Revival of sun-and-beach tourism through the lens of regulatory and risk dimensions of environmental sustainability

Abstract

Environmental sustainability is essential in tourism literature, and sun-and-beach tourism (SBT) is one of the most popular subsections of the tourism field. The appropriate policies and strategies during the COVID-19 pandemic to revive SBT growth through the lens of the regulatory dimension (RED) and risk dimension (RID) of environmental sustainability are gaining timely ground to conduct this research. The current study examined the nexus between SBT, RED, and RID utilizing three novel indexes (i.e., weighted sun-and-beach tourism index, weighted regulatory dimension index, and weighted risk dimension index) by employing the principal component analysis within the framework of six stages of empirical estimation strategy. These three novel indexes combine the most commonly used SBT, RED, and RID indicators. This research tested the CSD and homogeneous, then employed the second generation CIPS-CADF panel unit root test, used an AMG estimator, and employed the panel Toda-Yamamoto (PTY) causality test. The findings revealed that the RED positively influences SBT while the RID mitigates SBT. Results also indicate bidirectional causality between SBT, RID, and RED. In other words, changes in RID and RED have predictive power for the SBT, which further highlights the role of SBT on the RID and RED. Therefore, concerned authorities can focus on environmental sustainability design initiatives and appropriate policy/strategy implications to boost SBT.

Environmental sustainability; Regulatory dimension; Risk dimension; Sun-and-beach tourism; Panel data analysis.

1. Introduction

Environmental sustainability is essential in tourism literature (Kulözü-Uzunboy and Sipahi 2022; Streimikiene et al., 2021; Sarpong et al., 2020; Saint Akadiri et al., 2019a). Tourism is an important sector of the country’s economy, which arrays services and contributes 10.4% to global GDP (WTTC, 2020). Tourism contains the usage of humanity’s natural capital, boosts the country’s revenue, generates thousands of job opportunities, improves the infrastructures, and leads to cultural exchange (Collins, 1999). The tourism sector provides services to individuals and property, containing economic value and influencing the tax base and economy (Strange, 2005).

The tourism-environment nexus is interdependence (Păvăluc et al., 2020). “It [tourism] is a human activity which encompasses human behaviour, use of resources, and interaction with other people, economies and environments” (Holden, 2016). Holden’s definition of tourism is attentive to social or natural features that emphasize conserving the environment to sustain tourism because tourists interact with the environment, and touristic activities also include rides, sports, tours, and sunbathing. Environment considered as the destination’s nature or atmosphere for tourism, which involves water resources (waterfalls, sea, seaside, rivers, etc.), mountains, green environment (safaris, parks, forests, rainforests, plants, etc.), and wilderness (forest species, animals, wildlife, etc). “In environmental studies it has commonly been assumed that there exists a fundamental connection between a society’s management of natural resources and its perception of nature” (Brunn and Kalland, 1995, p.1).

A complex environment-tourism nexus indicates that tourism can positively and negatively affect environmental degradation (Păvăluc et al., 2020). Tourism could negatively affect the environment if the touristic activities increase compared to the environment’s ability to support it; tourism could positively influence if visitors know the fundamental idea of environmental protection and its sustainability (IvyPanda, 2019). The environmental quality is significant for tourism improvement (Sunlu, 2003) and tourist attractions (Mihalič, 2000), and it is crucial to sustaining the quality of enjoinment because fluctuations in environmental structure lead to the decline or upsurge in tourist destinations' competitiveness (CEC, 1994).

Budowski (2009) documented three relational aspects of tourism-environmental nexus for continuous tourism improvement and environmental protection: a) environmental protection and touristic actions can cause a conflict condition if excessive tourism affects the environment and its resultant mechanisms. Excessive tourism can significantly damage the environment so that several prescriptions or limits can be executed; b) the tourism-environment nexus may become evident by coexistence because this nexus may be noticeable positively, represented by a symbiosis, or have a negative influence, causing a conflict situation between the touristic activities and environment; c) the interdependence environment-tourism nexus purposes for both mechanisms to attain specific goals such as financial benefit and preserve natural resources. The tourism-environment nexus may positively impact society by improving the quality of life (Styles et al., 2013). Visitors show an increasing concentration on ecotourism (Nistoreanu, 2007), and ecotourism is considered a societal phenomenon with rising popularity (Hawkins and Lamoureux, 2001).

