Economic Growth, Energy Mix, and Tourism-Induced EKC Hypothesis: Evidence from Top Ten Tourist Destinations

Abstract

The tourism sector makes a sizable contribution to a country’s gross domestic product, increasing employment opportunities, foreign currency earnings, and economic diversification strategies. This paper uses the Environmental Kuznets Curve (EKC) model to analyze the effects of tourism on the environment in the world’s top 10 tourist countries from 1972 to 2021. Panel cointegration and second-generation unit root tests are suggested due to the presence of cross-sectional dependence and slope heterogeneity. A cross-sectional-autoregressive-distributed lag (CS-ARDL) model is applied to evaluate the marginal influence of environmental variables other than tourism on carbon dioxide (CO₂) emissions. These variables include fossil fuels, renewable energy, and nuclear energy. For the purpose of testing robustness, both the augmented mean group (AMG) and the common correlated effects mean group (CCEMG) estimators are employed. The CS-ARDL supports the EKC hypothesis in the short run and long run, but it is not significant in the short run. The existence of EKC demonstrates that rising income leads to fewer CO₂ emissions. All forms of environmental degradation can be accelerated by using fossil fuels. The results of this study indicate that CO₂ emissions can be reduced by using renewable energy and nuclear energy. A rise in tourism activity has a positive impact on environmental quality. The best-attended tourist spots around the globe are those that, in the long run, implement clean energy-related technology and promote ecotourism.

1. Introduction

The environment is a key element of many tourist destinations, as it provides natural resources to meet tourist demand and plays a substantial role in tourism growth. A number of countries rely on the tourism sector to promote more rapid economic growth as tourism substantially contributes to gross domestic product (GDP) and employment. Tourism is the world’s third largest economic sector after fuels and chemicals [1]. According to World Travel & Tourism Council (WTTC) and the World Tourism Organization (UNWTO), the contribution of tourism to the global GDP was 10.4% in the pre-pandemic period, with 1.5 billion international tourist movements, where 4% was added in 2019. Due to COVID-19, the tourism industry, which was the most dynamic sector up until 2019, has been confronted with catastrophic issues [2,3]. In 2021, there were approximately 427 million international tourists who visited other countries all over the world, which grew by 4% in 2021 compared to the previous year, 2020; although they were still 71% lower than the levels seen in 2019 [4,5].

The great hearsay is that the recovery of the tourism industry from the COVID-19 crisis proceeded at a healthy clip during the first three months of 2022 with 117 million international tourist arrivals, with an especially excellent performance in the months of February (−61 percent) and March (−56 percent) [6]. In 2021, Mexico welcomed 31.9 million visitors from other countries, making it the country with the largest number of tourists and surpassing France, which had previously held the record for the most visitors [7]. According to a poll conducted by the UNWTO Confidence Index, the year 2023 may mark the beginning of a possible comeback of foreign visitors to the levels seen in 2019 [6].

This increasing number of tourists raises the level of energy consumption by the countries hosting the highest tourist flow. Travel and tourism are significant drivers of global warming because of their high levels of carbon dioxide emissions, rising levels of energy consumption, and other factors. Previous studies find that tourism and energy have a causal relation, which indicates that tourism is a contributor to CO₂ emissions [8]. Still, countries with sustainable tourism practices demonstrate that increasing tourism activity negatively impacts CO₂ emissions, as in five Mediterranean countries [8]. Renewable energy, which has been shown to have an adverse impact on CO₂ discharges and an accelerating effect over the long term, continues to be an effective alternative to traditional fossil fuels as a source of energy [9]. Tourism can play a vital role in preserving the environment. Still, difficulties appear when tourists and service providers decline to observe nature just for pleasure rather than taking the potential adverse effects into account.

The tourism industry includes lodging and transportation, which use a large quantity of energy that negatively affects the environment through increasing greenhouse gas emissions and CO₂ emissions across countries. Around 5% of the worldwide CO₂ emissions are caused by tourism. The transportation sector accounts for 75% of the sector’s emissions, of which 50% is attributable to air travel. In comparison, the accommodation sector makes up 22%, and the remaining 4% is attributable to activity-related tourism [10]. According to UNWTO [7], tourism-related transportation-related emissions accounted for 5% of all man-made emissions in 2016 and are likely to rise to 5.3% by 2030. Although it is not often thought of as a highly polluting industry, predictions for tourist development show that by 2035, emissions from tourism-related activities will more than double [10].

On the other hand, according to the projections made by Yong [11], the growth of tourism, which is currently a dynamic and prospering industry in many nations, is expected to decline by the end of this century; this is due to the progress made in human development as well as the fact that developed nations overlook environmental conservation to a significant degree in order to advance their economic progress. However, according to the Environmental Kuznets Curve (EKC) hypothesis, harm to the environment increases during times of economic expansion before leveling off and eventually decreasing [12]. The EKC hypothesis’s inverted U-shaped connection is employed to study probable links between ecological destruction elements and other economic growth drivers, such as energy consumption and tourist flows [13,14,15,16,17,18,19,20]. An expanding subfield of research has started looking at the connections between the environment and the expansion of tourism, examining the possible existence of a relationship among tourism-related activities, levels of energy use, and carbon ejection [14,21,22,23]. Evidence on the nexus between tourism activities and environmental quality (proxied mostly by CO₂ emission) remains inconclusive.

