The Nexus between tourism-energy-environmental degradation: Does financial development matter in GCC countries?

Tourism and CO₂ emissions

Tourism plays a significant role in the entire world’s economic operations. Its major contribution to the worldwide economy is undeniable. It brings job opportunities, and a dynamic culture, and encourages exports and environmental changes (Işık et al., 2020a, 2020b; Karagöz et al., 2021; Dogru et al., 2023; Jabeen et al., 2023; Karagöz et al., 2023). Countries having scenic sights are considered more viable countries for tourism. Moreover, developing countries and those countries that are unable to utilize their other source due to lack of finance, prefer to nourish and promote their tourism sector. An influx of tourists enhances the demand that also boosts the business operations in the host country (Collins-Kreiner, 2019). An increase in business operations will also increase the utilization of conventional sources of energy (non-renewable). This leads to a polluted society and due to this, the routine of everything will gradually detract. In brief, tourism draws its impact on social, political, economic, and environment. However, tourism strengthens the economy of the host country by introducing innovative opportunities, but it also causes threats to the host country by deteriorating the environment. Different scholars have conceded the potential and significant impact of tourism on the dilapidation of the environment in the shape of CO₂ emissions. The work of Paramati et al. (2017) described the argument by supporting the environmental Kuznets curve (EKC) that the catastrophic effect of CO₂ emissions declines more swiftly in developed economies than in developing economies. They further noted that the EKC suggests that the abysmal effect of CO₂ diminishes as national revenue increases. Another study done by Kuo et al. (2012) asserted that increasing tourism enhances the level of CO₂ emissions in China to some extent. An influx of tourism expedites the degree of CO₂ secretions in Malaysia. Tourism affects CO₂ secretions negatively in the case of Singapore in long-oriented and short-oriented periods (Katircioğlu, 2014). International tourism works spur the level of carbon dioxide in the case of Cyprus (Katircioglu et al., 2014). A work done by Nepal et al. (2019) affirmed the positive link between tourism and the secretion of CO₂. Above-mentioned works provide positive and negative liaisons between tourism and the emissions of CO₂ secretions.

The study by Nepal et al. (2019) suggested that a sustainable tourism manifesto upsurges the economies of host countries. Numerous studies have examined the impact of tourism in different aspects. A study by Andrades and Dimanche (2017) identified some issues related to Russia that a strengthened economy in natural resources, businesses, and cultural resources has the potential to develop tourism. Park et al. (2019) noted that tourism authenticity has a key contribution to sustaining tourism-related operations. Another work by Zenker and Kock (2020) described that tourism has a major chunk in the global economy. Ellis et al. (2018) conducted research work on food tourism and their findings suggested that food is an essential factor to promote tourism. People prefer tourism due to conventional food or cultural food. They further noted that authenticity assures the promotion of food tourism. There is a positive link between authenticity, food, and tourism. The study of Rasoolimanesh et al. (2017) found that motivation positively affects the poor level of participation in the community and opportunity has a great impact on a significant degree of social contribution. Lin et al. (2017) asserted that the development of tourism gives economic and social-cultural advantages and it positively affects life satisfaction. Such studies observed tourism from a different perspective. Based on inconclusive findings of literature, the non-directional hypothesis can be argued,

H1: Tourism activities have significant relationship with CO₂ emissions.

Energy alternatives and CO₂ emissions

Utilization of energy is mandatory to proceed with business activities, but the matter of concern is the nature of energy. In this competitive age, economies are moving swiftly and achieving their targets by doing their best. However, the entire world is concerned about the soaring temperature and different countries have molded themselves toward renewable energy sources. However, developing economies, consider non-renewable energy sources as primary sources of energy. Different research conducted to question the liaison between the utilization of energy and CO₂ secretions. The study by Rahman (2020) noted the detrimental impact of electricity utilization on the environment (CO₂ emissions) by using FMOLS for the panel of G-7 economies. Another study by Jalil and Feridun (2011) described that the utilization of energy upsurges the level of CO₂ secretions and they employed the ARDL approach. The work of Hossain (2011) described that the use of energy maximizes the ratio of CO₂ emissions, and they employed Vector Error Correction Mechanism and GMM approach. Jayanthakumaran et al. (2012) and Ozturk and Acaravci (2013) explored the connection between energy utilization and CO₂ secretion and their results suggested that the increment in the utilization of energy boosts the emissions of CO₂ in the short run and long run. They employed the Autoregressive Distributed and Error Correction approach. Another study by Dong et al. (2017) employed the Granger causality and Augmented Mean Group estimator to find the effect of natural gas and renewable energy utilization on the emissions of CO₂ in BRICS economies from 1985 to 2016. They found a negative effect of energy utilization on CO₂ secretions. The work of Bélaïd and Youssef (2017) found a positive link between non-renewable energy and CO₂ emissions. They further noted an inverse liaison between the utilization of renewable energy and the secretions of CO₂. Chen et al. (2018) explored the positive link between coal and CO₂ secretions.

