Next Article in Journal
A Study on Information Communication Technology in Ba Province, Fiji
Previous Article in Journal
Urban Resilience Framework for Evaluating Jeddah’s Capacity for Sustainability and Adaptation
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Nexus Between Tourism and Environmental Quality in Countries Most Dependent on Tourism: A RALS Approach to the Cointegration Test

by
Yenilmez Ufuk Yilmaz
*,
Hamed Rezapouraghdam
and
Hasan Kilic
Faculty of Tourism, Eastern Mediterranean University, Via TRNC Mersin 10, Gazimağusa 99628, Turkey
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(9), 3943; https://doi.org/10.3390/su17093943
Submission received: 18 February 2025 / Revised: 22 April 2025 / Accepted: 23 April 2025 / Published: 27 April 2025

Abstract

Sustainable tourism encompasses the evaluation of its present and prospective economic, social, and ecological consequences by prioritizing the demands of its natural environment and the local populations. This study examined how tourism affects critical socio-economic variables, such as life expectancy, energy intensity (EI), economic growth (EG), and population, on the environmental quality (EQ) of tourism-dependent countries. The authors employed the newly developed “residual augmented least squares (RALS) cointegration econometric method” to estimate the long-term associations between the study factors. On the other hand, the “autoregressive distributed lag (ARDL) model” was used to estimate long- and short-run estimates. The consequences revealed that, in the long run, the population, the EI, and tourism exert positive pressure on carbon emissions. However, in the short run, the EI, EG, life expectancy (LE), and population exert positive pressure to boost emissions, resulting in environmental degradation. Based on these findings, sustainable tourism management and green EG should be given priority to preserve environmental quality.

1. Introduction

As a global force, tourism can improve the ecological, social, and economic well-being of people and places if it is managed responsibly [1]. However, the unfavorable effects of this industry’s rapid growth have elevated sustainability to the top of the global political agenda. The promotion of tourism has been recognized as a significant means of attaining the United Nations’ Sustainable Development Goals, which aim to bring prosperity to tourist destinations [2]. According to the report of the “World Tourism and Travel Council” [3], the tourism industry contributes 7.6% to the global EG (GDP), which increased by 22% over 2021 and created 22 million new job opportunities in 2022. Nevertheless, if tourism is not developed sustainably, it has the potential to undermine the ongoing economic progress [4]. Conversely, it is imperative to investigate how tourism affects the ecological quality in its host communities in light of global issues such as social alienation, climate change, and overconsumption.
The prevailing argument suggests that the emissions of carbon dioxide (CO2) resulting from international travel contribute significantly to climate change [4,5,6]. The surge in international tourism is expected to amplify energy consumption, thereby establishing a credible link between the expansion of international tourism and CO2 emissions [7,8,9,10]. Furthermore, data from the “International Transport Forum and World Tourism Organization [2]” indicate that transportation-related CO2 emissions constitute approximately three-quarters of all global tourism-related emissions [11]. This scenario has emerged as a significant concern for the global economy and poses a threat to the environment [12]. Figure 1 illustrates the correlation between tourism and CO2 emissions.
The effective accessibility and affordability of electricity play pivotal roles in facilitating tourist activities and fostering infrastructural development [13]. Conversely, the substantial energy consumption associated with tourism raises concerns regarding carbon emissions and its environmental impact. Previous studies have indicated that there is a constructive connection between ecological degradation and energy usage in tourism [14] since the tourism industry increases energy consumption and creates considerable long-term effects [15]. Consequently, it is necessary to restructure the sector of international tourism to ensure ecological sustainability and the sustainable development of nations [5]. However, following a comprehensive approach to sustainability not only involves evaluating how tourism impacts the environment, but also accounting for population well-being and demographics. While the careful management of population growth can foster sustainable development in tourist destinations, research on the relationship between population growth, density, and CO2 emissions remains limited [16]. Furthermore, there exists a causal relationship between EG, carbon emissions, and life expectancy, as these factors contribute to environmental degradation, which in turn affects health outcomes and sustainable development [17].
According to this background, the goal of this study was to characterize the impacts of global tourism on the EI, EG, life expectancy, population, and EQ in the most tourism-dependent countries from 2000 to 2022. The nations included were Malta, the Maldives, the Bahamas, the Seychelles, Vanuatu, Cabo Verde, St. Lucia, Cyprus, Belize, Fiji, Cambodia, Barbados, Bahrain, Antigua, Barbuda, Dominica, Montenegro, Croatia, Jamaica, Thailand, Georgia, the Philippines, Kiribati, and Iceland. This research has significance due to the substantial impact of international tourism on the economic viability of these countries. The results of this research are anticipated to aid these countries in developing green and clean energy initiatives and formulating ecologically sustainable tourism strategies to concurrently achieve economic prosperity and environmental well-being.
This study provides a substantial addition to the current body of literature (see Appendix A). Although the influence of foreign tourism on CO2 emissions has been broadly investigated, none of the studies have included the EI as a controlled variable in their analyses, to the best of our knowledge. However, it is crucial to account for this energy factor throughout the analysis since it is considered essential in the present time to make the international tourism business more environmentally friendly and mitigate the negative environmental effects linked to the rise in international tourism [18]. The purpose of this study was to fill the existing research gap by looking into the impact of international tourism development on CO2 emissions while considering the level of EI in countries that heavily rely on tourism. In terms of methodological contribution, the authors used the recently developed “RALS unit root test and RALS cointegration” to examine the stationarity of the data and the long-term relationship between the factors of concern. Furthermore, this research contributes to the existing body of literature by using the “Autoregressive Distributive Lag (ARDL) Model” [19] as an econometric method to examine the relationship between variables in both the short and long-term, resulting in more accurate and dependable results [20,21]. This study connected tourism and the EI to the Sustainable Development Goals (SDGs) and can thus help policymakers make appropriate policies. This research paper has the following segments: an overview of related literature works, the presentation of the data and the methodology used, an explanation of the findings, and a summary with recommendations for future actions or interventions from a policy perspective regarding each section covered in this document.

The Conceptual Framework

Tourism-Sustainable Development Nexus and Prerequisites
The urge for tourism practitioners to quickly address the globalizing effects of tourism on destinations is increasing [1]. Tourism, being one of the greatest global sectors, significantly contributes to community development while also exerting substantial cultural, social, and ecological effects. The sector’s dependence on nature is intensifying the need for the sustainable use of natural resources to address the global challenges of climate change and biodiversity loss [22,23].
Tourism both influences changes in the environment and is impacted by the environment. A significant issue is tourism’s influence on land use transformations, and such modifications, coupled with global warming, result in rising surface temperatures and a decline in ecological integrity and human well-being [24]. The incorporation of sustainable development concepts in tourism is essential since this sector is one of the largest globally. The tourism sector needs to take part in the adoption of environmental and socio-cultural sustainability practices to enhance the welfare of both people and the planet [25]. Despite this recognition, detrimental environmental repercussions continue to manifest as a result of tourism operations [26]. For these reasons, scholars warn that the effects of climate change, propelled by greenhouse gas emissions, transportation, and land use alterations resulting from population growth, need immediate and urgent action, especially in prominent tourist regions where the impact on quality of life is significant [27].
Sustainable development encompasses economic, environmental, and social components, referring to growth that meets the present demands without compromising the ability of future generations to meet their own needs [28,29]. The primary focus of the ecological dimension of sustainability is to restrict human activities within the ecosystem’s carrying capacity, which includes the materials, energy, land, and water in a specific area while prioritizing the quality of human existence, which is critically linked to air quality and health. Conversely, economic sustainability emphasizes the effective use of resources to improve the operational profit and increase the market value. It also addresses the substitution of natural resources with synthetic ones, as well as reuse and recycling. Moreover, social sustainability emphasizes the social welfare of the population [30].
The attainment of sustainability relies on the crucial collaboration between the economy, the environment, and society. In the case of tourism-dependent countries, employing the industry as a source of economic growth could be linked to increased energy consumption to meet the demands of the industry and tourists, a rise in the population, and changes in environmental conditions and the life expectancy of people. For instance, scholars have reported that densely residential areas have less greenery while prioritizing efficient land use, which means elevated levels of heat stress that are associated with increased mortality and an amplified chance of acquiring numerous illnesses [31]. Moreover, it is argued that a country’s social, economic, and environmental characteristics are indicative of its citizens’ life expectancy [32]. Therefore, gauging the interactions among variables such as economic development, renewable energy resources, CO2 emissions, and life expectancy aligns with the sustainable development paradigm [33].

2. Literature Review

2.1. Tourism vs. Carbon Emissions

The impact of tourism on CO2 emissions in Brazil was studied recently by Ref. [4], the author of which found similar results. Other studies, such as Ref. [34] in China, Ref. [10] in Thailand, Ref. [9] in Kuwait, and Ref. [8] in the most visited countries, have declared that tourism imposes a positive impact on carbon emissions. Furthermore, the authors of Ref. [35] stated that the tourism development coefficient is positively associated with carbon emissions in the case of 70 nations. Additionally, research findings have indicated that the tourism industry negatively affects the EQ [36,37,38,39,40,41].
However, many other studies, such as Refs. [42,43,44], have used threshold OLS estimation, bootstrapping ARDL, the panel-corrected standard errors (PCSE) model, and the quantile autoregressive distributed lag (QARDL) model to estimate the tourism–carbon emissions link. All of these studies have reported that, in the studied destinations, tourism has a positive impact on environmental quality. Furthermore, the authors of Refs. [45,46] applied the QARDL econometric model to Malaysia and China, and they demonstrated that tourism development had a positive influence on the environment. In light of the aforementioned literature, which is inconsistent, further research is necessary to advance the literature.