Tourism and environmental-related research have increased in the 2000s, and the existing research on the impact of tourism on CO₂ is not statistically satisfactory [10,24,25]. In the context of tourism, gaining a deeper understanding of the interplay between the environmental effects of fossil fuel use, renewable energy consumption, and nuclear energy may contribute to the inconclusive results in the literature of tourism and environmental quality research. The key objective of this study is to investigate the environmental impact of fossil fuel, renewable energy consumption, and nuclear energy for top tourist countries in the world. The specific objectives of this study are to estimate the long-run and short-run dynamics of the selected factors and test the tourism-induced EKC for the selected top ten tourist countries. We emphasize how much the tourism industry may be responsible for carbon emissions as they have a substantial impact on the calculation of carbon fluxes associated with the industry.

As a proxy for environmental quality, we employ data on CO₂ emissions for the top ten tourist arrival countries between 1972 and 2021. GDP and GDP square are employed to test the EKC hypothesis. Moreover, we distinguish energy consumption according to three sources of energy: renewable energy, fossil fuel, and nuclear energy. All data are extracted from the World Development Indicator (WDI). This study uses balanced panel data and a panel cointegration. The long-term connections are examined using panel cointegration experiments, employing the newly developed approach known as the CS-ARDL to investigate cross-sectional dependence and heterogeneity. The CS-ARDL test is generally believed to be superior to alternative approaches. For robustness, we apply the MG, CCEMG, and AMG estimators, where “Cross-sectional-autoregressive-distributed lag (CS-ARDL); mean group (MG); common correlated effects mean group (CCEMG) and Augmented Mean Group (AMG).”

This study helps enrich the existing literature by combining econometric analysis with policy adroitness. We contribute to the literature in three ways. First, we expand previous studies by examining the determinants of CO₂ emissions in the context of tourism activities and incorporating renewable and nuclear energy sources in the analysis. Second, we have assembled on the hypothesis that the EKC can be an appropriate tool to battle environmental degradation, applying the hypothesis to the context of tourism activities. The EKC can illustrate that achieving higher levels of economic growth can be successful in lowering environmental degradation. Third, we apply a newly developed approach, CS-ARDL, and incorporate the variable of tourism arrivals to test whether tourism arrivals are an important determinant of CO₂ emissions in top tourism destination countries. The implications of this research include environmental issues, economic and tourism development, and ecosystem tourism practices.

2. Literature Review

2.1. Tourism and Environmental Quality

The environment is an essential consideration in the tourism industry, and this consideration refers to the natural resources as well as the natural and built environments of the destination [26]. Tourism and the environment are closely connected with each other: tourism relies on the environment for its resources, and the environment provides its components, which make up the tourism products. It is expected that tourism stakeholders will protect the destination’s biodiversity and prevent environmental degradation. The effective use of natural resources may lead to an improvement in airborne contamination [27]. However, through commercialization, industrialization, the improper use of land, rapid technological improvement, and an increase in the number of visitors’ activities, tourist businesses and their operations may generate or worsen environmental issues [8,28]. The environmental elements are the major drivers of the adverse view of tourism among the local population because it is seen that tourists’ actions might destroy the environment’s hereditary characteristics. This perception is independent of all other indicators [23,29].

However, both the costs to the environment and the advantages it provides have to be taken into consideration for improved tourist growth. There is a possibility that tourism may serve as a driving force behind efforts to preserve the natural region and the ecologically sensitive area. As a result, the benefits of tourism are formed on a variety of bases, and tourism expansion should protect nations’ legacies and heritages, promote social and personal advancement, and halt environmental destruction [30].

Moreover, there are two distinct schools of thought about environmental issues concerning tourism and the nature of the world. Some suggest a causal link between rising levels of carbon dioxide in the atmosphere and the expansion of tourism as the greater levels of energy and fuel consumption associated with tourist-related activities such as transportation and accommodation [31,32]. The other study demonstrated that tourism growth that adheres to environmentally sound infrastructure principles, low carbon energy expenditure, and environmental preservation in tourist sites might reduce emissions [22,33,34]. According to the EKC’s reasoning, the primary factor in determining CO₂ discharges is energy use [35], which leads to a fast deterioration of the environment [36,37]. In this context, renewable sources of power have emerged as a significant competitive option for traditional forms of industrial production in the effort to enhance environmental quality [20,38,39]. Undeniably, renewable energy is making significant strides toward a sustainable environment.