“Sun-and-beach tourism is one of the most popular segments of tourism markets” (Femenías Rosselló, 2019). The consistently increasing environmental concerns have raised various questions about sustainability in multiple domains (Holzinger et al., 2021; Saint Akadiri et al., 2019b). Tourism is not exempted from this perspective; therefore, it has been noticed that modern researchers are directing their research to investigate sustainable tourism and highlight different sustainability issues and aspects of tourism (Grilli et al., 2021; Streimikiene et al., 2021; Falatoonitoosi et al., 2021; Garg and Pandey 2021; Akadiri et al., 2020; Uzuner et al., 2020). It means the interest in sustainable tourism is growing in the modern era; however, the current advancements and researches in this field are still limited.

In this regard, a recent topic that has become the point of controversy among researchers is the role of sustainability in enhancing tourism growth because some researchers harmonize that competitiveness and growth of tourism are not linked with sustainability while other researchers emphasize the potential role of sustainability in the growth and competitiveness of tourism (Liu et al., 2022; Hallaj et al., 2022; Tleuberdinova et al., 2022; Streimikiene et al., 2021; León-Gómez et al., 2021; Zhu et al., 2021; Bazargani and Kiliç, 2021). Empirical literature contains different results using different periods, methodologies, datasets, and countries. To conserve space, we have not reported the details literature. However, one should consider the previous studies for comprehensive literature review surveys (Silva et al., 2022; Mondal and Samaddar, 2021; Ahmad et al., 2020; Tang and Abosedra, 2016; Brida et al., 2014, 2016; Pablo-Romero and Molina 2013) as these studies have already conducted literature survey in detail. Therefore, our study seeks to relevantly new theatrical and empirical conclusions in the context of incorporating three novel indexes that combine the most commonly used sun-and-beach tourism (SBT), regulatory dimension (RED), and risk dimension (RID) indicators, which have not attained much scholarly attention yet.

The impacts of the RED and RID of the environmental sustainability on tourism (Pulido-Fernández et al., 2019) and SBT growth could affect tourism destinations' economic sustainability and competitiveness in the long term (Femenías Rosselló, 2019). Tourists' attraction to destination and competitive position are negatively affected if the ecological degeneration surpasses a certain threshold. Therefore, ensuring the competitiveness of SBT is a crucial element when managing these tourist destinations. It provides stakeholders with more accurate marketing, environmental management, or driving destination competitiveness (Femenías Rosselló 2019; Claver-Cortés et al., 2007). According to Pulido-Fernández et al. (2015) that “all models that have been designed to identify and study the determinants of a destination’s competitiveness consider sustainability to be a key factor”. Sustainability has tremendous importance in tourism literature (Streimikiene et al., 2021; El-Aidie et al., 2021; Font et al., 2021). In addition to these studies, the reports were published by global institutions and international organizations in which a particular emphasis was made on the positive contribution of sustainability towards touristic development in the countries (WTTC 2021; UNWTO 2021; World Economic Forum 2020; Sachs et al., 2021).

Various studies have investigated factors influencing the quality of the environment within the environmental Kuznets Curve (EKC) hypothesis (Akadiri et al., 2019, 2021). Environmental quality could be improved if SBT is promoted because within the context of the environmental Kuznets Curve (EKC) hypothesis (Grossman and Krueger, 1991), the environmental quality-growth nexus as an inverted-U shaped “implying that environmental degradation first increases with increasing income level, but after a threshold, economic expansion helps to solve environmental problems” (Pata et al., 2021, p. 5). Finally, SBT helps drive economic activity (income level) important to nearby communities, which could affect the environment quality (Pata et al., 2022) threefold effects: scale, composition, and technique effect (Grossman and Krueger, 1991 as precise by Pata et al., 2021). The tourism-environmental nexus literature delivers information that allows policymakers, investors, and stakeholders to make decisions regarding promotion, conservational supervision, or managing destination competitiveness (Claver-Cortés et al., 2007; Femenías Rosselló, 2019), among others.