2.2. EI and Carbon Emissions

Despite its importance, the EI is rarely discussed in the context of CO2 emissions, and the few available studies show a lack of agreement on the nature of the interaction. For instance, Ref. [47] demonstrated that energy efficiency has consistently led to a drop in CO2 emissions at all levels, with the greatest reduction seen at the 90th percentile in developing nations. The authors of Ref. [48] used the statistical techniques of the panel-generalized method of moments (GMM) and two-stage least squares (2SLS) to measure the impact of the EI on the mitigation of CO2 impacts via a reduction in its intensity. In a similar vein, the authors of Ref. [49] used spatial econometric regression techniques within the framework of China’s environment and observed a negative association between two variables. According to their findings, increasing the EI may help reduce carbon emissions in a local area.
On the contrary, Ref. [50] revealed that the EI enhanced the carbon emission intensity in the surrounding areas. Multiple studies, including Ref. [49] in 25 large emerging economies; Ref. [51] in 50 African countries; Ref. [52] in developing and transition economies; Ref. [53] in Morocco; Ref. [54] in China; and Ref. [55] “in higher-, upper-middle-, lower-middle-, and low-income groups” have shown that increased energy intensities lead to elevated quantities of carbon dioxide emissions. The contradictory character of the findings described in the literature necessitates further investigation into the correlation between EI and CO2 in our study.

2.3. EG and Carbon Emissions

The empirical findings have substantiated the presence of the EKC phenomenon with CO2 emissions inside China. The authors of Refs. [56,57,58,59,60] have confirmed the existence of the EKC, which posits that, in the first stages of economic development, there is a direct relationship between environmental deterioration and EG. However, once EG and the per capita income surpass a specific threshold, there is an observable trend of environmental pollution mitigation. Nevertheless, several academics have expressed skepticism over the EKC’s legitimacy. Typical cases are provided in Ref. [61] in Turkey and Ref. [62] in China. However, the authors of Refs. [12,63,64,65] stated that EG decreases the EQ, whereas EG squared increases the environmental quality. The authors of Refs. [63,66] discovered that EG decreases CO2, which boosts the ecological environment. Given the importance of EG and CO2 emissions in the sustainable tourism discourse, we included EG as an explanatory facet in our study.

2.4. Population, Life Expectancy, and Carbon Emissions

The examination of the impact of demographic variables on carbon dioxide emissions has emerged as a hot topic recently. In terms of the study scope, the primary focus of the investigation has been mostly within a single nation [17,67,68,69] or a group of countries, such as 154 countries [70]; BRICS [71]; the next 11 economies [72]; Portugal, Italy, Ireland, Greece, and Spain (PIIGS) [73]; or 46 Sub-Saharan African countries [74]. Simultaneously, the majority of research inquiries have centered on the following three themes: LE [17,67,69,71,75], population [68,72,74], and urbanization [70,73,76]. The theoretical frameworks used to analyze these three parameters provided varying results. Therefore, we used two factors, LE and population, to estimate the link with carbon emissions in the case of the most tourism-dependent countries. A summary of the aforementioned literature is presented in Appendix A.

3. Methodology

3.1. Data

The methodology employed in our study consisted of multiple steps, as illustrated in the flow chart in Figure 2. Our research investigated the association between carbon emissions, energy, tourism, EG, LE, and population in the case of 24 of the most tourism-dependent economies in the world. A basic econometric framework was developed, as shown below:
C O 2 = a 0 + a 1 T O U R i t + a 2 E N E R G Y i t + a 3 G D P i t + a 4 L I F E i t + a 5 P O P i t + i t
In Equation (1), the addition of a logarithm was used as a means of facilitating computations pertaining to sizable numerical values, hence aiding in the comparison of the magnitudes of distinct quantities.
l n C O 2 = a 0 + a 1 l n T O U R i t + a 2 l n E N E R G Y i t + a 3 l n G D P i t + a 4 l n L I F E i t + a 5 l n P O P i t + i t
In Equation (2), C O 2 depicts the carbon emissions, measured as “CO2 emissions (kt)”. Carbon emissions are seen as an indicator of the EQ among the 24 economies that heavily rely on tourism. Emissions arise because of the combustion and use of fossil fuels, including solid, liquid, and gaseous fuels, as well as gas flaring activities. The T O U R   s y m b o l i z e s the tourism as “international tourism, number of arrivals”. International inbound tourists refer to individuals who travel to a foreign country that is distinct from their habitual place of residence, but not inside their customary surroundings, for less than one year and with a purpose that does not include remunerated endeavors. The E N E R G Y reflects the symbol of the EI, as follows: “EI is the ratio between energy supply and gross domestic product measured at purchasing power parity”. The EI refers to “the measure of energy consumption required to generate a single unit of economic output”. A lower ratio signifies a reduced use of “energy in the production of a single unit of output”. The G D P is used as a symbol of the EG of the tourism economies. It is calculated as “the aggregate of the gross value contributed by all domestic producers within the economy, accounting for any applicable product taxes and deducting any subsidies that are not accounted for in the product valuation”. The L I F E refers to the LE of the residents of the tourism economies. It indicates the following: “LE indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life”. The P O P is representative of the total population of the tourism economies.
Although model specifications in Equations (1) and (2) are linear for the sake of simplicity and tractability, we do appreciate that some variables—such as tourism arrivals and energy intensity—can have an influence on economic growth (GDP), thereby potentially introducing endogeneity in the model. For instance, tourism-led growth and energy-led growth hypotheses consider tourism development and energy consumption as factors capable of influencing GDP growth importantly. This interconnectedness implies that GDP may be a mediating rather than a strictly exogenous predictor. To address this concern, we embarked on the ARDL modeling strategy, which is widely recognized as being robust to small samples and for accommodating potentially dynamically interrelated variables. Additionally, because ARDL does not impose strict exogeneity, it can accommodate both short- and long-run variable interactions, mitigating simultaneity to a certain extent.
The population is determined by the de facto definition, including all the individuals residing within a certain area, irrespective of their legal status or citizenship. The a 0 , a 1 to a 5 , i, t, and refer to the intercept, coefficients of independent facets, countries of the study (in our case, the following 24 tourism economies: “Malta, the Maldives, the Bahamas, Seychelles, Vanuatu, Cabo Verde, St. Lucia, Cyprus, Belize, Fiji, Cambodia, Barbados, Bahrain, Antigua, Barbuda, Dominica, Montenegro, Croatia, Jamaica, Thailand, Georgia, the Philippines, Kiribati, and Iceland”), time period (2000–2022 (23 years)), and white noise, respectively. The information was retrieved from the “World Bank database”. All of these countries were selected based on the share of tourism in their GDP, using World Bank data as the main data source. Specifically, we considered those countries where tourism has been responsible for more than 25% of the GDP over the past decade as “most dependent on tourism”. All of these countries have small populations (in most instances, less than one million), but their economies rely heavily on tourism, and they are the most suitable for investigating the environmental impact of tourism activity. This selection, based on thresholds, ensured that the sample was suitable for achieving the research aim of investigating the tourism–environmental nexus in tourism-dependent economies. Table 1 shows the data’s synopsis.