2.2. Tourism and Carbon Emissions

The carbon footprint of tourism is used to calculate the amount of carbon dioxide produced as a result of tourism-related activities in a specific region during a particular time. It is estimated that around eight percent of the world’s total carbon emissions are caused by tourism, with high-income nations being the primary contributors to this carbon footprint [40]. After conducting an analysis of the carbon footprint left by tourist activities in Barcelona, Rico et al. [41] concluded that transportation from and to the port is the primary source of explicit or implicit emissions caused by energy usage. Another study conducted in China pointed out domestic tourism as the primary cause of the high carbon emissions [42]. Furthermore, according to the findings of an analysis of the consumption-based carbon footprints left by regular visitors in Iceland, the size of the carbon footprint left by ordinary tourists is directly proportional to the distance traveled by aircraft [43]. According to an empirical study in Taiwan, the domestic tourist sector, international air flights, and buying from abroad are responsible for 47, 28, and 25 percent of the tourism business’s carbon footprint [25]. The contribution of travel and tourism to Spain’s GDP is 12.3 percent, but its CO₂ release counts for 15 percent of the country’s total discharges. This percentage is higher than the average for the world, which is eight percent, and the majority of Spain’s tourist-related emissions are caused by a few industries that are mostly air freight, motor carriage, or retail businesses [44]. Tourism promotes CO₂ emissions in 50 economies [45].

Tourism, on the other hand, is working to preserve biodiversity by implementing alternative forms of tourism such as ecotourism principles and reducing carbon emissions by maintaining carrying capacity. Ecotourism is one method of mitigating the environmental damage caused by conventional tourism [46]. The word “ecotourism” first appeared in the late 1980s as a direct consequence of the world’s acknowledgment and response to environmentally friendly and global eco-friendly practices [47]. Carrying capacity is the highest number of people that may travel to a tourist location at once without destroying the geological, economic, and socio-cultural surroundings or lowering visitor pleasure [48]. Spain is the world’s second most visited travel destination; the country welcomed 83.5 million visitors from other countries in 2019, up from 82.8 million in the previous year [5]. In Spain, a reverse scenario has been observed: a drop in the proportion of carbon footprints released by domestic tourism from 16.2 percent in 1995 to 14.8 percent in 2007, whereas tourism’s detrimental effects on the environment rose by a quarter [49]. With around 90 million tourists and 63.5 billion in tourism earnings in 2019, France has maintained its position as the most popular tourist destination in the world [5]. However, the tourism industry in France is also responsible for 11% of the country’s greenhouse gas emissions.

2.3. Tourism and Energy Consumption

The tourism industry can potentially raise the demand for energy consumption [50]. The planet will suffer if we continue relying on fossil fuels for our energy needs. At least 112 countries report increased air pollution due to tourist activity [51]. Shaheen et al. [52] provided evidence that there is a correlation between tourist activities, energy consumption, and environmental damage. A significant quantity of greenhouse gases is released into the atmosphere due to the heavy consumption of the energy sources necessary for manufacturing tourism products and services, particularly fossil fuels [49]. France, a high-income country, has had one of the most remarkable tourist volumes in recent years. It meets its energy needs primarily through nuclear power, and its CO₂ emissions from aviation and road traffic are notable [13]. In both developed and developing countries, CO₂ emissions increase as more energy is used [9]. Apergis et al. [53] also concluded in their study of 19 developed and developing countries that the use of nuclear power has a detrimental influence on CO₂ releases, whereas the use of energy has a favorable effect.

A numerical investigation found the nexus between energy and growth in Pakistan using a nonlinear ARDL model [54]. On the other hand, Chunling et al. [55] state that technology and investment in clean energy reduce emissions. In that case, Soylu et al. [56] also state the importance of renewable energy to diminish the emission level, and this is supported by Ahmed et al. [57]. However, according to Dogan et al. [22], tourism benefits Europe’s natural environments. Tourism drives economic growth for many countries but may also increase pollution. Zoundi [9] also identified that the use of renewable energy continues to be an effective alternative to that of conventional energy; it has a mitigating influence on CO₂ releases and an ever-increasing significance throughout time. Activities associated with tourism, such as transport systems, lodging, air transport, and speedboats, raise energy consumption, which is met by the use of fossil fuels such as oil, gas, and coal, all of which directly relate to a rise in CO₂ emissions [58,59].

Numerous studies have been conducted to study the relationship between energy requirements and the climate, e.g., [60]. From 1980 to 2009, the link between CO₂ emissions, economic expansion, and energy consumption in the context of Malaysia was investigated by Saboori and Sulaiman [61], and the result verified the EKC theory when it was applied to fossil fuels and electricity facts with individual incomes; meanwhile, not a single piece of evidence supported the EKC concept when using the whole energy expenditure record, and they stated CO₂ discharges may be brought under control by increasing the use of renewable energy on a global scale. Voumik et al. [62] also investigated the existence of EKC in Bangladesh. Boluk and Mert [63] also emphasized the need to make use of renewable energy sources rather than fuels that come from fossil fuels to control environmental pollution. However, nations with higher incomes see a reduction in their carbon emissions and a rise in their economic production due to using renewable energy sources, whereas in countries with lower incomes, the situation is the opposite [64]. However, this study considers the top ten tourism countries, which are better performing in the progress of their tourism industries. The tourism-based EKC emphasizes the concentration of CO₂ emissions with respect to tourism, GDP, and other variables that are considered in this study, such as renewable energy, fossil fuel consumption, and nuclear energy. The significant contribution of this study is to emphasize the econometric model of tourism that led to EKC. A balanced panel has been developed based on the top ten tourist arrival countries that have never been examined in that field.