Despite the commercial significance of the SBT sector and its close link to the climate ad environment, substantial gaps remain in the research field of environmental sustainability. It means the interest in SBT is growing in the modern era; however, the current advancements and researches in this field are still limited. Timely research is needed to check the influence of RED and RID on SBT, which could clarify the contribution of sustainability in the tourism industry to help organizations and public administrations deal with problems associated with SBT. Hence the research objectives of this study are twofold: (1) Does the RED of environmental sustainability significantly influences SBT? (2) Does the RID of environmental sustainability significantly influences SBT? This research provides help in estimating the destination’s ability to attract sun-and-beach tourists. Hence, to the best of our knowledge, the present study incorporated a relatively popularizing new six stages empirical estimation strategy, which tested the CSD and homogeneous, then employed the second generation CIPS-CADF panel unit root test, used an AMG estimator, and employed panel Toda-Yamamoto (PTY) causality test. This study is also unique and the first attempt that used three novel indexes: weighted sun-and-beach tourism index (WSBTI), weighted regulatory dimension index (WREDI), and weighted risk dimension index (WRIDI). WSBTI, WREDI, and WRIDI are fascinating because these three novel weighted indexes contained combine' traditionally and most commonly used SBT, RED, and RID indicators by employing the principal component analysis.

2. Data description

Environmental sustainability refers to the rates of “renewable resource harvest, pollution creation, and non-renewable resource depletion” that can be maintained consistently. If these rates are not consistent, they are not sustainable (Peeters and Landré, 2011). Environmental sustainability contains two dimensions (i.e., regularity and risk dimensions). Pulido-Fernández et al. (2019) explained the empirical underpinnings of the division of dimensions of environmental sustainability. To conserve space, we have not reported the details literature. However, one should be considered the study of Pulido-Fernández et al. (2019) for a comprehensive literature review survey about the division of dimensions of environmental sustainability. At the same time, our study seeks a relevantly new empirical conclusion incorporating three novel weighted indexes (i.e., WSBTI, WREDI, and WRIDI), which have not attained much scholarly attention yet.

The dataset in our study comprises traditionally and most commonly used SBT indicators, RED indicators, and RID indicators of three novel weighted indexes as proxies for the SBT, RED, and RID, respectively. The current paper uses annual data from 2005 to 2020 about the abovementioned indicators—the data collected from the top eleven sun-and-beach destinations involved in the SBT activities (i.e., Australia, Fiji, Greek, Philippines, Maldives, Malaysia, Indonesia, Spain, Cuba, United States, and France). The purposive sampling used in the current study in which the abovementioned countries sample has been decided according to the purpose. Data about the most commonly used SBT, RED, and RID indicators were collected from secondary sources (see Table 1). The World Bank Development Indicators (WDI) and Travel & Tourism Competitiveness Index (TTCI) were preferred to collect data for the current paper.

Table 1.

Variables and sources.

Variables	Symbol	Unit of measurement	Data sources
sun-and-beach tourism
Inbound arrivals	IA	number of arrivals	WDI (2020)
international tourism receipts	ITR	Current US$	WDI (2020)
International tourism expenditures	ITE	Current US$	WDI (2020)
Risk dimension
Electricity production	EP	% of total	WDI (2020)
CO2 emissions	CO2	Metric tons per capita	WDI (2020)
Terrestrial protected areas	TPA	% of total land area	WDI (2020)
Regulatory dimension
Stringency of environmental regulation	SER	scale	TTCI (2020)
Environmental regulation enforcement	ERE	scale	TTCI (2020)
Quality of the natural Environment	QNE	Number of World Heritage natural sites	TTCI (2020)

Note: We re-scaled the different measures into a similar unit system (see section 2.1).

Traditionally, the measurement of tourism flow in the tourism literature has been widely dependent on three key variables separately: inbound arrivals (IA) (Yıldırım et al., 2021; Yuan et al., 2021; Adedoyin et al., 2021), international tourism receipts (ITR) (Wei and Ullah 2022; Yıldırım et al., 2021; Adedoyin et al., 2021), and international tourism expenditures (ITE) (Gedikli et al., 2022; Maneejuk et al., 2022; Nguyen et al., 2022). These three variables partially reflect environmental and economic growth because of their unique characteristics. Such as IA, ITR, and ITE reflect the total number of tourists, expense side, and income side, respectively. Secondly, three RID indicators are combined into a single index and used as a proxy for the RID: electricity production (EP) (Bekun et al., 2022; Alola et al., 2021; Khan and Hou 2021), CO2 emissions (CO2) (Siqin et al., 2022; Huang et al., 2021), and terrestrial protected areas (TPA) (Li et al., 2022; Abedin et al., 2022). Similarly, three RED indicators are combined into a single index and used as a proxy for the RED: stringency of environmental regulation (SER) (Lu et al., 2022; Albulescu et al., 2022), environmental regulation enforcement (ERE) (Chu and Tran, 2022; Zhou and Zhao, 2022; Zhang et al., 2021), and quality of the natural Environment (QNE) (Wei and Ullah, 2022; Tripathy et al., 2022; Yang et al., 2022; Yang and Li 2022).