3.2. Econometric Structure

The current study employed a robust econometric model in examining the dynamic relationships between energy consumption, tourism, economic growth, population, life expectancy, and carbon emissions. To assist in establishing the stationarity and validity of the series data, we began by applying the augmented Dickey–Fuller (ADF) test and the residual augmented least squares ADF (RALS-ADF) test, recommended in Ref. [77]. RALS-ADF offers superior performance, particularly when the error terms do not follow a normal distribution.
To test whether there were long-run relationships among the variables, we used the Engle–Granger (EG) cointegration test [78] and the RALS-EG cointegration test [79], which is more efficient. The EG test is a two-stage test that starts with an estimation of the long-run relationship using ordinary least squares (OLS) followed by a stationarity test of the residuals. If the residuals are stationary, then the variables are cointegrated. However, since conventional cointegration tests may lose power if the errors are non-normal, we also ran the RALS-EG test for additional robustness and accuracy.
In addition, during non-normal errors, nonlinear cointegration tests have less power than their linear counterparts [80]. If non-normal errors exist, then the RALS estimator can still be used to ensure no loss in statistical strength by implementing standard least squares forecast under linearity assumptions. The Engle–Granger (EG) test was developed in Ref. [78] as a “two-step cointegration test that relies on the residual framework and conventional t-statistics”. The key advantage of the RALS method is that it can capture the higher-order moments (e.g., skewness and kurtosis) of the error distribution. The test modifies the standard cointegration equation by adding an additional term, φ ^ _t, to account for non-normality. This term includes squared and cubed residuals and enhances the precision of the test statistics. Adjusted t-statistics with modified asymptotic distributions are employed to test the null hypothesis of “no cointegration”.
Residual augmented least squares (RALS) cointegration was employed for its robustness under non-normal error distributions, which are typical in macro-panel datasets of many countries over various periods of time. In contrast to the earlier cointegration methods (e.g., Engle–Granger or Johansen tests), the RALS method allows for higher-order moments such as skewness and kurtosis and therefore yields more statistically robust results where normality assumptions are violated. This property is even more important in tourism-based research, wherein heterogeneity of data and structural shocks dominate across countries.
The autoregressive distributed lag (ARDL) model was chosen due to its capacity to handle small sample sizes and composite orders of integration (i.e., I(0) and I(1)). As compared to other models such as the vector error correction model (VECM), where all series are required to be I(1), the ARDL model allows for greater flexibility regarding model specification. It is also very effective in describing both short- and long-run dynamics in a unified framework, which is something that aligns with the objective of the study of assessing the differential temporal effects of tourism and energy consumption on the quality of the environment.
To begin the study, we first determined the order of integration of all the variables to make them compatible with the ARDL method. This was achieved by specifying a static integrating threshold whereby we utilized the employment of both the augmented Dickey–Fuller (ADF) and the residual augmented least squares ADF (RALS-ADF) unit root test. We used these tests in an effort to ascertain whether each variable was integrated of order zero [I(0)] or order one [I(1)]. Identification of this threshold was important because the ARDL model only supports variables as a mixture of I(0) and I(1) but not of I(2) or above. Hence, the term “static integrating threshold” summarizes the requirement that no variable in the model should be integrated higher than the first order—a prerequisite to the subsequent application of the cointegration and ARDL frameworks.
y t = ϵ Z t + ξ t
The “augmented Dickey–Fuller (ADF) unit root test” was performed on the acquired residuals ( ξ ^ _t) in order to determine the level of integration in the system.
ξ ^ t = ϵ 0 + ϵ 1 ξ ^ t 1 + i 1 p ϵ i + 1 Δ ξ ^ t 1 + λ t
The RALS approach demonstrates promise in ensuring more dependable, exact, and enduring results, mainly when the error term is not normally distributed. In their study, the authors of Ref. [79] employed the method as a supporting method to enhance the “EG cointegration test”. The approach to this research was new and involved two and three integration moments for residuals from ordinary cointegration tests. To enhance the execution of the RALS technique, Equation (4) was supplemented with the following term.
φ ^ t = ϖ ξ ^ t η ^ ξ ^ t ψ ^ t , t = 1 ,   2 ,   3 ,   4 ,   5 ,   T
where ξ ^ t represents the residuals derived from Equation (4) previously provided.
ϖ ξ ^ t = [ ξ ^ t 2 , ξ ^ t 3 ] ,   η ^ = 1 T i = 1 T ϖ ( ξ ^ t ) ,   ψ ^ t = 1 T i = 1 T ϖ ( ξ ^ t )
Moreover, as proposed by Ref. [81], the equation shown above corresponds to the RALS term.
φ ^ t = ξ ^ t 2 m 2 , ξ ^ t 3 m 3 3 m 2 ξ ^ t
where m = T 1 i = 1 T ξ ^ t j . In addition, RALS cointegration regression is shown in Equation (6) below by including φ ^ t in Equation (4).
Δ ξ ^ t = ϖ 1 ξ ^ t 1 + i 1 p ϖ i + 1 Δ ξ ^ t 1 + φ ^ t Ψ + Ω t
The null hypothesis was analyzed using a traditional t-test, which states that there is “no cointegration among the examined variables” if ϖ 1 = 0. In addition, in order to construct the “three different asymptotic distributions of t-statistics”, Equation (7) was applied.
t * ρ · t + 1 ρ 2 · Z
To examine the interrelationships between tourism, the EI, EG, life expectancy, the population, and carbon emissions, we proceeded to estimate the short-run and long-run dynamics using the autoregressive distributed lag (ARDL) model. This model is suitable for dealing with small sample sizes and has the ability to integrate variables to different orders (I(0) or I(1)). The general specification of the ARDL model used in this study is given in Equation (8), where the dependent variable is the log of CO2 emissions and the independent variables are tourism, energy consumption, GDP, life expectancy, and population. To achieve this, we modified Equation (2) into Equation (8), as shown below:
l n C O 2 , i , t = a 0 + a 1 l n T O U R i , t 1 + a 2 l n E N E R G Y i , t 1 + a 3 l n G D P i , t 1 + a 4 l n L I F E i , t 1 + a 5 l n P O P i , t 1 + t = 1 p a 1 l n T O U R t i + t = 1 q a 2 l n E N E R G Y t i + t = 1 q a 3 l n G D P t i + t = 1 q a 4 l n L I F E t i + t = 1 q a 5 l n P O P t i + i t
The error correction model (ECM), using the ARDL model, is presented in Equation (9). This model includes the error correction term (ECT), which indicates the speed with which the system returns to long-run equilibrium after a short-run shock. A statistically significant and negative coefficient of the ECT indicates the presence of a stable long-run relationship.
l n C O 2 , i , t = a 0 + t = 1 p a 1 l n C O 2 t i + t = 1 q a 2 l n T O U R t i + t = 1 q a 3 l n E N E R G Y t i + t = 1 q a 4 l n G D P t i + t = 1 q a 5 l n L I F E t i + t = 1 q a 6 l n P O P t i + λ E C M t 1 + t
The term marked as λ E C M t 1 represents the “rate of adjustment towards long-run equilibrium after a sudden shock within a brief timeframe”.

4. Results and Discussion

4.1. Unit Root and Cointegration Estimations

The stationarity of facets was assessed through the implementation of the RALS-ADF and ADF unit root tests. In Table 2, the ADF test’s stationarity outcome is shown. The findings indicated that all the variables except CO2 and ENERGY exhibited stationarity at this level. However, CO2 and ENERGY were found to be non-stationary when they were different at the first order. Moreover, this study used the RALS-ADF method, revealing that all the variables except the GDP demonstrated non-stationarity at the first difference and exhibited stationarity at this level. Hence, the obtained findings allowed us to perform an estimated cointegration analysis on the dataset.
Furthermore, the estimated outcome of the cointegration test is shown in Table 3. The EG estimations reflect that the critical value (−5.02) was higher than the t-statistic (−5.33) at the 1% significance level, suggesting the existence of cointegration among the facets of the study. Moreover, the outcomes of RALS-EG presented in Table 3 show that the t-statistic (−4.73) was less than the cut-off number (−4.19). Hence, the outcomes of EG were confirmed by the RALS-EG. Therefore, we declined the null hypothesis of the nonexistence of cointegration of CO2, tourism, the EI, EG, life expectancy, and the population.

4.2. ARDL Short- and Long-Term Estimations

The short- and long-term estimations of the ARDL estimators are depicted in Table 4. The coefficient associated with the “error correction term” was established to be negative and exhibited statistical significance, as shown by a p-value of 0.000. The error correction term is a measure of the rate at which the output adjusts to its long-term equilibrium after experiencing a shock in the short term.
Furthermore, the ARDL analysis indicated that the coefficient (0.019) linked to the variable TOUR had a positive and statistically significant correlation at a significance level of 1%, although this association was only seen in the long term. In light of these outcomes, a 1% increase in tourist activity in countries that strongly rely on tourism is only connected to a 0.019% decrease in the overall quality of the environment. The findings of our research are corroborated by Ref. [5] in 10 GDP countries, Ref. [4] in Brazil, Ref. [34] in China, Refs. [10,44] in Thailand, Ref. [9] in Kuwait, Ref. [8] in the case of most visited economies, Ref. [43] in 15 Mediterranean countries, Ref. [41] in the USA, and Ref. [37] in South Asia. Consequently, while tourism has positively impacted the economies of the nations under examination, it is evident that a rise in the influx of foreign tourists, particularly in nations heavily reliant on tourism, has a harmful effect on the natural environment and results in an escalation in energy consumption.
The ARDL estimation demonstrated that the coefficients (0.002 and 0.568) pertaining to the ENERGY variable exhibited a positive and statistically significant correlation at the 5% and 1% degrees of importance in the long and short run. These results suggest that a minimal increase of 1% in the EI in countries that largely depend on tourism is linked to a decrease in the EQ of around 0.002% and 0.568% in the long and short term, respectively. It is intriguing to observe that the extent of the EI’s impact on the environment is greater in the short term compared to the long term. These estimations were confirmed by Refs. [49,51,52,54,55,82] in the case of 25 large emerging economies, 50 African countries, the USA, developing and transition economies, Morocco, China, and the high-income group. Energy is an important element in fulfilling fundamental needs and attaining EG objectives. However, in cases where energy generation relies heavily on carbon-based fuels, the escalation of the EI may result in adverse environmental consequences. Hence, our findings demonstrate that, despite the focus on reducing the EI in nations heavily reliant on tourism, the advancements in energy efficiency remain inconsistent. Thus, the achievement of energy transition may be facilitated by the incorporation of key factors such as affordability, energy efficiency, energy independence, and dependability. The use of this measure is expected to mitigate the adverse environmental impacts resulting from a high EI in the most tourism-dependent countries.
Furthermore, the ARDL estimate demonstrated that the coefficient (0.568) associated with the GDP variable showed a positive and statistically remarkable relationship in the short run at an important degree of 1%. However, these coefficients were found to be negligible in the long term. The outcomes indicate that 1% growth in economic development among nations heavily reliant on tourism is associated with a short-term decline in the EQ of around 0.853. These estimations are verified by Refs. [56,57,58,60,83,84,85,86,87,88,89] in the context of India, China, the African economies, the OECD nations, France, South Asian developing economies, South European countries, middle- and low-income economies, BRICS, Vietnam, the Middle East nations, and West Asian and South American countries, respectively. The results of our study suggest that countries largely dependent on tourism experience economic development, but this prosperity comes at the cost of environmental damage. Furthermore, this finding suggests that the environmental performance of countries heavily reliant on tourism is influenced by EG.
Likewise, the ARDL estimate shows that the coefficients (0.023 and 0.216) related to the variable POP have a positive association that is statistically significant at the 5% and 1% levels of significance, both in the long run and the short run. Accordingly, an increase of one percent in the population in nations whose economies are heavily dependent on tourism may be connected to a reduction in the quality of the environment of around 0.023% and 0.216% over the short and long term. It is fascinating to note that the magnitude of the influence that the population has on the ecosystem is the greatest in the short term as opposed to the long term. The results of these estimates are consistent with Refs. [68,72,74,76] in the case of the USA, the next 11 economies, 46 Sub-Saharan African countries, the PIIGS countries, and China. Moreover, the ARDL estimate showed that, at the 5% level of significance in the short run, the coefficients (0.670) related to the variable LIFE indicate a statistically significant and favorable connection. According to the findings, a short-term decline in EQ of around 0.670% is associated with a 1% increase in LE in nations that heavily rely on tourism. Our estimations are verified by Ref. [71] in BRICS and Refs. [17,69] in the case of Turkey. The findings of this research diverge from those of Ref. [70], the authors of which examined 154 countries and argued that the rise in LE had an important effect on the reduction in carbon dioxide emissions.