3. Materials and Methods

The World Development Indicator served as the source for all of the variables (WDI). The specifics of the variables, as well as descriptive statistics, have been presented here to better understand the variables that we have used. However, this study used balanced panel data and a panel cointegration analysis because there was T greater than N. We looked at data from ten different countries over the course of five decades. Conventional panel data methods cannot be employed if T > N, as they are more appropriate when N is greater than or equal to T. Long-term connections were examined using panel cointegration experiments, such as those conducted by CS-ARDL and Westerlund [65]. However, top tourist countries between 1972 and 2021 were also studied to ensure the study’s robustness. To begin, a series of tests was performed to confirm the premise of slope homogeneity. Second, panel data were examined for any indications of cross-sectional dependencies. In the third step, we ensured that the data remained steady-tested by the second-generation unit root test method. The panel cointegration test was used in the fourth phase. Finally, using the CS-ARDL model that was selected from the unit root test, this estimation investigated the long-term causal links between the variables using short-run and long-run dynamics. However, the slope homogeneity test indicated that it is an important consideration when working with panel data. After that, the Pesaran and Yamagata [66] test for slope homogeneity was performed. The weighted slope of each participant was used to compute the results of this exam. The testing has yielded the following equation:

$$\check{\mathsf{\Delta }}=\sqrt{N}\left(\frac{N^{-1}S\%-k}{\sqrt{2k}}\right)\mathrm{and}{\check{\mathsf{\Delta }}}_{adj}=\sqrt{N}\left(\frac{N^{-1}S\%-k}{\sqrt{\frac{2k\left(T-k-1\right)}{T+1}}}\right)$$

(1)

In addition, cross-sectional dependency (CSD) has significance in the case of increasing economic integration, and the removal of other barriers would likely lead to an increase in cross-sectional dependence in panel data econometrics [67]. Cross-section dependence can lead to information that is biased, misleading, and inconsistent if we choose to disregard the issue and assume that cross-sections are independent of one another [68]. In order to analyze cross-section dependency, this study makes use of large panel data econometrics with weakly exogenous cross-section dependence [69]. For panel data econometric unit root tests, the slope homogeneity and cross-sectional dependency must be present before the tests can be applied. For CSD tests, this formula is the formula to use:

$$\mathrm{CSD}=\sqrt{\frac{2T}{N\left(N-1\right)N}\left({\displaystyle \sum }_{i=1}^{N-1}{\displaystyle \sum }_{K=i+1}^N{\widehat{Corr}}_{i,t}\right)}$$

(2)

${\widehat{Corr}}_{i,t}$ is here pairwise correlation obtained from equation (1). The null hypothesis of the cross-sectional dependence test is that each unit is independent of the others. However, the traditional unit root tests, such as Kao and Chiang [70] and Pedroni [71] may produce erroneous results because of slope variability and cross-sectional dependency [72]. For this reason, we used a second-generation unit root test known as CIPS, created by Pesaran, to determine if the variables in the CSD existence and slope heterogeneity were stationary (2007). A cross-sectional average of ti is required in order to derive a CIPS estimate, as demonstrated in the accompanying illustration:

$$\mathrm{CIPS}=\frac{1}{N}{\displaystyle \sum }_{i=1}^N{t}_i\left(N,T\right)$$

(3)

CIPS is gaining favor in recent studies because of its ability to handle CSD and heterogeneity. The null hypothesis of this test is that the series in question contains a unit root. This shows that if the variable is stationary at the first difference, then a cointegration test should be performed before parameter estimation. To collect CIPS statistics, one must use the CADF approach. Cross-sectional augmented Dicky Fuller (CADF), on the other hand, can be calculated as follows:

$$\Delta Y_{it}={\phi }_i+{\zeta }_i{Y}_{i,t-1}+{\delta }_i{\overline{Y}}_{t-1}+{\displaystyle \sum }_{j=0}^P{\delta }_{ij}{\overline{Y}}_{t-1}+{\displaystyle \sum }_{j=1}^P{\lambda }_{ij}\Delta Y_{i, t-1}+{\epsilon }_{it}$$

(4)

where ${\overline{Y}}_{t-1}$ and $\mathsf{\Delta }{Y}_{i, t-1}$ are the lagged and first difference averages for each cross-sectional series. As the first generation of co-integration tests [73,74] do not take into account the influence of CD, it is impossible to anticipate the features of panel data size distortion using those tests. However, even when assessing cross-sectional data, Kao et al. [70] and Pedroni [71] failed to account for CD. This method is used to identify whether or not there is cointegration because of the CD and heterogeneity of the data as well as the non-stationarity. It is the Westerlund and Edgerton [68] method of data analysis that incorporates heterogeneity in slope, coefficient of variation, and correlated errors. The second-generation panel cointegration technique [65] was used in this study to determine the cointegration linkages between the variables of interest. It is possible to reliably estimate cointegration in cross-sectionally dependent heterogeneous panel data sets by utilizing this method. Four-panel non-cointegration test statistics are created by applying error correction to data from multiple panels. A general definition of this evaluation is as follows:

$${\mathrm{G}}_{\mathsf{\alpha }}=\frac{1}{\mathrm{n}}{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{N}}\frac{{\acute{\mathsf{\alpha }}}_{\mathrm{i}}}{\mathrm{SE}\left({\acute{\mathsf{\alpha }}}_{\mathrm{i}}\right)}$$