2.1. Re-scaling the indicators

Table 2 presents the statistical summary of re-scale the indicators to calculate SBT, RED, and RID. In preparing the nine indicators (see Figure 1) as a standard process for homogenizing the information, standardized values were calculated from the average values of SBT indicators, RED indicators, and RID indicators separately for each year to re-scale the different measures into a similar unit system. Cronbach’s alpha was employed to check the internal consistency for the SBT (Cronbach’s alpha value was 0.714 in 2005 and 0.756 in 2020), RED (Cronbach’s alpha value was 0.934 in 2005 and 0.937 in 2020), and RID (Cronbach’s alpha value is 0.853 in 2005 and 0.875 in 2020). The values of Cronbach’s alpha confirm the creamy consistency of scale in both cases.

Table 2.

Statistical summary of re-scaling the indicators to calculate SBT, RED, and RID.

Indicators	2005 Average		2020 Average		Mean Diff.	Mean Diff. 95% CI
	Mean	Std. Dev.	Mean	Std. Dev.		Min.	Max.
IA	35.362	9.505	44.892	11.983	9.53	3.864	14.559
ITR	1881.1	484.917	1790.854	496.731	-90.246	-158.904	13.46
ITE	5.981	0.98	9.071	1.723	3.09	0.865	3.531
SER	4.871	0.084	4.905	0.089	0.034	0.152	0.37
ERE	4.207	0.099	4.932	0.097	0.725	-0.031	0.252
QNE	4.991	0.087	4.013	0.091	-0.978	-0.301	0.004
CO2	6.155	0.744	4.971	0.492	-1.184	-0.601	-0.041
TPA	14.023	0.965	16.81	0.994	2.787	1.821	4.952
EP	5.106	0.396	5.998	0.521	0.892	0.089	0.845

Note: A bootstrap method for estimation, as argued by Zaman et al. (2016), there is a high chance of multicollinearity during the regression model in response to the contemporary use of these variables because these variables highly correlated with each other (normality assumption was not met).

Figure 1.

An overview of the preparation of the variables.

2.2. Novel weighted indexes: WSBTI, WREDI, and WRIDI

Zaman et al. (2016) argued that there are high chances of multicollinearity during regression models in response to the contemporary use of these variables because these variables are highly correlated. In this context, the use of principal component analysis (PCA) is a relatively an advantageous method, enabling to analyze of a precise and comprehensive picture of the nexus between SBT, RED, and RID by employing the majority of relevant and accurate information of the SBT indicators, RED indicators, and RID indicators into single indexes separately: weighted sun-and-beach tourism index (WSBTI), weighted regulatory dimension index (WREDI), and weighted risk dimension index (WRIDI). Table 3 depicts the overall results of PCA for WSBTI, WREDI, and WRIDI alone with their concerning indicators. Results show the relevance of the first principal component as the eigenvalues exceed 1. The eigenvalues of WREDI, WRIDI, and WSBTI are 2.951, 2.864, and 2.763, respectively. Furthermore, weighted indexes also have a high positive correlation as the correlation coefficients exceed 0.7.

Table 3.

PCA results: WSBTI, WRIDI, and WREDI.

WI	EV	PE1stPC	FL1stPC			CWWI
WSBTI	2.951	0.984	IA	ITR	ITE	IA	ITR	ITE
			0.573	0.58	0.573	0.956	0.957	0.982
WRIDI	2.763	0.942	EP	CO2	TPA	EP	CO2	TPA
			0.592	0.581	0.573	0.879	0.782	0.935
WREDI	2.864	0.963	SER	ERE	QNE	SER	STTID	ERE
			0.581	0.592	0.571	0.841	0.932	0.954

Note: This table depicts the overall results of principal component analysis (PCA). WI, AVI, PE1^stPC, FL1^stPC, and CWWI denote average values of indicators, Eigenvalue, proportion explained by first principal component, factor loading of first principal component, and correlation with weighted index, respectively. WSBTI, WRIDI, and WREDI denote the weighted sun-and-beach tourism index, risk dimension index, and regulatory dimension index. AI, ITR, ITE, EP, CO2, TPA, SER, ERE, and QNE denote inbound arrivals, international tourism receipts, international tourism expenditures, electricity production, CO2 emissions, terrestrial protected areas, the stringency of environmental regulation, environmental regulation enforcement, and quality of the natural environment, respectively.