H2: Renewable energy has a negative while non-renewable energy has a positive significant relationship with CO2 emissions.

Moderating role of financial sector development

The literature on the relevant role of the financial sector in determining environmental quality is inconclusive. Some studies mention the positive role of the financial sector in handling the environment while other studies enlisted its deteriorating impact on environmental quality. For instance, Zafar, et al., (2019) probes that the developed financial sector helps in managing the increasing environmental issues in G-7 economies while it deteriorates the environmental quality in N-11 economies. They found a direct relationship between the banking development index and CO2 emissions in N-11 economies while an inverse relationship in the case of G-7 economies. Similarly, Bui (2020) examined the direct and indirect impact of financial sector development on CO2 emissions by arranging the empirical analysis on a global sample over the period 1990–2012. The empirical analysis highlighted the transmission channel through which FD deteriorates the environmental quality. The developed financial sector augments the consumption of energy and other economic activities that further boost the emissions of CO₂ and thus hamper the environmental quality. Recently, Qalati, et al., (2023) investigated the empirical nexus between energy consumption, trade, FD, and environmental damages. They found that the environmental damages were more apparent in the countries that have developed the financial sector. Most studies explore the individual impact of tourism activities and energy consumption in determining environmental quality (Jalil and Feridun, 2011; Selvanathan, et al., 2021; Zhang and Zhang, 2021). However, how financial sector development moderates this relationship is not well known in the literature. This study fulfills this gap by extending the literature on the moderating role of financial sector development.

Control variables and CO₂ emissions

Advancement in various sectors brings development in the financial sector and similarly, development in the financial sector makes other sectors develop. This advancement urges financial institutions to give a preamble of new techniques of financing approaches on easy terms and conditions. The availability of funds motivates recipients to consider innovative commercial and non-commercial activities and such activities may uplift the level of CO₂ emissions. Economies are concerned about environmental degradation, and they impose terms and conditions on pollution-intensive firms. Scholars and researchers conducted work to find the connection between financial sector development and CO₂ emissions Ahmad et al., 2022; Ahmad et al., 2021a, Ahmad et al., 2021b; Çetin et al., 2023. Authors noted that bringing up more development would invite more CO₂ emissions. The study by Jensen (1996) asserted that financial deepening enhances industrial growth, and it welcomes the emissions of CO₂. Another study by Zhang (2011) disclosed that a swift positive movement in FD bestows amenities of funds on a lax basis, and due to this, people consume more and industries consume energy to fulfill massive demand that will increase the emissions of CO₂. An improvement in FD leads to an influx of foreign direct investment (FDI) and hence spiraling the emissions of CO₂ (Sadorsky, 2011). The development in the financial sector affects CO₂ emissions positively and negatively.