(5)

$${\mathrm{G}}_{\mathrm{t}}=\frac{1}{\mathrm{n}}{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{N}}\frac{\mathrm{T}{\acute{\mathsf{\alpha }}}_{\mathrm{i}}}{{\acute{\mathsf{\alpha }}}_{\mathrm{i}}\left(1\right)}$$

(6)

$${\mathrm{P}}_{\mathrm{t}}=\frac{\acute{\mathsf{\alpha }}}{\mathrm{SE}\left(\acute{\mathsf{\alpha }}\right)}$$

(7)

$${\mathrm{P}}_{\mathsf{\alpha }}=\mathrm{T}\acute{\mathsf{\alpha }}$$

(8)

Cointegration in statistical analysis is represented by Pt and Pa, whereas Gt and Ga are group mean statistics. Test statistics are expected when the alternative hypothesis claims that the variables in a model have cointegrating linkages, whereas the null hypothesis claims that the variables in the model do not have cointegrating linkages. Finally, CS-ARDL model has been used to analyze the data. This study used the newly developed approach known as the CS-ARDL with the PMG estimator to investigate cross-sectional dependence and heterogeneity. Chudik et al. [75] developed the CS-ARDL test, which is used for both long- and short-term evaluations in this study. This method is superior to others, such as the MG, PMG, CCMG, and AMG, in terms of efficacy and dependability [42]. This technique solves the concerns of unobserved common components, slope heterogeneity, CSD, and mixed order integration in unit root tests. This is because omitting unobserved common components will lead to incorrect estimation findings. The CS-ARDL can be represented by the equation that is presented below. The equation for the model is expressed as:

$${\mathrm{CO}}_{2\mathrm{it}}={\mathsf{\alpha }}_{\mathrm{it}}+{\displaystyle \sum }_{\mathrm{j}=1}^{\mathrm{P}}{\mathsf{\beta }}_{\mathrm{it}}{\mathrm{CO}}_{2\mathrm{i},\mathrm{t}-\mathrm{j}}+{\displaystyle \sum }_{\mathrm{j}=0}^{\mathrm{P}}{\mathsf{\gamma }}_{\mathrm{it}}{\mathrm{X}}_{\mathrm{t}-\mathrm{j}}+{\displaystyle \sum }_{\mathrm{j}=0}^3{\mathsf{\delta }\overline{\mathrm{Y}}}_{\mathrm{t}-\mathrm{j}}+{\mathsf{\epsilon }}_{\mathrm{it}}$$

(9)

where $\overline{{\mathrm{Y}}_{\mathrm{t}}}={\left(\mathsf{\Delta }\overline{{\mathrm{CO}}_{2\mathrm{it}}},{\overline{{\mathrm{X}}_{\mathrm{it}}}}^{\text{'}}\right)}^{\text{'}}$ and ${\mathrm{X}}_{\mathrm{it}}=\left({\mathrm{GDP}}_{\mathrm{it}},{\mathrm{GDP}}_{\mathrm{it}}^2,{\mathrm{TA}}_{\mathrm{it}},{\mathrm{REN}}_{\mathrm{it}},{\mathrm{FOS}}_{\mathrm{it},}{\mathrm{NUC}}_{\mathrm{it}}\right)\text{'}$.

However, the framework of the analysis implies the EKC statement where CO₂ emission is a dependent variable and tourists’ arrivals, renewable energy, gross domestic product, fossil fuel consumption, and nuclear energy are used as independent variables. However, the baseline equation (equation 10) of the theoretical framework is:

CO_2t = f (GDP_t, GDP²_t, TA_t, REN_t, FOS_t, NUC_t)

(10)

CO_2it = β₀ + β₁GDP_it + β₂GDP²_it + β₃TA_it + β₄REN_it + β₅FOS_it + β₆NUC_it + ε_it

(11)

Here, CO₂ = CO₂ emission_, TA = tourists’ arrivals, REN = renewable energy, GDP = gross domestic product, GDP² = square of gross domestic product, FOS = fossil fuel consumption, and NUC = nuclear energy. Log form of Equation (11) is presented in Equation (12).

LCO_2it = β₀+ β₁LGDP_it + β₂LGDP²_it + β₃LTA_it + β₄LREN_it + β₅LFOS_it + β₆LNUC_it + ε_it

(12)

Here, ${\mathsf{\beta }}_0$ is the intercept term. ${\mathsf{\beta }}_1,{\mathsf{\beta }}_2,{\mathsf{\beta }}_3,{\mathsf{\beta }}_4,{\mathsf{\beta }}_5,{\mathrm{and}\mathsf{\beta }}_{6,}$ are the slope coefficients. The ε is present in the residual, i presents the cross-section country, and t presents the time.