5. Conclusions

In this study, we investigated the nexus of tourism, the EI, EG, population, and LE on carbon emissions in the case of most tourism-dependent countries. We employed RALS cointegration and the augmented distributed lag model (ARDL) on data spanning from 2000 to 2022 in order to estimate the cointegration link among the facets of the study and the long- and short-run coefficients.
The outcomes of the RALS estimator demonstrated the long-run association amidst the relevant factors. Moreover, the ARDL estimator showed that the ECT-1 term has a negative coefficient, affirming the existence of a long-term association between the independent variables and the dependent variable. Likewise, the ARDL econometric estimator revealed that, in the long run, the EI, tourism, and the population have substantial positive effects on carbon emissions, whereas LE and EG have insignificant coefficients with carbon emissions. Furthermore, in the short run, the ARDL estimator predicted that the EI, EG, life expectancy, and population growth have positive and substantial coefficients with carbon emissions, whereas tourism has insignificant coefficients with carbon emissions. These outcomes suggest that the boost in the EI, EG, life expectancy, and population hampers the EQ in the case of tourism-dependent countries.

5.1. Policy Recommendations

Nowadays, as destinations face a variety of social, economic, and environmental challenges, sustainable development is not only an option but a necessary strategy that governments should implement. This is critical for tourism-dependent destinations, given that tourism is recognized as one of the largest industries with inevitable environmental and societal impacts [1,23,26]. Accordingly, the first policy recommendation for such nations is to adjust their current tourism-planning processes with the goals of sustainable tourism development.
Using the findings of our study, it is essential for destination policymakers to increase the variation in the forms of incentives allocated to tourism business owners, such as hoteliers, restauranteurs, and other hospitality and tourism organizations, to reduce their reliance on polluting energy sources. The strategy of long-term loans to change power sources from fossil fuel to green energy plans and deducting the taxes from business owners who follow sustainable energy production and consumption strategies can also help these destinations reduce their energy intensity and carbon dioxide emissions.
Moreover, it is necessary for policymakers to control the migration of residents from rural places to cities. This can happen by providing adequate facilities and supporting eco-tourism and sustainable tourism development in the less-urbanized parts of the nations. Such strategies can not only create jobs but also motivate people not to move to larger and more populated cities.
The governments of the most tourism-dependent countries can implement well-defined regulations about environmental quality. For example, tourist places that have a significant adverse influence on the environment should allocate sufficient financial resources towards ecological restoration to preserve their ecological assets.
These nations need to demonstrate a rigid commitment to mitigating carbon intensity by actively pursuing enhanced energy efficiency measures. Moreover, they need to prioritize the attainment of energy efficiency strategies. The use of enhanced methods of production, together with the utilization of renewable energy sources, may be regarded as a viable solution to address the escalating energy requirements.
Additionally, the acceptance and growth of renewable energy are heavily influenced by green technological innovation, which necessitates a substantial investment. It is essential to allocate resources towards renewable technology via both governmental and non-governmental programs to augment the proportion of green energy within the overall energy consumption. Thus, the investigation of renewable energy sources must be given top priority, including wind, solar, and geothermal sources, as well as bio-diesel fuel.
Furthermore, it is simultaneously essential to implement measures aimed at progressively diminishing the use of fossil energy in the pursuit of economic objectives.
Finally, for EG, the industrialization strategies of these nations must prioritize energy conservation, as well as emission implementation reduction measures and environmentally friendly methods of manufacturing.

5.2. Limitations

This study was subject to various limitations since it was only carried out in nations reliant on tourism. Moreover, its scope was constrained since it only covered the time period from 2000 to 2022 due to the unavailability of data for the preceding years.

5.3. Future Studies

The present study exhibits the possibility of further expansion across several dimensions. The examination of the impact of CO2 emissions on the well-being of the local populations living in major tourist destinations is an intriguing subject of study. However, it is of interest to use techniques that can make predictions, such as the neural system, to assess the long-term impact that the tourism business may have on the ecological aspects of a country.

Author Contributions

Conceptualization, Y.U.Y., H.R. and H.K.; writing—original draft, Y.U.Y. and H.R.; methodology, Y.U.Y.; editing, Y.U.Y. and H.R.; supervision, H.K. All authors have read and agreed to the published version of the manuscript.

Funding

This study received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

This paper does not contain any studies with human participants performed by any of the authors.

