses. The requirement that the short-term and long-term adjustment processes be the same is eliminated by doing away with this restriction. By using the bootstrap procedure developed by (Westerlund, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), we can obtain reliable critical values and minimize cross-section-dependent distortion effects. The findings displayed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrate that the presence of cointegration is strongly supported by the results of the (Westerlund, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and (Pedroni, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) bootstrapped cointegration tests. The fact that the null hypothesis was rejected in both tests suggests that the models are cointegrated over a long time horizon.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Fully Modified Ordinary least squares (FMOLS)\u003c/h2\u003e \u003cp\u003eThe effect of the independent variables on the dependent variables is determined using long-run estimators once the cointegration test has shown that the variables have a long-term equilibrium association. This study employs FMOLS and DOLS econometric techniques. The FMOLS and DOLS are single equation estimators for both short- and long-term cointegrated associations. Due to their suitability for the current study and their capacity to address the issues of serial correlations and indigeneity in the error terms, the panel FMOLS and DOLS approaches were chosen for the current investigation (explained further in the portion on results and analysis). Additionally, because FMOLS is a non-parametric method for handling serial correlation, it is resistant to autocorrelation and indigeneity (Akpolat, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Merlin \u0026amp; Chen, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the other hand, the DOLS approach uses parametric techniques to remove issues caused by the explanatory variables' leads and lags (Addis \u0026amp; Cheng, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Merlin \u0026amp; Chen, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, the DOLS technique also imposes additional requirements that all variables be integrated in the same order and yields accurate estimates. It is also more effective for small sample sizes. Above all, the DOLS approach can deal with data heterogeneity and cross-sectional dependence (CD) (Addis \u0026amp; Cheng, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Results could be biased if the CD problem is not addressed. Therefore, this study obtained unbiased and trustworthy estimates by detecting the causality among the study variables and analyzing the longer run affiliation using the FMOLS and DOLS methods (Dogan \u0026amp; Afsar, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The CD test is also examined in this study in addition to DOLS and FMOLS estimations. This is significant because it helps determine which panel unit root test to use the first-generation or second-generation type. The research's observed variable is either stationary or nonstationary, and in the second stage, panel unit root tests are used to judge this. It is imperative to assess the unit root tests prior to performing the panel cointegration test, as failure to do so could result in ambiguity and inaccurate results. Therefore, using a range of unit root tests predicated on the null hypothesis, the current study was conducted. Panel cointegration test is conducted in the third stage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5 Robustness analysis\u003c/h2\u003e \u003cp\u003eThis study used Dynamic Ordinary Least squares (DOLS) to verify the results obtain from FMOLS. DOLS results confirm the sign and magnitude of the estimation from FMOLS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.6 Panel causality test\u003c/h2\u003e \u003cp\u003eTo provide additional information and assist policymakers in developing responsible policies, Among the underlying variables of the study, we performed a panel causality analysis (Dumitrescu \u0026amp; Hurlin, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Compared to conventional Granger causality tests, the Dumitrescu and Hurlin test provides more accurate results by taking cross-sectional dependence into account. Additionally, it functions well in situations through T\u0026thinsp;\u0026lt;\u0026thinsp;N, T\u0026thinsp;\u0026gt;\u0026thinsp;N and data is unbalanced (Karmaker et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As opposed to the null hypothesis, which states that there are no causal relationships in the panel, the alternative hypothesis asserts that there is a causal relationship in at least one group within the sample.\u003c/p\u003e \u003cp\u003eWe take the average of each cross-sectional unit's individual Wald statistics to obtain the statistics for this test, as shown below:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\left[{S}_{n,t}^{DH}=\\frac{1}{N}\\sum _{i=1}^{N}{S}_{i,t}\\right] \\left(6\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere Wald statistics at time t for each group are explained by S_(i,t).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a preliminary, descriptive analysis of the selected variables prior to the formal analysis of the models.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.03e\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30e\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60e\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2300000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.09e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.24e\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e127.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3162.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-70.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e177.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3563.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e139.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWGP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8269.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9371.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10850.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5256.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1120000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2039.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Results of cross-sectional dependence and slope homogeneity tests\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents results that indicate the null hypothesis, namely the absence of cross-sectional dependence, is rejected in view of the CD findings. The findings indicate a correlation between the panel's variables in this section, suggesting that decisions made in one country may affect those in other countries. Thus, it emphasizes how important it is to put new policies into place. Additionally, the SH results demonstrate differences in slope among the panels, highlighting the necessity of important policy directives in order to deal with these issues in an efficient manner (CD \u0026amp; SH). Disregarding these policy distinctions could have a significant effect. Once the presence of CD and SH in the variables has been established, the series must be examined for the possibility of unit roots and their cointegration relationship. However, not every variable at the level is stationary., Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e's results indicate stationarity when converted to first differences. First-order stationarity is thus displayed by the chosen variable dimension, thereby achieving the study's primary goal. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, second panel: The reported results for SH show that there are heterogeneous slopes in the models and reject the null hypothesis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCrossectional dependence test and slope homogeneity tests results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD-test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbs (Corr)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCD test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWGP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSH test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj_delta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Results of panel unit root tests\u003c/h2\u003e \u003cp\u003eWe first looked at the simulated variables for possible cross-sectional interdependence before determining the various integration orders between the variables. We first looked at the simulated variables to see if there might be cross-sectional interdependence, and then we went on to figure out the various integration orders between the variables. The Pesaran CIPS test results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrated that the variables EX, RP, CC and WGP are stationary at level while TA and PS display first-order integration. This demonstrates that the accepted level of the series supports the null hypothesis of unit roots. Based on these results, we investigated the potential equilibrium relationship between the regressand and regressor using cointegration analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Pesaran CIPS \u0026amp; CADF tests of unit roots\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCIPS\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCADF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLevel\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1st -difference\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eLevel\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e1st -difference\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.092\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.671\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEX\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.239\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.858\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.693\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.262\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.570\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.938\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.574\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.344\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.912\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.501\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.163\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.109\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-8.577\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWGP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.610\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.610\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.168\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e: Significance at *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Results of the panel cointegration tests\u003c/h2\u003e \u003cp\u003eOnce the variables were confirmed to show stationarity at the first difference, we proceeded to determine whether cointegration was present in the models. The cointegration results for the TA model using two different cointegration methods (Westerlund, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and (Pedroni, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Existence of cointegration is strongly supported by the results of the Westerlund and Pedroni cointegration tests. This suggests a strong long-run relationship among the variables in the model, indicating a stable equilibrium relationship among the regressor and regressed in the model and bolstering the validity of our cointegration analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of panel cointegration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWesterlund (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2005\u003c/span\u003e\u003cem\u003e) cointegration\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTA-model\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome panels are cointegrated (VR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.8739**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll panels are cointegrated (VR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.3901*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePedroni (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e\u003cb\u003e) cointegration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModified Phillips-Perron t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhillips-Perron t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.3926*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugmented Dickey-Fuller t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.3722***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKao Test (1999)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModified Dickey-Fuller t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.7947***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDickey-Fuller t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.2941**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugmented Dickey-Fuller t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.5175**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnadjusted modified Dickey-Fuller t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.8224***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnadjusted Dickey-Fuller t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.3013**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003eNote\u003c/b\u003e: VR is variance ratio, and significance at *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Panel FMOLS results\u003c/h2\u003e \u003cp\u003eAs the BRICS countries' observed variables are cointegrated, Next step involves estimating long-term association between the following variables: relative price, exchange rate, political stability, world GDP per capita, tourist arrivals, and climate change. A range of estimators, including between and within dimension groups like the FMOLS, DOLS, and ordinary least squares (OLS) estimators, are available to estimate a cointegration vector using panel data. When estimating the long-run relationship in cointegrating panels, the OLS method has at least two significant drawbacks: it may affect from heteroscedasticity and have an un-standard distribution of OLS estimations, which causes the standard testing procedure to fail. Additionally, the limits of the sample bias of the parameters are larger until and unless the regressors are severely exogenous. (Ugrinowitsch et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Consequently, it is generally not feasible to use the OLS estimation for reliable inference (Kao \u0026amp; Chiang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ozarslan Dogan \u0026amp; Afsar, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On