3. Empirical methodology

We followed a six-step empirical estimation strategy, as shown in Figure 2. The approaches are selected according to the analysis requirements at each stage.

Figure 2.

Six stages of empirical estimation strategy.

3.1. Cross-sectional dependence

Firstly, cross-sectional dependence (CSD) means joint shocks and unobservable effects between cross-section units because a shock (positive or negative) in one country’s economy can influence another country’s economy. Three different CSD tests can determine this dependence. Breusch and Pagan (1980) proposed the Lagrange Multiplier (LM) test.

$$\text{LM}=T\sum _{i=1}^{N-1}\sum _{j=i+1}^N{\hat{P}}_{ij}^2,{\chi }_{N(N-1)/2}^2,\text{i}\ne \text{j}$$ (1)

Pesaran (2004) criticized the Breusch-Pagan LM test because it is ineffective when N is large and developed $CD$ and $C{D}_{LM}$ tests to encounter this issue.

$$C{D}_{LM}=\sqrt{\frac{1}{N(N-1)}}\sum _{i=1}^{N-1}\sum _{j=i+1}^N(T{\hat{P}}_{ij}^2-1),N\left(\mathrm{0,1}\right)$$ (2)

$$CD=\sqrt{\frac{2T}{N(N-1)}}\left(\sum _{i=1}^{N-1}\sum _{j=i+1}^N{\hat{P}}_{ij}\right),N\left(\mathrm{0,1}\right)$$ (3)

3.2. Slope homogeneity

Secondly, Pesaran and Yamagata (2008) proposed delta tests ($\hat{\Delta }$ and ${\hat{\Delta }}_{adj}$) based on the $\tilde{S}$ statistic developed by Swamy (1970) to deal with panel data that may not be homogeneous (Eqs. (4) and (5)).

$$\hat{\Delta }=\sqrt{N}\left(\frac{N^{-1}\tilde{S}-k}{\sqrt{2k}}\right)$$ (4)

$${\hat{\Delta }}_{adj}=\sqrt{N}\left(\frac{N^{-1}\tilde{S}-E\left({\tilde{z}}_{it}\right)}{\sqrt{var\left({\tilde{z}}_{it}\right)}}\right)$$ (5)

3.3. Panel unit root tests

Thirdly, we considered CSD in this study by employing Pesaran’s (2007) proposed the second generation augmented cross-sectional IPS panel unit root test (CIPS), which led to improved statistical presentation than the first-generation panel unit root test. CIPS extends the test of Im et al. (2003) IPS for evaluating CSD by considering cross-sectional averages (Eqs. (6) and (7)).

$$\Delta y_{it}=a_i+g_{i}t+b_i{y}_{i,t-1}+c_i{\overline{y}}_{t-1}+\sum _{j=0}^p{d}_{ij}\Delta {\overline{y}}_{t-j}+\sum _{j=0}^p{\phi }_{ij}\Delta {\overline{y}}_{i,t-j}+e_{it}$$ (6)

$$CIPS\left(N,T\right)=N^{-1}\sum _{i=1}^{N}CAD{F}_i$$ (7)

3.4. Bootstrap LM panel cointegration test

Fourthly, to test the cointegration relationship, we employed the LM panel cointegration test (Eq. (8)) to include CSD proposed by Westerlund and Edgerton (2007).

$$L{M}_N^{+}=\frac{1}{N{T}^2}\sum _{i=1}^N\sum _{t=1}^T\left({\hat{\omega }}_i^{-2}{S}_{it}^2\right)$$ (8)

3.5. Long-run estimator

Fifthly, this study used an augmented mean group (AMG) estimator to check the long-term coefficients of variables, see Eq. (9), which Eberhardt and Teal (2010) suggested.

$$\Delta y_{it}=a_i+b_i\Delta X_{it}+{Ф}_i{\pounds }_t+\sum _{t=2}^T{\tau }_t{D}_t+{€}_{it}$$ (9)