4. Results and Discussions

In

Table 1. Variables’ Names and Details.

Variable Name	Log Form	Indicators’ Name
CO2 emission	LCO2	Carbon dioxide emissions per capita
GDP	LGDP	It is proxied by the gross domestic product percapita (2015 Constant USD)
GDP square	LGDP2	Square of GDP
Tourist arrivals	LTA	Number of arrivals
Renewable energy	LREN	Renewable energy consumption (% of total final energy consumption)
Fossil fuel	LFOS	Fossil fuel energy consumption (% of total)
Nuclear energy	LNUC	Alternative and nuclear energy (% of total energy use)

Source: WDI, 2022.

, the variable lists for this paper are presented. In

Table 2. Descriptive Statistics.

	(1)	(2)	(3)	(4)
Variables	Mean	Sd	Min	Max
LCO2	13.29	1.22	11.40	16.19
LGDP	9.40	1.19	5.70	11.02
LGDP2	89.96	21.19	32.50	121.5
LTA	17.89	0.87	15.75	19.20
LREN	2.30	0.81	−0.49	3.52
LFOS	4.37	0.18	3.74	4.57
LNUC	1.58	1.10	−0.94	3.90

, a summary of the descriptive statistics of all variables is provided. The mean value of LCO₂ is 13.29, the standard error is 1.225, and the maximum value is 16.19, with a minimum value of 11.40. The mean value for the variable LGDP is 9.40, the standard error is 1.12, and the maximum value is 11.02, with a minimum value of 5.70. The mean value for the variable LGDP² is 98.96, the standard error is 21.19, and the maximum value is 121.5, with a minimum value of 32.50. The mean value for the variable LTA is 17.89, the standard error is 0.87, and the maximum value is 19.20, with a minimum value of 15.75. Similarly, the mean value for the variable LNUC is 1.58, the standard error is 1.10, and the maximum value is 3.90. The estimated mean value for the variable LFOS is 4.37, the standard error is 0.18, and the minimum value is 3.74.

The findings of the slope homogeneity test [66] and the cross-section dependence tests [69] are presented in

Table 3. Slope Homogeneity Test Result.

Slope Homogeneity Tests	Δ Statistic	p-Value
$\check{\mathsf{\Delta }}$ test	6.018 ***	0.000
${\check{\mathsf{\Delta }}}_{adj}$ test	8.114 ***	0.000

Note: A test of slope heterogeneity begins with the assumption that slope coefficients are all homogeneous. *** denotes 1% significance level.

and

Table 4. Results of Cross-sectional Dependence Analysis.

Variable	Test Statistics
LGDP	40.15 ***
LCO2	15.28 ***
LGDP2	35.99 ***
LTA	21.28 ***
LREN	2.52 ***
LFOS	−2.62 ***
LNUC	9.42 ***

Note: The significant levels at 1% are indicated by and ***.

, respectively. The result of the slope homogeneity check is presented in Table 3, where the variables are assumed to be heterogeneous among the cross units. The p values were counted to check the test statistics. At that time, the estimated p value was significant, which means the tested hypothesis was rejected. The hypothesis that was tested revealed that the variable has a homogeneity issue across the unit.

It is decisive to ensure cross-sectional dependence (CSD) before beginning an econometric analysis of panel data. There are symptoms of cross-sectional dependence in every panel of data in Pesaran [69], Table 4, which summarizes their findings. There is a cross-sectional dependence between CO₂, GDP, GDP², TA, REN, FOS, and NUC because of similar economic, social, and political parallels. This is to be expected, given the size of the top tourist economies relative to the rest of the world. Additionally, these nations share common approaches to trade and macroeconomics. If the CSD and slope heterogeneity issues can be resolved, then bias in the results can be minimized. Slope homogeneity and CSD tests were followed by second-generation tests such as panel unit root and cointegration. Before continuing, one should clarify that the data have been correctly inserted.

Table 5. CIPS and CADF Unit Root Test.

(a) CIPS Unit Root Test
Variables		Level		First Difference		Order
		Without Trend	With Trend	Without Trend	With Trend
LCO2		−2.170 **	−3.197 ***	−3.639 ***	−3.636 ***	I(0)
LGDP		−2.727 ***	−2.809 **	−3.715 ***	−3.706 ***	I(0)
LGDP2		−1.633	−1.991	−2.066 **	−2.293 **	I(1)
LTA		−2.054 **	−2.786***	−3.066 ***	−3.204 ***	I(0)
LREN		−3.837 ***	−3.706 ***	−5.730 ***	−5.900 ***	I(0)
LFOS		−2.886 ***	−2.774 ***	−4.839 ***	−5.038 ***	I(0)
LNUC		−1.956	−1.808	−3.984 ***	−5.232 ***	I(1)
(b) CADF Unit Root Test.
Variable	At Level			1st Differences
	T-Bar	Z-t-Tilde-Bar	p Value	T-Bar	Z-t-Tilde-Bar	p Value
LCO2	−2.485	−1.671	0.047
LGDP	−2.606	−1.966	0.025
LGDP2	−2.041	−0.621	0.267	−3.051	−3.023	0.001
LTA	−2.820	−1.416	0.041
LREN	−2.333	−1.320	0.093
LFOS	−1.173	1.444	0.926	−4.745	−7.053	0.000
LNUC	−2.310	−2.853	0.015

Note: ** and *** explain the level of significance at 5% and 1%, respectively.