An improvement in tourism activities will increase CO₂ secretions. Tourism affects the emissions of lethal gases positively and inversely but in most cases. Tourism affects positively which means that tourism promotion hikes in demand and hence the industries enhance operations to cover the demand of consumers in this way, they will increase the emissions of CO₂ (Kuo et al., 2012; Paramati et al., 2017). Similarly, the utilization of energy is a major cause of increasing CO₂ emissions (Rahman, 2020). Developed economies are trying their best to convert themselves fully to renewable energy and for this purpose; they have succeeded to some extent. Still, there are some deficiencies to achieve the goal of renewable energy. However, developing economies are mostly dependent on non-renewable energy, and they are extending their utilization of non-renewable energy day by day for sustainable growth. This increasing CO₂ secretion is responsible for the dilapidation of the atmosphere (Dong et al., 2017). Our survival must mitigate the secretion of CO₂, but different research proved that there is a positive and significant link between the utilization of fuels and carbon dioxide (Farooq, 2022). The catastrophic effect of such lethal gas may hamper life. But the development in financial sectors urges financial institutions to grant funds to start commercial and non-commercial activists. Given that, they make the best utilization of funds under strict regulations by the environmental regulatory authorities. Moreover, they go towards substituting non-renewable sources of energy by promoting renewable energy sources. The developed financial sector converts positive relationships between energy utilization and CO₂ secretions into negative relationships between energy usage and CO₂ emissions (Jensen, 1996). Similarly, the positive link between tourism and CO₂ emissions will convert into a negative due to the developed financial sector. The provision of hefty funds boosts the confidence of the managers, and they will promote and introduce green technological innovation to lessen CO₂ emissions. They will bring eco-friendly technology to appease the scorching temperature of this hot world (Zhang, 2011).

Economic growth creates a chain of development in almost every sector of an economy. Economic growth affects society, politics, economics, and the environment. It has both a positive and negative impact on the atmosphere. It welcomes employment opportunities and appeases the inflation rate. In short, we can say that economic growth is the backbone of the whole economy and individually every sector of an economy. Economic growth strengthens the financial matters of individuals and collectively, but it throws a negative impact and nowadays economies are panicking about increasing CO₂ emissions (Rahman, 2020). In short, economic growth is possible due to development in the industrial sector. Industries work in a befitting manner to fulfill the demand and for this purpose, they exaggerate the production process. An enlargement in the production process will emit massive CO₂. Pollution-oriented industry firms face severe terms and conditions regarding emissions of CO₂. The study of Liobikienė and Butkus (2019) delivered that economic expansion and urbanization mitigate the level of CO₂ secretions only via the utilization of energy efficiently. For this cause, the economies should pay clear attention to technological advancement as they want viable economic growth. He further noted that without technological advancement, economic growth will increase the emissions of CO₂. Another work by Talbi (2017) described that energy efficiency has a major contribution to waning the emissions of lethal gases. Similarly, the work of Liobikienė and Butkus (2019) affirmed the contribution of GDP and urbanization in mitigating the emissions of CO₂. Chen et al. (2018) depicted that growing GDP, utilization of energy, and urbanization are the chief factors affecting emissions of lethal gases (CO_2s) in many developing and developed economies. The study of Wang et al. (2016) noted that urbanization hits CO₂ emissions positively. There exists a U-shaped relation between economic growth and CO₂ emissions.

Other different studies have been done on similar topics. Energy consumption is considered as a backbone to proceed with trade operations. A prosperous economy is associated with boosting its trade activities. From a very grassroots level, the consumption of the masses is increasing nowadays, and businesses are very passionate and eager to fulfill the demand of the masses. They continue trade activities by utilizing the natural resources of a country. The depletion of natural resources for any purpose makes an environmental threat to a country. Different scholars have done work to find the link between trade and CO₂ emissions. Jun et al. (2020) indicated that trade activities increase pollution in China. They further noticed that the “pollution haven hypothesis” exists in China which means that trade openness will make more information and will mitigate the information asymmetric impact. Le et al. (2016) increasing trading activities in high-income economies mitigate CO₂ emissions but trade operations in lower- and middle-income economies will maximize the level of CO₂ emissions. Similarly, the study of Baek et al. (2009) asserted that trade operations improve the quality of the environment in developed economies but deteriorate in developing and underdeveloped economies.

Theoretical framework

This section provides the relationship of variables in a frame. Tourism and energy alternatives are the main explanatory variables on the left-hand side. Environmental pollution is the main dependent variable on the right-hand side. FD is a moderating variable, and it moderates tourism and energy alternatives with environmental pollution as shown in Figure 1.

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Figure 1.

Theoretical framework.