3.6. Panel Toda-Yamamoto causality test

Finally, we employed Toda-Yamamoto (PTY) causality test (Toda and Yamamoto, 1995). Emirmahmutoglu and Kose (2011) suggested a new test based on merging the test statistics of the PTY test, as shown in Eq. (10), by following the meta-analysis approach of Fisher (1992).

$$PTY=-2\sum _{i=1}^N\text{ln}\left(p_i\right)\text{ i }=\text{ }1\text{, }2\text{, }3\text{,}\dots \text{,N}$$ (10)

This test contains the ability to test the whole panel against the alternative hypothesis to evaluate Granger causality exists from $X_{it}$ to $Y_{it}$.

4. Results

We used Eviews 12 student version and Stata 15.1 × 64 to conduct data analysis. The study’s empirical analysis comprises six steps (see Figure 2). We examined the CSD and homogeneity for the SBT, RED, and RID and the model in Eqs. (1), (2), and (3). Table 4 implies that the SBT, RED, RID, and Eq. (1) model contains CSD. This study rejected the null hypothesis and employed the second-generation panel data methods to comprise CSD and heterogeneity.

Table 4.

Results of CSD and homogeneity.

	Breusch-Pagan LM	Pesaran scaled LM	Pesaran CD
lnSBT	215.4739∗∗∗	14.25178∗∗∗	4.350608∗∗∗
lnRED	324.9354∗∗∗	24.68852∗∗∗	1.028383
lnRID	388.0202∗∗∗	30.70343∗∗∗	11.2113∗∗∗
(Pesaran and Yamagata, 2008. Journal of Econometrics) H0: slope coefficients are homogenous
Slope Homogeneity		Test Statistics	p-value
$\hat{\Delta }$ test		4.323∗∗∗	0.000
${\hat{\Delta }}_{adj}$ test		5.295∗∗∗∗	0.000

Note: ∗ indicate significance at 1% level.

In the third stage, we used the CIPS panel unit root test. The results are presented in Table 5. Results show that SBT, RED, and RID contain non-stationary at the level but attained stationary at first differences.

Table 5.

CIPS panel unit root test results.

Variables	Level	First Difference
lnRID	1.70944	4.15808∗
lnRED	1.72178	3.91255∗
lnSBT	1.89406	3.85441∗

Note: ∗ denote the rejection of the null of unit root at 1% level. The critical value of the CIPS at the 1% level is -2.63.

In the fourth stage, we used the panel LM cointegration test to explore the long-term relationships using the panel LM cointegration test. The cointegration test results accepted the null of cointegration demonstrated in Table 6. Consequently, this study concluded that SBT, RED, and RID move together in the long term.

Table 6.

Results of LM bootstrap panel cointegration.

Tests	Coefficient	p-value
Constant	-0.8448	0.1991
Constant and Trend	1.1914	0.1167

Then, we employed AMG estimator to assess the long-run coefficients (see Table 7). The findings suggest that a 1 unit increase in RED leads to 0.52% increase in SBT insignificantly, while a 1 unit increase in DIR leads to a 0.75% decrease in SBT. The results imply a direct connection between RED and SBT in the long run. These findings are consistent with the finding of previous research (Wan and Li, 2013; Sirakaya-Turk et al., 2014). The findings also show an inverse relationship between RID and SBT in the long run. These outcomes are also consistent with the previous studies (Peeters and Landré, 2011; Pulido-Fernández et al., 2015).

Table 7.

The AMG estimation.

Tests	Coefficient	Standard Error	p-value
lnRED	0.5221881∗ (3.31)	0.157862	0.001
lnRID	-0.7559881∗∗ (2.19)	0.345793	0.029
Constant	4.000358∗ (3.53)	1.131855	0.000

Note. ∗ and ∗∗ indicate significance at 1% and 5% levels, respectively.

Finally, we investigate the causal effects among the SBT, RID, and Red using the PTY panel causality test. The findings demonstrated in Table 8 show a bidirectional causality between SBT, RID, and RED. In other words, changes in RID and RED have predictive power for the SBT. This further highlights the role of SBT on the RID and RED. Results of Table 8 indicate that regulatory and risk dimensions have significant granger causality with the SBT because the p-value is less than .05, and the null hypothesis is rejected. Hence, the current study suggests findings in line with previous ones and finds enough support from the existing literature (Wan and Li, 2013; Sirakaya-Turk et al., 2014; Peeters and Landré, 2011; Pulido-Fernández et al., 2015).