the other hand, standard errors are consistently estimated by FMOLS and DOLS, which can be utilized for assumption. Phillips et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) initially introduced the panel FMOLS estimation, a non-parametric technique, to estimate cointegration coefficients and resolve endogeneity in the regressors and further refined by (Pedroni et al., 2001) to deal with the endogeneity in the regressors and estimate the cointegration coefficients. The panel FMOLS estimation considers the possibility of a constant term and the potential relationship between the error term and the first differences of the regressors in order to handle corrections for serial correlation (Phillips et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Additionally, the panel DOLS model, a fully parametric method was first put forth by (Saikkonen, 1992) and (Stock and Watson, 1993), DOLS estimator was then applied to panel datasets by (Kao \u0026amp; Chiang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). As a practical alternative for the panel FMOLS cointegration estimator, the panel DOLS estimation is advised by(Mark et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Additionally, the endogeneity and serial correlation can also be adjusted with the panel DOLS estimators. To account for endogenous feedback, lead and lag differences of the explanatory variables are added to the long-run regression in the panel DOLS estimation. Furthermore, the panel DOLS estimator employs parametric adjustment to the errors to produce an unbiased estimator of the long-run parameters by incorporating the past and future values of the differenced I(1) regressors (Mark et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e displays the results of the panel cointegration relationships obtained using the FMOLS and DOLS pooled estimation technique. These tables investigate the validity of long-term linear cointegration relations among observed variables for the BRICS nations. The analysis takes tourist arrivals as dependent variable. The panel FMOLS findings show that all variables are statistically significant at 1 percent level of significance with expected directions. EX, WGDP, and PS are positively influencing TA, while RP and CC are negatively influencing TA. Generally, the findings of this study indicates that there is a strong long-run relationship among EX, WGDP, RP, PS, and CC on TA. The finding in FMOLS model also indicates that, a 1% increase in EX correlate with an increase of 1.7% in TA. The study's findings are consistent with (Martins et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)(Peng et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, ER has highest impact on TA followed by WGDP. A 1% increase in WGDP and PS leads to increase in TA by 1.6% and 0.12% respectively, which is consistence with vv(Aydin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Martins et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Webber, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Conversely, a 1% increase in RP and CC correlated to decrease in TA by 0.8% and 0.17% respectively. The results of this study are coinciding with plenty of previous like (Aydin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Dogan \u0026amp; Afsar, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Robustness analysis results\u003c/h2\u003e \u003cp\u003eThe estimated results from FMOLS are re-estimated using DOLS estimation to verify the robustness of the findings. The empirical results in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that similar results with consistent magnitudes and directions are produced by the variables. We may therefore state with confidence that these trustworthy estimates are important for theory, policy, and the actual application of policy by policymakers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLong-run estimation with FMOLS and DOLS estimators (TA-model)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFMOLS\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eDOLS\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCoefficients\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003et-Statistics\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP-values\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCoefficients\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003et-Statistics\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP-values\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLnEX\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.7013***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.5253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.1019***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.1937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLnWGDP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.6025***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.6089***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.3035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0787***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.1472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.5429***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.5475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1213***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2143***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.7033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLnCC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1717**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.9243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.4527***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.8483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: *, * *, and *** indicate the level of significance at 1%, 5%, and 10%, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Results of panel causality test\u003c/h2\u003e \u003cp\u003e(Sheng Yin \u0026amp; Hussain, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) highlighted the substantive policy significance of causal dimensions in modern econometric research. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e present the findings of panel causality testing that was done for the study. The empirical results show the EX, RP and PS have unidirectional causalities on TA. Contrary, TA has unidirectional casualty on CC, while WGP and TA validate the presents of bidirectional links. Moreover, RP has the strongest causality on TA followed by WGP. However, non-economic factors like PS and CC demonstrated a weak causal link.