(a&b), show the results of a unit root test used for this purpose. All variables have been determined to have a heterogeneous order of integration. In other words, they are not moving in the same direction as the rest of the economy, including CO₂, GDP, GDP², TA, REN, FOS, and NUC.

However, we have examined panel data to determine whether the variables are stationary. As shown in Table 5 (a&b), the results of the second-generation unit root tests reveal that some variables are stationary at level or I(0). In contrast, others become stationary after only first difference or I(1). We can draw the conclusion that each of the variables in the investigation is either I(0) or I(1), but not one of them is I(2). Having established the stationary nature of the variables, it is now necessary to examine the cointegration of the long-run variables.

Table 6. Westerlund Cointegration Test.

Statistic	Value	Z-Value	p-Value
Gt	−3.431	−1.842	0.010
Ga	−6.539	2.291	0.050
Pt	−4.404	2.685	0.550
Pa	−3.886	2.799	0.750

displays the results of the Westerlund [65] cointegration tests, which were used to arrive at these conclusions. p-values at a 1% level of significance suggest that the null hypothesis of Westerlund [65] can be rejected. Our long-term variables are therefore co-integrated as a result.

In the wake of checking the variables’ stationarity, the ensuing step is to affirm regardless of whether the long-run factors are co-integrated. For this reason, we have utilized Westerlund [65] cointegration tests, the after-effects of which are shown in Table 6. Table 6 delineates that the null hypothesis of Westerlund [65] is discarded inside the situation of Gt and Pt measurements based on the p-values at a 1% level. Along these lines, we can state the variables are co-integrated in the long run.

Table 7. CS-ARDL Test Results.

Variables	Coefficients	Standard Errors
LGDP	4.85 **	(7.7686)
LGDP2	−1.309 *	(1.101)
LTA	−0.221*	(2.3324)
LREN	−0.1129	(0.5905)
LFOS	0.3953 **	(0.2734)
LNUC	−0.309	(0.0624)
Adjusted Term (ECT)	−0.9114 ***	(0.1150)
Short-run Dynamics
LGDP	7.77 ***	(6.486)
LGDP2	−0.966	(4.1324)
LTA	−0.197	(3.224)
LREN	−0.035 **	(0.4440)
LFOS	1.361 **	(0.648)
LNUC	−0.119	(0.0925)
R-square	0.344

Note. Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

shows the CS-ARDL model, which consists of our long- and short-term foundation models, respectively. LGDP has a significant impact on raising emissions in the top 10 tourist arrival countries. However, the coefficient of GDP square is negative. The coefficient value of variable renewable energy shows a negative sign. That means more consumption of renewable energy has been introduced with fewer emissions in the top 10 tourist countries. A similar finding exists for the use of nuclear energy. However, tourist arrivals show no significant impact on emissions in those states. Fossil fuel shows a significant contribution to raising emissions, where the coefficient shows a positive sign with a value of 0.3953. That means a one percent increase in fossil consumption tends to increase emissions by 0.3953 percent. The error correction term (ECT) shows the desired sign, where it has a negative sign and the ECT value is −0.914%. That means the speed of movement towards the equilibrium is 0.9114%. Short-run dynamics show similar kinds of results, but the coefficient values for the selected variables show a rising trend, such as fossil consumption. Moreover, the MG, CCEMG, and AMG estimators are used in

Table 8. Robustness Tests’ Results.

Variables	AMG		CCEMG
LGDP	1.429	(13.31)	6.778	(17.14)
LGDP2	−0.091	(0.650)	−0.167	(0.750)
LTA	−0.0291 **	(0.0132)	−0.105	(0.0720)
LREN	−0.180 ***	(0.0633)	−0.118	(0.121)
LFOS	0.354	(0.519)	0.141	(0.474)
LNUC	−0.0119	(0.0299)	−0.200 **	(0.0970)
Constant	0.692	(68.09)	−24.97	(86.65)

Note. Standard errors in parentheses. *** p < 0.01, ** p < 0.05.

of the paper; they tested the robustness of the CS-ARDL model.

Table 7 summarizes the long- and short-term outcomes of CS-ARDL following confirmation of co-integration. The long- and short-term effects of GDP on CO₂ are claimed to be significant, with a positive coefficient. On the other hand, GDP² has a negative impact on environmental degradation. That means, for the top tourist countries, the EKC curve has an inverted U-shape because of the negative GDP² coefficient, which means the more GDP increases, the more environmental degradation will be reduced. However, as shown in Table 8, the augmented mean group (AMG) and common correlated mean group (CCEMG) all show robustness results. The MG, AMG, and CCEMG verify an inverted U-shaped EKC curve in the top tourist countries but the coefficients are insignificant. In both methods, tourism has negative coefficients. In addition, all models show a positive link between fossil fuel consumption and CO₂ with coefficient values of 0.354 and 0.141 percent, respectively. On the other hand, renewable energy and nuclear energy show a negative correlation with CO₂ for the CCEMG and AMG models, correspondingly. However, the summary of the findings shows that the LGDP has a significant impact on increasing emissions in the top ten tourist arrival countries. Ameyaw and Yao [76] in the United States; Awan and Azam [77] in the G-20 countries; Rahman and Majumder [78] in the N-11 countries; Piatowska et al. [79] in Spain; Zhang and Zhang [80] in China; Kizilkaya [81] in Turkey; and Ben Jabeur [82] in France all found the same coefficient sign. However, the GDP square coefficient is negative.