Table 8.

Results of PTY bootstrap causality test.

Variables	statistic	p-value
lnRID → lnSBT	25.32879∗	0.0014
lnRED → lnSBT	61.69646∗	0.0000
lnSBT → lnRID	19.32898∗∗	0.0132
lnSBT → lnRED	14.44016∗∗∗	0.0710

Note: ∗, ∗∗ and ∗∗∗ indicate significance at 1%, 5% and 10% levels, respectively.

5. Conclusions and discussion

The contribution and novelty of the research are: 1) this study examines the nexus between SBT, RED, and RID; 2) it utilized the three novel indexes (i.e., weighted sun-and-beach tourism index, weighted regulatory dimension index, and weighted risk dimension index) by employing the principal component analysis; 3) presented an aggregate (panel) level picture tourism-environmental nexus within the top eleven sun-and-beach destinations involved in the SBT activities; 3) tested the CSD and homogeneous, then employed the second generation CIPS-CADF panel unit root test, used AMG estimator, and employed panel Toda-Yamamoto (PTY) causality test.

The present study’s findings can be summarized: Firstly, we found a long-term link between the SBT, RED, and RID. Secondly, RED increases the SBT while RID mitigates SBT. Finally, results show a bidirectional causality between SBT, RID, and RED. In other words, changes in RID and RED have predictive power for the SBT, which further highlights the role of SBT on the RID and RED. These crucial conclusions could offer several appropriate policies and strategies to revive SBT, and the findings of our study can also be meaningfully viewed in several aspects by covering the existing research gap in environmental sustainability and SBT. The direct positive correlation between the RED and SBT enhances tourism growth, confirming this study’s proposed hypothesis. At the same time, the direct negative correlation between the RID and SBT indicates the reduction of environmental sustainability because the parameters that measure the RID are inversely proportional to sustainability. These findings are consistent with the results obtained by most international organizations and institutions and most scientific literature.

Therefore, in the context of management implications, findings indicate that concerned authorities should focus on the SBT industry by improving/adopting tourism-oriented policies. The stakeholders of SBT destinations should encourage environmental variables that lead to increased tourism activities because tourism undoubtedly contributes to the host society and limits the negative influence such action poses on the environment and its future development prospects. Results also indicate that policymakers should focus on tourism activities to boost the tourism industry, leading to economic growth and vice versa. The current study has important implications for theory and practices because the findings indicate a valuable addition to the current literature about sustainability in SBT. This study could benefit tourist firms by enhancing their growth and economic performance through sustainable SBT. Our study also provides practical implications for the environmental and sun-and-beach touristic improvement. The concerned authorities should focus on triggering the importance of RED and RID within pro-environmental campaigns. Countries' policymakers will be able to make appropriate policies to realize the positive contribution of environmental sustainability to SBT. Besides these contributions, the current study has certain limitations.

5.1. Limitations and future research

Although this study provides fruitful insights and represents a pioneering attempt and preliminary steps to investigate the nexus between SBT, RED, and RID utilizing three novel indexes impact, it does have some shortcomings that open avenues for future work. The findings could not be generalized as a theory yet, because our study reflects only the tourism-environment nexus and is limited to only the top eleven sun-and-beach destinations. A larger panel sample and different periods should also be considered to generalize the results of this research in future research. This study presented an aggregate (panel) level picture tourism-environmental nexus within the top eleven sun-and-beach destinations involved in the SBT activities but does not indicate a cross-sectional country-level picture of the tourism-environmental nexus. This blinking area leads toward future research. Future researchers should assess this relationship in other segments of tourism. Furthermore, the current study considered only two dimensions of environmental sustainability while many other dimensions can affect the SBT, so future researchers should go for more dimensions of sustainability. Future research may further expand to the different regional level tourism-environment nexus through a longitudinal study and expand the sample for more reliable results.

Declarations

Author contribution statement

Zhimin Zhou and Shafaqat Mehmood: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Ather A. Khan, Zahid Ahmad and Salman Khan: Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This work was supported by the National Natural Science Foundation of China [grant number: 72172093 and 71832015].

Data availability statement

Data included in article/supp. material/referenced in article.

Declaration of interest’s statement

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