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eD\u0026amp;H-Granger non-causality test results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCausality direction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW-statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{\\varvec{Z}}\\)\u003c/span\u003e\u003c/span\u003e-statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003ee-model\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u0026thinsp;\u0026ne;\u0026thinsp;EX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.41842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEX\u0026thinsp;\u0026ne;\u0026thinsp;TA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.14561*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.73105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u0026thinsp;\u0026ne;\u0026thinsp;RP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.37037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRP\u0026thinsp;\u0026ne;\u0026thinsp;TA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.20147***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.68133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u0026thinsp;\u0026ne;\u0026thinsp;PS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.57297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS\u0026thinsp;\u0026ne;\u0026thinsp;TA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.21860*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.74326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u0026thinsp;\u0026ne;\u0026thinsp;WGP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.9716**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.5705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWGP\u0026thinsp;\u0026ne;\u0026thinsp;TA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.81515**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.33363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u0026thinsp;\u0026ne;\u0026thinsp;CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.43259**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.03956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u0026thinsp;\u0026ne;\u0026thinsp;TA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.26786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote\u003c/b\u003e: α\u0026ne;\u0026#120573; indicates that α does homogeneously cause \u0026#120573;, D\u0026amp;H is Dumitrescu \u0026amp; Hurlin (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and Significance at * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study examines the influence of some important economic determinants and non-economic factors, such as political stability/ absence of violence, climate change, WGDP, relative price and exchange rate on inbound number of tourist arrivals for the panel of BRICS countries over the period of 1996 to 2022. The model was considered, Tourist Arrivals (TA), measuring for the demand of the tourism. quantity. Prior to using a panel unit root test to account for cross-sectional dependency, the study looks at cross-national dependency. Correspondingly, cointegration relationships are frequently supported by controlling cross-sectional dependence because of the presence of non-stationary variables. Following the establishment of cointegration relations, the study applies FMOLS and DOLS estimators to confirm long run relationships among the variables. To sum up the findings, TA increase due to rise in WGDP, ER and PS. While, CC and RP are adversely affecting TA. The results reveal that an upsurge in WGDP, a depreciation of the domestic currency, a decline of relative indigenous price, decrease of climate change and increase in the political stability encourages boosting up the international tourism demand. Moreover, the exchange rate is the most influencing factor followed by WGDP for tourism demand. Relative price has the least impact among all three economic determinants. In addition, both economic and non-economic determinants are also statistically significant. The proxy of climate change is negatively affecting the tourism demand while political stability has a minor positive impact on number of arrivals. All the estimates are in line with (Aydin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)(Li et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)(Martins et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In conclusion, the findings reveal that economic factors are more influential than non-economic factors to upsurge the tourism sector of BRICS country.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Policy recommendations\u003c/h2\u003e \u003cp\u003eIt is just an effective increase in tourism and economics literature. Policy makers who are developing strategic tourism policies can benefit from our work. Additionally, prior to evaluating the influence of economic and non-economic variables, they ought to focus on the selection and characterization of the explanatory and dependent variables. Second, both Arrivals and Expenditures are important proxies for the demand for tourism may respond differently to global income, relative prices, the nominal exchange rate, political stability and climate change. Consequently, Policymakers should take this into account when determining which target variables to include in their policies. Since the ultimate objective is to increase the tourism demand to boost up the income from tourism sector. Lastly, political stability/absence of terrorism is a big hurdle for the development of the demand of international tourism. Therefore, there should be a prime objective to keep the country peaceful and politically stable to attract international visitors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe primary manuscript text, from the introduction to the end, was written by Mir Alam. Faiza Kiran assisted Mir Alam in gathering data. 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The role of physical and human capital in FDI-pollution-growth nexus in countries with different income groups: A simultaneity modeling analysis. \u003cem\u003eEnvironmental Impact Assessment Review\u003c/em\u003e, \u003cem\u003e91\u003c/em\u003e(January). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eiar.2021.106664\u003c/span\u003e\u003cspan address=\"10.1016/j.eiar.2021.106664\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Economic and non-economic factors, relative price, tourist arrivals, BRICS countries","lastPublishedDoi":"10.21203/rs.3.rs-4143234/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4143234/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent decades, economic and non-economic factors have significantly shaped tourism demand, affirming attention from researchers and policymakers. This study employs advanced econometrics techniques, including cross-sectional dependency, slope homogeneity, unit root, cointegration and long-run estimation, to explore the impact of economic determinants (world GDP per capita, exchange rate, interest rate, and relative price) and non-economic factors (political stability and climate change) on tourism demand in BRICS economies from 1996 to 2022. Findings indicate that exchange rates, interest rates, political stability, and world GDP per capita positively influence tourism demand, while climate change and relative price negatively impact it. Moreover, bidirectional causality exists between world GDP per capita and tourism demand, and one-way causation is observed for exchange rate, relative price, and political stability. In conclusion, the economic determinants significantly impact tourism demand more than non-economic factors. The study provides policy guidelines for enhancing tourism demand.\u003c/p\u003e","manuscriptTitle":"Unlocking the Dynamic Impact of Economic and Non-Economic Factors on Tourism Demand in BRICS Economies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-26 07:22:31","doi":"10.21203/rs.3.rs-4143234/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eae39eb7-7c06-47cd-b5c0-1edb07774e5f","owner":[],"postedDate":"March 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-28T15:44:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-26 07:22:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4143234","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4143234","identity":"rs-4143234","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}