Variables explanation

In the current analysis, CO₂ emission is a dependent variable, depending on a list of other variables. To measure the CO₂ emissions, we follow the measurement specified by the WDI, The World Bank which is a per capita metric ton emission of CO₂. This ratio further exemplifies the intensity of environmental degradation in a specific country. The emissions of CO₂ stem from major economic activities including the burning of fossil fuels, production of industrial goods, consumption of industrial products, etc., it further includes the emissions from the combustion of gas fuels, liquid gas, gas flaring, and manufacturing activities. This acceleration of CO₂ has been repeatedly considered a proxy of ecological (environmental) quality by a plethora of studies (Chen et al., 2018; Jun et al., 2020; Farooq, 2022). Tourism and energy alternatives are the main explanatory variables while FD serves as a moderating variable. The discussion on tourism has been distributed into two proxies, that is, the number of tourists arrivals, and tourism receipts. Tourism arrivals are the number of tourists who come into the country for a period not exceeding 12 months and the main purpose of visits is recreation and visits to historical places, etc.; similarly, the tourism receipts show the volume of expenditures made by international tourists while traveling to the host’s country. It also includes the expenditures made during the visits such as residential, food, and domestic traveling expenditures. In sum, tourism receipts show the percentage of funds earned by the host country due to visits of tourists (Mishra et al., 2022). In addition to WDI, many other studies have also specified a similar measurement of both tourism arrivals and tourism receipts (Le and Nguyen, 2021; Isaeva et al., 2022; Chen et al., 2022).

Regarding the energy alternatives, we use fossil fuel energy consumption (FFE) and renewable energy consumption (REC) as proxy variables. The FFE comprises the production of energy from the combustion of fossil fuels including natural gas products, oil, coal, and petroleum. Similarly, the REC demonstrates the percentage production of energy from renewable sources, for example, sunlight, wind power, and hydroelectric. Next, the domestic credit to private sector ratio depicts the status of FD. This ratio further refers to the volume of funds provided by financial institutions to the private sector of an economy. The financial institutions mainly include banks, microfinance institutions, leasing companies, etc., The study of Isaeva et al. (2022) has utilized similar measurements of energy alternatives and FD. In addition to the main variables, the current analysis includes foreign investment, economic growth, and GCF as control variables. Foreign investment is a direct investment by non-resident individuals into capital ventures of the host country. It also includes the acquisition of existing business ventures. Similarly, the economic growth was enumerated by GDP growth rate while GCF is an outlay of gross investment into the fixed assets by the federal government plus net changes in the volume of inventories. Table 1 provides a summary of all variables of the study.

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Table 1.

Variables of study.

Acronym	Variable	Measurement	Reference
CO2	Pollution emissions	CO2 emissions (metric tons per capita)	(Koçak et al., 2020; Grosbois and Fennell, 2022)
ITA	International tourism arrivals	International tourism, number of arrivals	Isaeva et al. (2022)
ITR	International tourism receipts	International tourism, receipts (% of total exports)	Le and Nguyen (2021)
FFE	Fossil fuel energy consumption	Fossil fuel energy consumption (% of total)	Sarwar (2022)
REC	Renewable energy consumption	Renewable energy consumption (% of total final energy consumption)	Zhang and Zhang (2021)
FD	Financial development	Domestic credit to the private sector (% of GDP)	Jianguo et al. (2022)
FDI	Foreign investment	Foreign direct investment, net inflows (% of GDP)	Farooq (2022)
GDP	Economic growth	GDP growth (annual %)	Hossain (2011)
GCF	Gross capital formation	Gross capital formation (% of GDP)	Mujtaba et al. (2022)

Econometric models and methodology discussion

The relationship among the variables can be presented in the form of the following equations:

Equation (1) mainly outlays the impact of tourism proxied by impact on the number of tourist arrivals (ITA)s (international tourism arrivals), international tourism receipts (ITR), and energy alternatives which are segregated into FFC (fossil fuel energy consumption), and REC (renewable energy consumption) on CO₂ (pollution emissions). In this equation, the FDI (foreign investment), GDP (economic growth), and GCF are working as control variables. Equation (2) mainly shows the impact of FD on CO₂ while equation (3) demonstrates the moderating impact of FD and ITA (ITA*FD). Similarly, equation (4) is an extension of equation (3) where the moderating role of FD and FFE (FFE*FD) was added to the linear model. In these equations, the subscript j is for the country while t is for the time effect. Similarly, α and β are the coefficients while ε j t is a residual term.