Data Availability Statement

This research investigated the association between tourism, energy, economic growth, life expectancy, population, and carbon emissions in the case of 24 of the most tourism-dependent economies in the world. When seeking numerical data for a wide range of components and variables, it is essential to consider the World Bank Data collection. A total of 23 years were studied between 2000 and 2022, with data being collected on an annual basis throughout the period.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Sr. No.AuthorsTime PeriodCountryMethodResults Remarks
1[5]1995–201810 GDPCMMQRTOUR (+), sig → CO2
EG (+), sig → CO2
2[4]1990–2019BrazilARDLTOUR (+), sig → CO2
EG (+), sig → CO2
3[34]1997–2012ChinaEEBT and SDATOUR (+), sig → CO2
4[10]1990–2020ThailandARDLTOUR (+), sig → CO2
EG (+), sig → CO2
5[9]1995-2020KuwaitVEC TOUR (+), sig → CO2
EG (+), sig → CO2
6[8]1995–2018MVC FEPQRTOUR (+), sig → CO2
7[43]2001–201715 MCThreshold OLS TOUR (−), sig → CO2
8[44]1994–2014ThailandBootstrapping ARDLTOUR (−), sig → CO2
9[35]2000–201770 countriesGNSTOURDEV (+), sig → CO2 (DE)
TOURDEV (−), sig → CO2 (IE)
10[6]1995–2014TITBPGCTOUR (+), sig → CO2
EG (+), sig → CO2
11[42]1998–201495 countriesPCSETOUR (−), sig → CO2
EG (−), sig → CO2
EG2 (+), sig → CO2
12[41]2001–2017USAPMWCTOUR (+), sig → CO2
13[46]1970–2019MalaysiaQARDLTOURDEV (−), sig → CO2
14[37]1980–2018South AsiaNARDLTOUR (+), sig → CO2
15[39]2005–2016ChinaSEMTOUR (+, −), sig → CO2 (IUSC UDTA)
TOUR (−, +), sig → CO2 (USC-DTA)
16[36]2000–2020DDCGMM, FMOLS, and DOLS TOUR (+), sig → CO2
17[40]1995–201412 PCCGNCTOUR (+), sig → CO2
18[38]1990–2020TurkeyDOLSTOUR (+), sig → CO2
EG (+), sig → CO2
19[45]1995Q1–2017Q4ChinaQARDLTOURDEV (−), sig → CO2 (LT)
EG (+), sig → CO2 (LT)
20[47]1990–2014DCFE-PQREI (−), sig → CO2
EG(Y) (+), sig → CO2
EG2 (Y2) (−), sig → CO2
21[49]1990–201825 LEE ARDLEI (+), sig → CI
22[48]2005–2018BRICS and OECD2SLS and GMMEI (−), sig → CI
23[51]1980–201850 African countriesPCS-DLEI (+), sig → CI
EG (+), sig → CO2
24[52]1995–2017DTEMG and AMGEI (+), sig → CO2
EG (+), sig → CO2
EG2 (−), sig → CO2
POP (+), sig → CO2
25[53]1990–2020MoroccoARDLEI (+), sig →CO2
26[54]1980–2019ChinaARDLGDP (+), sig → CO2
GDP2 (−), sig → CO2
EN (+), sig → CO2
27[50]2006–2019China SEREI (−), sig → CO2
28[55]1990–2017HULG GMMEI (+), sig → CO2 (HIG)
29[83]1990–2018IndiaVECMEG (+), sig → CO2
30[84]2001–2016China Spatial econometric modelEI (+), sig → CO2
EG (+), sig → CO2
31[85]1980–2019African economiesPLS EG (+), sig → CO2
32[86]2000–201435 OECD GMM and PVAREI (+), sig → EP
EG (+), sig → EP
33[88]1987–2019FranceARDLEG (+), sig → CO2
34[57]1990–2014SADEFMOLS and DOLSEG (+), sig → CO2
EG2 (−), sig → CO2
35[90]1990–2018SECCCEMGEG (+), sig → CO2
EP (−), sig → CO2
36[60]1990–2018LMHIACMGEG (+), sig → CO2
EG (−), sig → CO2
37[89]1990–2019BRICSFE EG (+), sig → CO2
URB (+), sig → CO2
38[87]2007–2015OECDRegressionEG (+), sig → CO2
39[56]1986–2018VietnamThreshold regressionEG (+), sig → CO2
EG2 (−), sig → CO2
FE (+), sig → CO2
40[58]1990–2017WAMECUP-FM and CUP-BCEG (+), sig → EF
EG2 (−), sig → EF
POP (+), sig → EF
41[12]1971–2016USMCAAMGEG (−), sig → CO2 Canada and USA
EG (+), sig → CO2 Mexico
EG2 (+), sig → CO2 Canada and USA
EG2 (−), sig → CO2 Mexico
42[80]1995–2020SACPMG, MG, and DFEEG (+), sig → CO2
43[69]1971–2019TurkeyAARDL and DARDLLE (+), sig → EF
EG (+), sig → EF
EG2 (−), sig → EF
44[70]1992–2016154 countriesThreshold regression URB (+), sig → CO2
LE (−), sig → CO2
POP (+), sig → CO2
EG (+), sig → CO2
EG2 (−), sig → CO2
45[67]1975–2020PakistanARDL CO2 (−), sig → LE
46[71]1999–2016BRICSPARDL, PCSE, FGLS, and PMGLE (+), sig → EQ (LT)
EG (+), sig → EQ (LT)
LE (−), sig → EQ (ST)
EG (−), sig → EQ (ST)
47[17]1960–2018TurkeyWC, FTY, and BCFDSCLE (+), sig → CO2
48[68]1971–2016USAGMM and GLMPG (+), sig → CO2 and EF
49[72]1990–2017Next 11BQR POP (+), sig → AP
50[74]2000–201546 SSAGMMPOP (+), sig → CO2
51[73]1990–2019PIIGSDOLSURBPOP (+), sig → CO2
52[76]2000–2015BTHUAEntropy methodURBPOP (+), sig → EQ
URBPOP (−), sig → EQ
Note: MMQR = method of moment quantile regression; ARDL = autoregressive distributed lag model; EEBT = emissions embodied in the bilateral trade; SDA = structure decomposition analysis; TOUR = tourism; EG = economic growth; CO2 = carbon emissions; VEC = vector error correction model; FMOLS = fully modified ordinary least squares; FEPQR = fixed-effect panel quantile regression; OLS = ordinary least squares; GNS = generalized nested spatial model; BPGC = bootstrap panel Granger causality; PCSE = panel-corrected standard error model; PMWC = partial and multiple wavelet coherence; USA = United States of America; SBM-DEA = super-efficiency slacks-based measure and data envelope analyses; QARDL = quantile autoregressive distributed lag model; PARDL = panel autoregressive distributed lag model; SEM = spatial error model; GMM = generalized moment method; DOLS = dynamic ordinary least squares; GNC = Granger non-causality test; FE-PQR = fixed-effect panel quantile regression; 2SLS = two-stage least squares; PCS-DL = panel cross-sectional augmented distributed lags; MG = mean group; AMG = augmented mean group; SER = spatial econometric regression; VECM = vector error correction model; PLS = panel least squares; FE-2SLS = fixed-effect two-stage least squares; PVAR = panel vector autoregressive regression; CCEMG = common correlated effects mean group; FE = fixed-effect estimation model; CUP-FM = continuously updated fully modified; CUP-BC = continuously updated bias-corrected; DFE = dynamic fixed effect; AARDL = augmented autoregressive distributed lag model; DARDL = dynamic augmented autoregressive distributed method; PCSE = panel-correlated standard error; FGLS = feasible generalized least squares; WC = wavelet coherence; FTY = Fourier Toda–Yamamoto; BCFDSC = Breitung–Candelon frequency-domain spectral causality; GLM = generalized linear model; BQR = bootstrap quantile regression; BTHUA = Beijing–Tianjin–Hebei urban agglomeration; PIIGS = Portugal, Ireland, Italy, Greece, and Spain; SSA = Sub-Saharan African countries; BRICS = Brazil, Russia, India, China, and South Africa; SAC = South American countries; USMCA = USA–Mexico–Canada Agreement; WAME = West Asian and Middle East nations; OECD = Organization of Economic Coordination and Development; LMHIAC = low-income, middle-income, and high-income Asian countries; SEC = South European countries; SADE = South Asian developing economies; HULG = higher-, upper-middle-, lower-middle-, and low-income groups; DTE = developing and transition economies; LEE = large emerging economies; DC = developing countries; PCC = post-communist countries; DDC = developing and developed countries; TIT = tourism island territories; MC = Mediterranean countries; MVC = most visited countries; GDPC = gross domestic product countries; DE = direct effect; IE = indirect effect; ST = short term; LT-IUSC = long-term inverted U-shaped curve; LQ = lower quantile; UQ-IUSC = upper quantile inverted U-shaped curve; IUSC UDTA = inverted U-shaped in underdeveloped tourism areas; USC-DTA = U-shaped in developed tourism areas; CI = carbon intensity; HIG = high-income group; EP = environmental performance; AP = air pollution; TOURDEV = tourism development; EG2 = economic growth squared; EI = energy intensity; GDP = gross domestic product; GDP2 = gross domestic product squared; POP = population; EN = energy; EP = energy productivity; URB = urbanization; FE = fossil energy; LE = life expectancy; PG = population growth; URBPOP = urban population.