An expected sign of the result exists for the use of nuclear energy, where nuclear energy helps to reduce emissions. Tourist arrivals, on the other hand, show no significant impact on emissions in those states. Fossil fuels make a significant contribution to rising emissions; the coefficient displays a positive sign, meaning the increase in fossil consumption tends to raise emissions. The coefficient value of variable renewable energy is negative, indicating that the increased consumption of renewable energy has resulted in lower emissions in the top ten tourist countries. The sign of the coefficient of renewable energy is similar to that of the existing literature such as Ma et al. [83] in France and Germany, Zheng et al. [84] in China, Sharif et al. [85] in the United States, Beltrami et al. [86] in Italy, and Salazar-Nuñez et al. [87] in Mexico. However, future research that focuses on other macroeconomic determinants and builds new panels would be a significant contribution to this research field.

The results support the existence of a nexus between tourism and the environment, signaling that tourism activities in the top destination countries are compatible with environmental quality. The efficient use of natural resources and the implementation of environmental controls in major tourist destinations are most likely contributing to lower CO₂ emissions. Our findings are in line with earlier studies demonstrating that tourism expansion that adheres to sustainable principles might help to curtail CO₂ [22,33,34]. Tourism may serve to preserve natural resources, protect ecological footprints, and cease environmental damage in line with earlier findings [27]. Still, our results differ from previous studies, where an increasing number of foreign tourists have contributed to deterioration in the environment as in the case of China [42,80] or in a sample of 50 countries [45]. The differences in the results signal that not all countries have adopted environmental practices, suggesting that the tourism sector around the globe needs to accommodate sustainable policies and practices to prevent the deterioration of the environment. Our results also suggest that current sustainable practices in top destinations may be helping to lower CO₂ emissions [8,18,22,24].

Our findings contrast with studies in Spain [49], France [5], and Turkey [14] (top destinations), where tourism development caused a sizeable intensification in climate change. New studies may be needed to address country-specific factors, where additional environmental variables (i.e., carbon footprints, ecological footprints, nitrous oxide, and methane emissions), climate change variables, and specific tourism activities may help assess the progress of sustainable tourism at the country level.

5. Conclusions

Currently, tourism and environmental sustainability are important issues in the world. Tourism expansion contributes significantly to global CO₂ emissions. Economic development largely depends on tourism arrivals in countries where the development process has experienced environmental contamination. The tourism industry contributes significantly to the country’s gross domestic product, raising employment opportunities, foreign exchange earnings, economic diversification, and sustainability. However, the sector’s explosive growth and the resulting increase in international tourists have been major contributors to the increase in carbon pollution and other environmental problems. This study takes into account the top ten tourism countries that are performing better in terms of tourism industry development. Tourism-based EKC concentrates on CO₂ emissions in relation to tourism, gross domestic product, and other variables considered in this study, such as renewable energy, fossil fuel consumption, and nuclear energy. This paper used an EKC model to examine the environmental effects of tourism in the world’s top ten tourist destinations.

The primary purpose of this research was to investigate the environmental impact of tourism, fossil fuel, renewable energy, and nuclear energy consumption in the world’s top tourist destinations. The specific objectives were to estimate the long- and short-run dynamics of the selected factors, as well as to test the tourism-induced EKC for the top ten tourist countries. The study objectives were investigated using balanced panel data from 1972 to 2021. Due to the presence of cross-sectional dependence and slope heterogeneity, panel cointegration and second-generation unit root tests were proposed. A CS-ARDL model was used to assess the marginal impact of environmental variables other than tourism on CO₂ emissions. The key findings of this study state that the EKC theory is accepted in the short and long run by CSARDL but insignificant in the short run. The EKC appear in the CSARDL model in the long run, the model does show that rising income leads to lower CO₂ emission after a certain level. The use of fossil fuels can hasten all forms of environmental degradation. According to the findings of this study, CO₂ reduction can be achieved by using renewable energy, nuclear energy, and increasing the number of tourists.

The most admired tourist destinations in the world are those that, in the long run, use clean energy-related technology and promote ecotourism, which is defined as environmentally friendly tourism. Planning and budgeting for ecosystem tourism should be a top priority for those countries because of the economic contribution tourism makes and because environmental evaluation, in a given period, must improve environmental quality. The significant contribution of this study is to emphasize the econometric model of tourism, led by EKC and initiatives in the field of tourism industries and environmental sustainability assurance.