To test the formulated econometric equations, we apply the FMOLS model and check the robustness by applying the GLS model. However, the selection of these models depends upon some pre-condition models, for example, unit root testing, cointegration analysis, and cross-sectional dependence test. As the analysis contains many macroeconomic variables, it is necessary to check the stationarity status. Given that, the unit root analysis was conducted, and the results were reported in Table 4. In unit root testing, we employ the two techniques IPS (Im et al., 2003, Pesaran et al., 2001, and Shin W-stat), and ADF (Augmented Dickey-Fuller) test proposed by Im et al. (2003) and, Dickey and Fuller (1979) relatively. The statistical analysis shows that most variables are stationary at the first difference, suggesting checking the cointegration among variables. The cointegration among variables was investigated by employing the Johansen Cointegration test. The statistics shown in Table 5 confirm the existence of cointegration among variables. Based on statistical suggestions of pre-estimation techniques (unit root testing and cointegration analysis), we employ the FMOLS model to check the regression among variables. The estimated coefficients by employing the FMOLS model suggest the long-term relationship among the variables. This model was first proposed by Phillips and Hansen (1990) to estimate the macro panel data. As the analysis is based on the financial statistics of six economies, therefore there are more chances of cross-section dependency. The corresponding statistical values of a specific country can influence the statistical responses of other companion countries and thus can create biases in estimation. Given that, we check the cross-section dependence by employing a series of tests including Breusch-pagan LM (Breusch and Pagan, 1980), Pesaran LM, and Pesaran CD, and report the analysis in Table 6. The significant p-values (p ≤ 0.05) reject the null hypothesis, that is, no cross-section dependence in residuals and subjects to cross-section dependence. Therefore, we apply the GLS model to treat the observed problem of cross-section dependence and to check the robustness.

Descriptive and correlation analysis

Table 2 shows the descriptive analysis of the variables of the study. The mean value of CO₂ is 22.381, showing the average emissions of CO₂. This value further shows the intensity of environmental degradation in the GCC region. The mean value of ITA is 7892789.000 while the average value of tourism receipts (ITR) is 5.183%. This value shows the percentage of tourism revenue in comparison with the total GDP. The average value of FFE is 98.525, showing that the GCC economies are fulfilling their energy needs of 98.525% from non-renewable resources. Conversely, renewable resources have a minor contribution of 0.022% toward total energy consumption. The average FD is 56.617, indicating the percentage of domestic credit provided by financial institutions to the private sector in comparison with the total GDP. The average percentage inflow of foreign investment is 2.501 while the average economic growth is 4.640% with a maximum value of 26.170% (experienced by Qatar in 2006) and a minimum value of −7.076% (experienced by Kuwait in 2009). Lastly, the mean value of GCF is 26.457%, implying the fixed investment made by the central government in comparison with the total GDP. In addition to descriptive analysis, Table 3 shows the correlation statistics among the variables of the study. Supplementary Appendix Table A1 shows the average values of variables across each country and enhances the statistical understanding of the specific trend of each variable.

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Table 2.

Descriptive statistics.

Variables	Mean	Median	Std. Dev.	Max.	Min.
CO2	22.381	21.347	0.083	47.699	9.938
ITA	7892789.000	6217000.000	0.063	25282000.	1056933.1
ITR	5.180	3.470	0.040	18.490	0.500
FFE	98.520	99.950	0.090	100.001	86.120
REC	0.020	0.000	0.040	0.250	0.001
FD	56.610	56.360	0.080	105.180	24.230
FDI	2.500	1.650	0.020	15.750	−2.760
GDP	4.640	4.290	0.000	26.170	−7.076
GCF	26.457	26.472	0.651	49.486	10.665

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Table 3.

Correlation analysis.

Variables	CO2	ITA	ITR	FFE	REC	FD	FDI	GDP	GCF
CO2	1.000
ITA	−0.310a	1.000
ITR	0.020a	−0.040a	1.000
FFE	0.100b	−0.480a	0.110a	1.000
REC	−0.070a	0.620b	−0.160a	−0.658a	1.000
FD	0.060a	0.310b	0.210a	−0.423a	0.445b	1.000
FDI	0.040a	0.060a	0.043a	0.000a	0.060b	−0.130a	1.000
GDP	0.440a	−0.200a	−0.140a	0.110a	−0.100a	−0.320a	0.250b	1.000
GCF	0.330a	−0.100a	0.390b	0.170a	−0.150a	0.070a	0.200a	0.169a	1.000

Source: Authors’ calculations.