References

  1. Rezapouraghdam, H.; Alipour, H.; Arasli, H. Workplace spirituality and organization sustainability: A theoretical perspective on hospitality employees’ sustainable behavior. Environ. Dev. Sustain. 2019, 21, 1583–1601. [Google Scholar] [CrossRef]
  2. WTO. Tourism in the 2030 Agenda. 2023. Available online: https://www.unwto.org/tourism-in-2030-agenda (accessed on 21 October 2023).
  3. WTTC. Economic Impact Research. 2023. Available online: https://assets-global.website-files.com/6329bc97af73223b575983ac/647df24b7c4bf560880560f9_EIR2023-APEC.pdf (accessed on 21 October 2023).
  4. Raihan, A. Economic growth and carbon emission nexus: The function of tourism in Brazil. J. Econ. Stat. 2023, 1, 68–80. [Google Scholar] [CrossRef]
  5. Razzaq, A.; Fatima, T.; Murshed, M.M. Asymmetric effects of tourism development and green innovation on economic growth and carbon emissions in Top 10 GDP Countries. J. Environ. Plan. Manag. 2023, 66, 471–500. [Google Scholar] [CrossRef]
  6. Akadiri, S.S.; Lasisi, T.T.; Uzuner, G.; Akadiri, A.C. Examining the causal impacts of tourism, globalization, economic growth and carbon emissions in tourism island territories: Bootstrap panel Granger causality analysis. Curr. Issues Tour. 2020, 23, 470–484. [Google Scholar] [CrossRef]
  7. Skendžić, S.; Zovko, M.; Živković, I.P.; Lešić, V.; Lemić, D. The impact of climate change on agricultural insect pests. Insects 2021, 12, 440. [Google Scholar] [CrossRef]
  8. Erdoğan, S.; Gedikli, A.; Cevik, E.I.; Erdoğan, F. Eco-friendly technologies, international tourism and carbon emissions: Evidence from the most visited countries. Technol. Forecast. Soc. Change 2022, 180, 121705. [Google Scholar] [CrossRef]
  9. Khan, A.M.; Khan, U.; Naseem, S.; Faisal, S. Role of energy consumption, tourism and economic growth in carbon emission: Evidence from Kuwait. Cogent Econ. Financ. 2023, 11, 2218680. [Google Scholar] [CrossRef]
  10. Raihan, A.; Muhtasim, D.A.; Farhana, S.; Rahman, M.; Hasan, M.A.U.; Paul, A.; Faruk, O. Dynamic linkages between environmental factors and carbon emissions in Thailand. Environ. Process. 2023, 10, 5. [Google Scholar] [CrossRef]
  11. WTO; ITF. Transport-Related CO2 Emissions of the Tourism Sector–Modelling Results; UNWTO: Madrid, Spain, 2019. [Google Scholar] [CrossRef]
  12. Ongan, S.; Işık, C.; Amin, A.; Bulut, U.; Rehman, A.; Alvarado, R.; Ahmad, M.; Karakaya, S. Are economic growth and environmental pollution a dilemma? Environ. Sci. Pollut. Res. 2023, 30, 49591–49604. [Google Scholar] [CrossRef]
  13. Meșter, I.; Simuț, R.; Meșter, L.; Bâc, D. An investigation of tourism, economic growth, CO2 emissions, trade openness and energy intensity index nexus: Evidence for the European Union. Energies 2023, 16, 4308. [Google Scholar] [CrossRef]
  14. Khanal, A.; Rahman, M.M.; Khanam, R.; Velayutham, E. Are tourism and energy consumption linked? Evidence from Australia. Sustainability 2021, 13, 10800. [Google Scholar] [CrossRef]
  15. Katircioglu, S.T.; Feridun, M.; Kilinc, C. Estimating tourism-induced energy consumption and CO2 emissions: The case of Cyprus. Renew. Sustain. Energy Rev. 2014, 29, 634–640. [Google Scholar] [CrossRef]
  16. Rahman, M.M. Do population density, economic growth, energy use and exports adversely affect environmental quality in Asian populous countries? Renew. Sustain. Energy Rev. 2017, 77, 506–514. [Google Scholar] [CrossRef]
  17. Rjoub, H.; Odugbesan, J.A.; Adebayo, T.S.; Wong, W.-K. Investigating the causal relationships among carbon emissions, economic growth, and life expectancy in Turkey: Evidence from time and frequency domain causality techniques. Sustainability 2021, 13, 2924. [Google Scholar] [CrossRef]
  18. Al Fahmawee, E.A.D.; Jawabreh, O. Sustainability of green tourism by international tourists and its impact on green environmental achievement: Petra heritage, Jordan. Geo J. Tour. Geosites 2023, 46, 27–36. [Google Scholar] [CrossRef]
  19. Cho, J.S.; Greenwood-Nimmo, M.; Shin, Y. Recent developments of the autoregressive distributed lag modelling framework. J. Econ. Surv. 2023, 37, 7–32. [Google Scholar] [CrossRef]
  20. Befikadu, A.T. An Empirical Analysis of the Effects of Population Growth on Economic Growth in Ethiopia Using an Autoregressive Distributive Lag (ARDL) Model Approach. J. Knowl. Econ. 2023, 15, 8209–8230. [Google Scholar] [CrossRef]
  21. Liao, H.; Chen, Y.; Tan, R.; Chen, Y.; Wei, X.; Yang, H. Can natural resource rent, technological innovation, renewable energy, and financial development ease China’s environmental pollution burden? New evidence from the nonlinear-autoregressive distributive lag model. Resour. Policy 2023, 84, 103760. [Google Scholar] [CrossRef]
  22. García, D.H.; Rezapouraghdam, H. Climate change, heat stress and the analysis of its space-time variability in European metropolises. J. Clean. Prod. 2023, 425, 138892. [Google Scholar] [CrossRef]
  23. Hidalgo-García, D.; Founda, D.; Rezapouraghdam, H. Spatiotemporal variability of the Universal Thermal Climate Index during heat waves using the UrbClim climate model: Implications for tourism destinations. Urban Clim. 2025, 59, 102281. [Google Scholar] [CrossRef]
  24. García, D.H.; Rezapouraghdam, H.; Hall, C.M.; Karatepe, O.M.; Koupaei, S.N. Spatio-temporal variability of the earth’s surface temperature and the changes in land user/land cover: Implications for sustainable tourism development. J. Policy Res. Tour. Leis. Events 2023, 17, 557–584. [Google Scholar] [CrossRef]
  25. Rezapouraghdam, H.; Karatepe, O.M.; Enea, C. Sustainable recovery for people and the planet through spirituality-induced connectedness in the hospitality and tourism industry. J. Hosp. Tour. Insights 2023, 6, 1776–1795. [Google Scholar] [CrossRef]
  26. Alipour, H.; Rezapouraghdam, H.; Akhshik, A. Heritage redemption and the curse of tourism: The case of world’s last inhabited troglodyte village. Tour. Plan. Dev. 2021, 18, 68–85. [Google Scholar] [CrossRef]
  27. Rezapouraghdam, H.; Hidalgo-García, D. Urban Development and Climate Change: Implications for Educational Tourism Destination Planning. Water Air Soil Pollut. 2024, 235, 319. [Google Scholar] [CrossRef]
  28. Holden, E.; Linnerud, K.; Banister, D. Sustainable development: Our common future revisited. Glob. Environ. Change 2014, 26, 130–139. [Google Scholar] [CrossRef]
  29. Rezapouraghdam, H.; Alipour, H.; Akhshik, A. A futuristic approach to sustainable tourism development: Lessons from Kandovan Village. In Strategies for Promoting Sustainable Hospitality and Tourism Services; IGI Global: Hershey, PA, USA, 2020; pp. 140–157. [Google Scholar] [CrossRef]
  30. Rezapouraghdam, H.; Akhshik, A. Tracing the complexity-sustainability nexus in a small Mediterranean island: Implications for hospitality and tourism education. Worldw. Hosp. Tour. Themes 2021, 13, 476–487. [Google Scholar] [CrossRef]
  31. Puga-Bonilla, M.; Hidalgo-García, D.; Rezapouraghdam, H.; Bolivar, F.J.L. Risk of mortality and disease attributable to the heat stress index and its variability during heat waves: An observational study on the city of Madrid. Sustain. Cities Soc. 2025, 121, 106189. [Google Scholar] [CrossRef]
  32. Agbanike, T.F.; Nwani, C.; Uwazie, U.I.; Uma, K.E.; Anochiwa, L.I.; Igberi, C.O.; Enyoghasim, M.O.; Uwajumogu, N.R.; Onwuka, K.O.; Ogbonnaya, I.O. Oil, environmental pollution and life expectancy in Nigeria. Appl. Ecol. Environ. Res. 2019, 17, 11143–11162. [Google Scholar] [CrossRef]
  33. Wiredu, J.; Yang, Q.; Lu, T.; Sampene, A.K.; Wiredu, L.O. Delving into environmental pollution mitigation: Does green finance, economic development, renewable energy resource, life expectancy, and urbanization matter? Environ. Dev. Sustain. 2025, 17, 1–30. [Google Scholar] [CrossRef]
  34. Dong, S.; Xia, B.; Li, F.; Cheng, H.; Li, Z.; Li, Y.; Zhang, W.; Yang, Y.; Liu, Q.; Li, S. Spatial–temporal pattern, driving mechanism and optimization policies for embodied carbon emissions transfers in multi-regional tourism: Case study of provinces in China. J. Clean. Prod. 2023, 382, 135362. [Google Scholar] [CrossRef]
  35. Liu, Z.; Lan, J.; Chien, F.; Sadiq, M.; Nawaz, M.A. Role of tourism development in environmental degradation: A step towards emission reduction. J. Environ. Manag. 2022, 303, 114078. [Google Scholar] [CrossRef] [PubMed]
  36. Khan, Y.A.; Ahmad, M. Investigating the impact of renewable energy, international trade, tourism, and foreign direct investment on carbon emission in developing as well as developed countries. Environ. Sci. Pollut. Res. Int. 2021, 28, 31246–31255. [Google Scholar] [CrossRef] [PubMed]
  37. Chishti, M.Z.; Ullah, S.; Ozturk, I.; Usman, A. Examining the asymmetric effects of globalization and tourism on pollution emissions in South Asia. Environ. Sci. Pollut. Res. Int. 2020, 27, 27721–27737. [Google Scholar] [CrossRef]
  38. Raihan, A.; Tuspekova, A. Dynamic impacts of economic growth, renewable energy use, urbanization, industrialization, tourism, agriculture, and forests on carbon emissions in Turkey. Carbon Res. 2022, 1, 20. [Google Scholar] [CrossRef]
  39. Huang, C.; Wang, J.-W.; Wang, C.-M.; Cheng, J.-H.; Dai, J. Does tourism industry agglomeration reduce carbon emissions? Environ. Sci. Pollut. Res. 2021, 28, 30278–30293. [Google Scholar] [CrossRef] [PubMed]
  40. Isaeva, A.; Salahodjaev, R.; Khachaturov, A.; Tosheva, S. The impact of tourism and financial development on energy consumption and carbon dioxide emission: Evidence from post-communist countries. J. Knowl. Econ. 2022, 13, 773–786. [Google Scholar] [CrossRef]
  41. Mishra, S.; Sinha, A.; Sharif, A.; Suki, N.M. Dynamic linkages between tourism, transportation, growth and carbon emission in the USA: Evidence from partial and multiple wavelet coherence. Curr. Issues Tour. 2020, 23, 2733–2755. [Google Scholar] [CrossRef]
  42. Le, T.-H.; Nguyen, C.P. The impact of tourism on carbon dioxide emissions: Insights from 95 countries. Appl. Econ. 2021, 53, 235–261. [Google Scholar] [CrossRef]
  43. Yıldırım, S.; Yıldırım, D.Ç.; Aydın, K.; Erdoğan, F. Regime-dependent effect of tourism on carbon emissions in the Mediterranean countries. Environ. Sci. Pollut. Res. 2021, 28, 54766–54780. [Google Scholar] [CrossRef]
  44. Yue, X.-G.; Liao, Y.; Zheng, S.; Shao, X.; Gao, J. The role of green innovation and tourism towards carbon neutrality in Thailand: Evidence from bootstrap ADRL approach. J. Environ. Manag. 2021, 292, 112778. [Google Scholar] [CrossRef]
  45. Razzaq, A.; Sharif, A.; Ahmad, P.; Jermsittiparsert, K. Asymmetric role of tourism development and technology innovation on carbon dioxide emission reduction in the Chinese economy: Fresh insights from QARDL approach. Sustain. Dev. 2021, 29, 176–193. [Google Scholar] [CrossRef]
  46. Sun, Y.; Kamran, H.W.; Razzaq, A.; Qadri, F.S.; Suksatan, W. Dynamic and causality linkages from transportation services and tourism development to economic growth and carbon emissions: New insights from Quantile ARDL approach. Integr. Environ. Assess. Manag. 2022, 18, 1272–1287. [Google Scholar] [CrossRef] [PubMed]
  47. Akram, R.; Chen, F.; Khalid, F.; Ye, Z.; Majeed, M.T. Heterogeneous effects of energy efficiency and renewable energy on carbon emissions: Evidence from developing countries. J. Clean. Prod. 2020, 247, 119122. [Google Scholar] [CrossRef]
  48. Xiaoyang, X.; Kanaado, M.B.; Epadile, M. The impact of technological innovation, research and development, and energy intensity on carbon emissions: An experience from BRICS and OECD countries. Int. J. Sustain. Dev. World Policy 2022, 11, 1–17. [Google Scholar] [CrossRef]
  49. Rahman, M.M.; Sultana, N.; Velayutham, E. Renewable energy, energy intensity and carbon reduction: Experience of large emerging economies. Renew. Energy 2022, 184, 252–265. [Google Scholar] [CrossRef]
  50. Pang, G.; Ding, Z.; Shen, X. Spillover effect of energy intensity reduction targets on carbon emissions in China. Front. Environ. Sci. 2023, 11, 1054272. [Google Scholar] [CrossRef]
  51. Namahoro, J.P.; Wu, Q.; Zhou, N.; Xue, S. Impact of energy intensity, renewable energy, and economic growth on CO2 emissions: Evidence from Africa across regions and income levels. Renew. Sustain. Energy Rev. 2021, 147, 111233. [Google Scholar] [CrossRef]
  52. Nwani, C.; Bekun, F.V.; Gyamfi, B.A.; Effiong, E.L.; Alola, A.A. Toward sustainable use of natural resources: Nexus between resource rents, affluence, energy intensity and carbon emissions in developing and transition economies. In Natural Resources Forum; Blackwell Publishing Ltd.: Oxford, UK, 2023; pp. 155–176. [Google Scholar] [CrossRef]
  53. Zhang, X.; Shi, X.; Khan, Y.; Khan, M.; Naz, S.; Hassan, T.; Wu, C.; Rahman, T. The impact of energy intensity, energy productivity and natural resource rents on carbon emissions in Morocco. Sustainability 2023, 15, 6720. [Google Scholar] [CrossRef]
  54. Yang, Z.; Cai, J.; Lu, Y.; Zhang, B. The impact of economic growth, industrial transition, and energy intensity on carbon dioxide emissions in China. Sustainability 2022, 14, 4884. [Google Scholar] [CrossRef]
  55. Naqvi, S.A.A.; Shah, S.A.R.; Abbas, N. Nexus between urbanization, emission, openness, and energy intensity: Panel study across income groups. Environ. Sci. Pollut. Res. 2020, 27, 24253–24271. [Google Scholar] [CrossRef] [PubMed]
  56. Vo, D.H.; Ho, C.M. Foreign investment, economic growth, and environmental degradation since the 1986 “Economic Renovation” in Vietnam. Environ. Sci. Pollut. Res. 2021, 28, 29795–29805. [Google Scholar] [CrossRef]
  57. Ali, W.; Gohar, R.; Chang, B.H.; Wong, W.K. Revisiting the impacts of globalization, renewable energy consumption, and economic growth on environmental quality in South Asia. Adv. Decis. Sci. 2022, 26, 75–98. [Google Scholar] [CrossRef]
  58. Kihombo, S.; Vaseer, A.I.; Ahmed, Z.; Chen, S.; Kirikkaleli, D.; Adebayo, T.S. Is there a tradeoff between financial globalization, economic growth, and environmental sustainability? An advanced panel analysis. Environ. Sci. Pollut. Res. 2022, 29, 3983–3993. [Google Scholar] [CrossRef] [PubMed]
  59. Joof, F.; Samour, A.; Ali, M.; Tursoy, T.; Haseeb, M.; Hossain, M.E.; Kamal, M. Symmetric and asymmetric effects of gold, and oil price on environment: The role of clean energy in China. Resour. Policy 2023, 81, 103443. [Google Scholar] [CrossRef]
  60. Luo, J.; Ali, S.A.; Aziz, B.; Aljarba, A.; Akeel, H.; Hanif, I. Impact of natural resource rents and economic growth on environmental degradation in the context of COP-26: Evidence from low-income, middle-income, and high-income Asian countries. Resour. Policy 2023, 80, 103269. [Google Scholar] [CrossRef]
  61. Akbostancı, E.; Türüt-Aşık, S.; Tunç, G.İ. The relationship between income and environment in Turkey: Is there an environmental Kuznets curve? Energy Policy 2009, 37, 861–867. [Google Scholar] [CrossRef]
  62. Wang, Z.; Ye, X. Re-examining environmental Kuznets curve for China’s city-level carbon dioxide (CO2) emissions. Spat. Stat. 2017, 21, 377–389. [Google Scholar] [CrossRef]
  63. Ali, M.; Seraj, M.; Turuc, F.; Tursoy, T.; Uktamov, K.F. Green finance investment and climate change mitigation in OECD-15 European countries: RALS and QARDL evidence. Environ. Dev. Sustain. 2024, 26, 27409–27429. [Google Scholar] [CrossRef]
  64. Samour, A.; Jahanger, A.; Ali, M.; Joof, F.; Tursoy, T. Renewable energy, natural resources, technological innovation, and consumption-based carbon emissions in China: Tracking environmental neutrality. In Natural Resources Forum; Blackwell Publishing Ltd.: Oxford, UK, 2023. [Google Scholar] [CrossRef]
  65. Joof, F.; Samour, A.; Tursoy, T.; Ali, M. Climate change, insurance market, renewable energy, and biodiversity: Double-materiality concept from BRICS countries. Environ. Sci. Pollut. Res. 2023, 30, 28676–28689. [Google Scholar] [CrossRef]
  66. Samour, A.; Joof, F.; Ali, M.; Tursoy, T. Do financial development and renewable energy shocks matter for environmental quality: Evidence from top 10 emitting emissions countries. Environ. Sci. Pollut. Res. 2023, 30, 78879–78890. [Google Scholar] [CrossRef]
  67. Azam, M.; Uddin, I.; Saqib, N. The determinants of life expectancy and environmental degradation in Pakistan: Evidence from ARDL bounds test approach. Environ. Sci. Pollut. Res. 2023, 30, 2233–2246. [Google Scholar] [CrossRef] [PubMed]
  68. Khan, I.; Hou, F.; Le, H.P. The impact of natural resources, energy consumption, and population growth on environmental quality: Fresh evidence from the United States of America. Sci. Total Environ. 2021, 754, 142222. [Google Scholar] [CrossRef]
  69. Pata, U.K.; Yurtkuran, S.; Ahmed, Z.; Kartal, M.T. Do life expectancy and hydropower consumption affect ecological footprint? Evidence from novel augmented and dynamic ARDL approaches. Heliyon 2023, 9, e19567. [Google Scholar] [CrossRef] [PubMed]