Note: a, b, significance level at 1%, and 5% level relatively.

Pre-estimation techniques

Pre-estimation approach analysis, we employ unit root testing (reported the results in Table 4) for stationarity check, the Johansen Cointegration test (reported the results in Table 5) for cointegration analysis, and cross-section dependence tests (reported the results in Table 6) to check the status of cross-section dependence. The discussion on these pre-estimation techniques has already been made in the methodology section.

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Table 4.

Unit root testing.

	(Im, Pesaran and Shin W-stat)		(ADF - Fisher Chi-square)
	At level	At first difference	At level	At first difference
CO2	(0.409) 0.658	(−3.132) 0.000***	(7.179) 0.845	(30.841) 0.002***
ITA	(3.040) 0.998	(−3.186) 0.000***	(4.589) 0.970	(34.368) 0.000***
ITR	(0.793) 0.786	(−7.411) 0.000***	(10.457) 0.575	(83.687) 0.000***
FFE	(0.803) 0.7893	(−4.967) 0.000***	(6.801) 0.870	47.994 0.000***
REC	(4.065) 1.000	(−1.790) 0.036***	(0.637) 0.999	17.061 0.029***
FD	(2.547) 0.994	(−2.932) 0.001***	(3.542) 0.990	(29.028) 0.003***
FDI	(−1.039) 0.149	(−2.970) 0.001***	(15.211) 0.230	(28.723) 0.004***
GDP	(−2.235) 0.012***	—	(25.182) 0.014***	—
GCF	(−1.624) 0.052***	—	(18.414) 0.103	—

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Table 5.

Johansen cointegration test.

Kao Residual Cointegration Test
Test Name	t-statistics	Probability
ADF	−1.568	0.058
Residual variance	1.762	—
HAC variance	1.816	—

Note: the significant probability value rejects the null hypothesis, that is, no cointegration exists.

Source: self-estimation.

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Table 6.

Cross-section dependence Test.

Test	Statistics	d.f.	Probability
Breusch-pagan LM	34.819	15	0.002
Pesaran scaled LM	2.523	—	0.011
Pesaran CD	−0.253	—	0.800

Note: the significant probability values of the first two tests reject the null hypothesis, that is, no cross-section dependence.

Source: Author’s estimation.

Regression findings

To check the regression among the variables, we employ the FMOLS and GLS models and report the results in Tables 7–10. The statistical analysis for equation (1) has been reported in Table 7. Following the coefficient values, the ITA has a significant positive coefficient value (0.511) while ITR has a significant negative coefficient value (−0.428). These values imply that a one-percent change in ITA can boost the CO₂ emissions by 5.11% while a one-percent change in ITR can reduce the CO₂ emissions by 4.28% relatively. Similarly, FFE has a positive coefficient value while REC carries a negative coefficient sign. Both values are significant at the 1% level. The robustness was approved in the case of ITA, ITR, and FFE while REC does not show robustness as it has an insignificant coefficient value of 1.446 while we apply the GLS model. All control variables, that is, FDI, GDP, and GCF show a positive correlation with CO₂ emissions. The regression analysis for testing equation (2) has been reported in Table 8. Controlling the other factors, FD shows a significant negative impact on CO₂ emissions. Similarly, the statistical analyses for equation (3) and equation (4) have been presented in Tables 9 and 10 Following the coefficient values, FD shows the moderating role both in the case of ITA and FFE. In Table 9, the coefficient value of ITA*FD is −0.881 which is significant at the 1% level. Similarly, the coefficient value of FFE*FD is −0.119 (in Table 10), implying that FD can mitigate the adverse impact of FFE on environmental quality.

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Table 7.

Impact of tourism and energy Alternatives on CO₂ emissions.

	CO2 emissions as a dependent variable
	FMOLS		GLS
Variables	Coefficients	Prob.	Coefficients	Prob.
ITA	0.511***	0.005	2.921***	0.000
ITR	−0.428***	0.018	−0.135***	0.029
FFE	0.640***	0.001	0.337***	0.001
REC	−1.112***	0.008	1.446	0.345
FDI	0.302***	0.005	−0.106	0.230
GDP	0.102***	0.004	0.276***	0.000
GCF	0.039	0.654	0.171***	0.000
Adjusted R-squared	0.915		0.548
SE of regression	2.524		0.836
Prob.(F-statistics)	—		0.000
Long-run variance	11.564		—

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Table 8.

Impact of FD on CO₂ emissions.

	CO2 emissions as a dependent variable
	FMOLS		GLS
Variables	Coefficients	Prob.	Coefficients	Prob.
FD	−0.111***	0.003	−0.212**	0.064
FDI	0.103***	0.043	0.227***	0.011
GDP	0.109***	0.003	0.225***	0.027
GCF	0.244***	0.014	0.125***	0.017
Adjusted R-squared	0.890		0.116
SE of regression	2.617		0.786
Prob.(F-statistics)	—		0.001
Long-run variance	13.829		—

Source: Authors' calculations.

Note: ***, **, *, denote the probability at 1%, 5%, and 10% levels relatively.

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Table 9.

Moderating role of Financial Development in the nexus Tourism-CO₂ emissions.

	CO2 emissions as a dependent variable
	FMOLS		GLS
Variables	Coefficients	Prob.	Coefficients	Prob.
ITA	0.840***	0.000	0.003***	0.015
ITR	−0.292***	0.004	−0.203***	0.002
FD	−0.157***	0.000	−0.135***	0.001
ITA*FD	−0.881*	0.106	−1.180***	0.008
FDI	0.112***	0.038	−0.118	0.254
GDP	0.172***	0.023	0.290***	0.002
GCF	0.233***	0.015	0.173***	0.002
Adjusted R-squared	0.885		0.473
SE of regression	2.671		0.860
Prob.(F-statistics)	—		0.000
Long-run variance	11.713		—

Source: Authors’ calculations.

Note: ***, **, *, denote the probability at 1%, 5%, and 10% levels relatively.

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Table 10.

Moderating role of FD in the nexus between energy alternatives and CO₂ emissions.

	CO2 emissions as a dependent variable
	FMOLS		GLS
Variables	Coefficients	Prob.	Coefficients	Prob.
FFE	1.094***	0.000	0.994***	0.001
REC	−1.267***	0.000	−1.224***	0.038
FD	−1.945***	0.000	−1.680**	0.070
FFE*FD	−0.119***	0.011	−0.017**	0.066
FDI	0.251***	0.005	0.227***	0.017
GDP	0.237*	0.097	0.273***	0.006
GCF	0.232*	0.100	0.061	0.308
Adjusted R-squared	0.836		0.292
SE of regression	2.096		0.764
Prob.(F-statistics)	—		0.000
Long-run variance	16.625		—

Source: Authors’ calculations.

Note: ***, **, *, denote the probability at 1%, 5%, and 10% levels relatively.

Policy implications

Based on underlying empirical findings, it can be recommended that the tourism arrivals should be aligned with the assumptions of green environmental protection. In this regard, the use of cleaner energies in transportation and hotels can serve as an important policy tool to reduce the negative impact of tourist arrivals on environmental quality. Similarly, the empirical analysis suggests enhancing the consumption of renewable energies to reduce pollution emissions. Notably, this study recommends an important policy regarding the role of FD in managing environmental quality. The policy officials should enhance the co-bounding of FD and the tourism industry because the financial sector can help the tourism industry regarding pollution mitigation. Similarly, the consumption of dirty energy can be reduced if the financial sector brings into the channel of energy consumption. The developed financial sector can help in updating the production systems and transforming from non-renewable to renewable energy utilization. Lastly, it is recommended that policy officials should ensure that foreign investors do not overpass the green environmental assumptions. Similarly, gross investment should be aligned with green environmental assumptions, and policy officials should focus more on sustainable economic growth. The current analysis is unable to explore the individual trend of each country due to panel considerations. However, future analysis can be conducted by incorporating this issue and adding some more variables like resource income as an explanatory variable.