  70. Wang, Q.; Li, L. The effects of population aging, life expectancy, unemployment rate, population density, per capita GDP, urbanization on per capita carbon emissions. Sustain. Prod. Consum. 2021, 28, 760–774. [Google Scholar] [CrossRef]
  71. Mahalik, M.K.; Padhan, H.; Patel, G.; Mishra, S.; Chyrmang, R. The role of gender life expectancy in environmental degradation: New insights for the BRICS economies. Environ. Dev. Sustain. 2023, 26, 9305–9334. [Google Scholar] [CrossRef]
  72. Sinha, A.; Sengupta, T.; Alvarado, R. Interplay between technological innovation and environmental quality: Formulating the SDG policies for next 11 economies. J. Clean. Prod. 2020, 242, 118549. [Google Scholar] [CrossRef]
  73. Balsalobre-Lorente, D.; Ibáñez-Luzón, L.; Usman, M.; Shahbaz, M. The environmental Kuznets curve, based on the economic complexity, and the pollution haven hypothesis in PIIGS countries. Renew. Energy 2022, 185, 1441–1455. [Google Scholar] [CrossRef]
  74. Acheampong, A.O. Modelling for insight: Does financial development improve environmental quality? Energy Econ. 2019, 83, 156–179. [Google Scholar] [CrossRef]
  75. Ali, M.; Seraj, M. Nexus between energy consumption and carbon dioxide emission: Evidence from 10 highest fossil fuel and 10 highest renewable energy-using economies. Environ. Sci. Pollut. Res. 2022, 29, 87901–87922. [Google Scholar] [CrossRef]
  76. Liang, L.; Wang, Z.; Li, J. The effect of urbanization on environmental pollution in rapidly developing urban agglomerations. J. Clean. Prod. 2019, 237, 117649. [Google Scholar] [CrossRef]
  77. Im, K.S.; Lee, J.; Tieslau, M.A. More Powerful Unit Root Tests with Non-Normal Errors; Springer: New York, NY, USA, 2014. [Google Scholar] [CrossRef]
  78. Engle, R.F.; Granger, C.W. Co-integration and error correction: Representation, estimation, and testing. Econom. J. Econom. Soc. 1987, 251–276. [Google Scholar] [CrossRef]
  79. Lee, H.; Lee, J.; Im, K. More powerful cointegration tests with non-normal errors. Stud. Nonlinear Dyn. Econom. 2015, 19, 397–413. [Google Scholar] [CrossRef]
  80. Ali, M.; Joof, F.; Samour, A.; Tursoy, T.; Balsalobre-Lorente, D.; Radulescu, M. Testing the impacts of renewable energy, natural resources rent, and technological innovation on the ecological footprint in the USA: Evidence from Bootstrapping ARDL. Resour. Policy 2023, 86, 104139. [Google Scholar] [CrossRef]
  81. Meng, M.; Strazicich, M.C.; Lee, J. Hysteresis in unemployment? Evidence from linear and nonlinear unit root tests and tests with non-normal errors. Empir. Econ. 2017, 53, 1399–1414. [Google Scholar] [CrossRef]
  82. Zhang, D.; Wang, Q.; Yang, Y. Cure-all or curse? A meta-regression on the effect of tourism development on poverty alleviation. Tour. Manag. 2023, 94, 104650. [Google Scholar] [CrossRef]
  83. Hu, K.; Raghutla, C.; Chittedi, K.R.; Zhang, R.; Koondhar, M.A. The effect of energy resources on economic growth and carbon emissions: A way forward to carbon neutrality in an emerging economy. J. Environ. Manag. 2021, 298, 113448. [Google Scholar] [CrossRef]
  84. Li, J.; Li, S. Energy investment, economic growth and carbon emissions in China—Empirical analysis based on spatial Durbin model. Energy Policy 2020, 140, 111425. [Google Scholar] [CrossRef]
  85. Osadume, R.; University, E.O. Impact of economic growth on carbon emissions in selected West African countries, 1980–2019. J. Money Bus. 2021, 1, 8–23. [Google Scholar] [CrossRef]
  86. Ozcan, B.; Tzeremes, P.G.; Tzeremes, N.G. Energy consumption, economic growth and environmental degradation in OECD countries. Econ. Model. 2020, 84, 203–213. [Google Scholar] [CrossRef]
  87. Schröder, E.; Storm, S. Economic growth and carbon emissions: The road to “hothouse earth” is paved with good intentions. Int. J. Political Econ. 2020, 49, 153–173. [Google Scholar] [CrossRef]
  88. Xue, C.; Shahbaz, M.; Ahmed, Z.; Ahmad, M.; Sinha, A. Clean energy consumption, economic growth, and environmental sustainability: What is the role of economic policy uncertainty? Renew. Energy 2022, 184, 899–907. [Google Scholar] [CrossRef]
  89. Zhang, H. Technology innovation, economic growth and carbon emissions in the context of carbon neutrality: Evidence from BRICS. Sustainability 2021, 13, 11138. [Google Scholar] [CrossRef]
  90. Liu, M.; Chen, Z.; Sowah Jr, J.K.; Ahmed, Z.; Kirikkaleli, D. The dynamic impact of energy productivity and economic growth on environmental sustainability in South European countries. Gondwana Res. 2023, 115, 116–127. [Google Scholar] [CrossRef]
Figure 1. Bubble plot of the CO2 emissions in the most tourism-dependent countries.
Figure 1. Bubble plot of the CO2 emissions in the most tourism-dependent countries.
Sustainability 17 03943 g001
Figure 2. Flow chart depicting the stages used to obtain the study outcomes.
Figure 2. Flow chart depicting the stages used to obtain the study outcomes.
Sustainability 17 03943 g002
Table 1. Data synopsis.
Table 1. Data synopsis.
Symbol Variable NameUnit of Calculation (Measurement)Source
CO2Carbon emissions“CO2 emissions (kt)”.WB
TOURTourism“International tourism, number of arrivals”.WB
ENERGYEnergy intensity“Ratio between energy supply and gross domestic product measured at purchasing power parity”. WB
GDPEconomic growth“GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products”. WB
LIFELife expectancy“Life expectancy indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life”.WB
POPPopulation“Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship”. WB
Source: authors compilation, note: WB = World Bank, https://databank.worldbank.org/source/world-development-indicators. accessed on 2 October 2023.
Table 2. Unit root test results.
Table 2. Unit root test results.
VariablesADFRALS-ADF ρ 2
CO2−2.49−1.870.51
TOUR−4.12 ***−2.990.74
ENERGY−2.33−2.150.63
GDP−3.85 ***−7.39 ***0.91
LIFE−3.98 ***−2.830.63
POP−3.63 ***−1.190.55
∆CO2−23.04 ***−54.99 **0.81
∆TOUR−12.14 ***−26.87 ***0.91
∆ENERGY−24.21 ***−38.55 ***0.84
∆LIFE−26.62 **−42.55 ***0.90
∆POP−23.39 ***−63.85 ***0.86
Note: *** and ** indicate significance at 1%, 5%, and 10%, respectively. The 1%, 5%, and 10% critical values for the ADF were −3.58, −2.93, and −2.60, respectively. The 1%, 5%, and 10% critical values for the RALS-ADF were −3.75, −3.30, and −3.05, respectively.
Table 3. RALS cointegration test results.
Table 3. RALS cointegration test results.
MethodsKTest Statistics ρ 2
EG0−5.33 ***
RALS-EG0−4.73 **0.81
Note: *** and ** indicate significance at 1%, 5%, and 10%, respectively; K shows the optimal lag length found using recursive statistics; the 1%, 5%, and 10% critical values for the EG test were −5.02, −4.32, and −3.98, respectively; and the 1%, 5%, and 10% critical values for the RALS-EG test were −4.80, −4.19, and −3.88, respectively.
Table 4. ARDL results (dependent variable: LOG (CO2)).
Table 4. ARDL results (dependent variable: LOG (CO2)).
VariableCoefficientStd. Errort-StatisticProb.
Long-Run Coefficients
LOG(TOUR)0.0190.0063.0280.002 ***
LOG(ENERGY)0.0040.0022.010.041 **
LOG(GDP)0.0070.0150.4650.642
LOG(LIFE)0.0700.1580.4410.659
LOG(POP)0.0230.0102.2470.025 **
Short-Run Coefficients
ECM (−1)−0.0430.010−4.2620.000 ***
DLOG(ENERGY)0.5680.04113.5700.000 ***
DLOG(GDP)0.8530.02434.3050.000 ***
DLOG(LIFE)0.6700.3152.120.033 **
DLOG(POP)0.2160.0258.5700.000 ***
Note: *** and ** indicate significance at 1%, 5%, and 10%, respectively.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yilmaz, Y.U.; Rezapouraghdam, H.; Kilic, H. The Nexus Between Tourism and Environmental Quality in Countries Most Dependent on Tourism: A RALS Approach to the Cointegration Test. Sustainability 2025, 17, 3943. https://doi.org/10.3390/su17093943

AMA Style

Yilmaz YU, Rezapouraghdam H, Kilic H. The Nexus Between Tourism and Environmental Quality in Countries Most Dependent on Tourism: A RALS Approach to the Cointegration Test. Sustainability. 2025; 17(9):3943. https://doi.org/10.3390/su17093943

Chicago/Turabian Style

Yilmaz, Yenilmez Ufuk, Hamed Rezapouraghdam, and Hasan Kilic. 2025. "The Nexus Between Tourism and Environmental Quality in Countries Most Dependent on Tourism: A RALS Approach to the Cointegration Test" Sustainability 17, no. 9: 3943. https://doi.org/10.3390/su17093943

APA Style

Yilmaz, Y. U., Rezapouraghdam, H., & Kilic, H. (2025). The Nexus Between Tourism and Environmental Quality in Countries Most Dependent on Tourism: A RALS Approach to the Cointegration Test. Sustainability, 17(9), 3943. https://doi.org/10.3390/su17093943

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop