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Abstract

Tourism drives economic growth and impacts the environment. This study examines the nonlinear relationships among tourism development, economic growth, foreign direct investment, economic globalization, renewable energy consumption, and greenhouse gas (GHG) emissions in the Organization for Economic Co-operation and Development economies from 1995 to 2020. It also explores how green technological innovation (GTI) moderates and influences the link between tourism development and GHG emissions. We employ panel data analysis techniques, including fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), canonical cointegrating regression (CCR), feasible generalized least squares (FGLS), and the method of moments quantile regression (MMQR). We find an inverted U-shaped relationship between tourist numbers and GHG emissions and a U-shaped relationship between tourism receipts and GHG emissions. The moderating effect of GTI flattens the inverted U-shaped curve, thereby reducing the negative environmental impact of tourism to some extent. Our findings suggest that advancing GTI in the tourism industry is crucial for mitigating environmental degradation while promoting economic growth.

Plain language summary

Tourism drives economic growth and impacts the environment. This study examines the relationships among tourism development, economic growth, foreign direct investment (FDI), economic globalization, renewable energy consumption, and greenhouse gas (GHG) emissions in Organization for Economic Co-operation and Development (OECD) economies between 1995 and 2020. It also explores the moderating and threshold effects of green technological innovation (GTI) on the relationship between tourism development and GHG emissions. The empirical results reveal an inverted U-shaped relationship between tourist numbers and GHG emissions and a U-shaped relationship between tourism revenue and GHG emissions. We find that the moderating effect of GTI flattens the inverted U-shaped curve, thereby reducing the negative environmental impact of tourism to some extent. Consequently, advancing GTI in the tourism industry is crucial for mitigating environmental degradation.

Keywords

tourism development greenhouse gas emissions green technology innovation foreign direct investment environmental Kuznets curve

Highlights

Tourism boosts economic growth but raises environmental concerns.

The study shows tourism affects emissions nonlinearly in OECD countries.

FDI increases emissions, while globalization and renewable energy reduce them.

Green technological innovation is key in mitigating tourism’s environmental impact.

Introduction

During the COVID-19 pandemic and post-pandemic era, revitalizing green economic growth and promoting environmental sustainability have become crucial (Yousaf et al., 2023). Countries are seizing this opportunity to drive the green transformation of their economies (Shang et al., 2023; Werikhe, 2022), focusing on reducing carbon emissions, with ecotourism emerging as a key focus. In November 2021, at the 26th United Nations Climate Change Conference (COP26), countries reaffirmed their commitment to the Paris Agreement goals, emphasizing the need to reduce global carbon dioxide ( $C O_{2}$ ) emissions by 45% to achieve net-zero emissions by the mid-century and calling for urgent effective action. Regarding tourism development, the United Nations World Tourism Organization estimated that the tourism industry’s direct GDP would return to pre-pandemic levels in 2023, reaching $3.3 trillion, accounting for 3% of global GDP. By early 2024, international arrivals were projected to recover to 97% of 2019 levels (UN Tourism, 2024). Tourism significantly drives global economic growth and is a major source of income for many nations (Ahmad et al., 2022). It enhances quality of life, fosters social development, and boosts international cooperation (Ramkissoon, 2023). Increased tourist flows and revenue facilitate capital flows, attract investments, and create employment (Avcı et al., 2024; Kaya et al., 2022). Additionally, tourism fosters cultural exchange, promotes innovation, and supports sustainable economic development (Shang et al., 2023).

While the tourism industry has significantly contributed to economic development, it faces criticism for increasing GHG emissions and negatively impacting environmental quality (Koçak et al., 2020). Traditional perspectives suggest that tourism’s negative environmental effects primarily stem from tourists’ carbon footprint, associated with activities including hotel accommodation, shopping, dining, and transportation at destinations (Nepal et al., 2019; Y. Y. Sun et al., 2020). These activities typically lead to significant GHG emissions. However, international tourism can also enhance environmental quality by promoting economic growth in tourist destinations, encouraging environmentally friendly technologies, and supporting green infrastructure (Balsalobre-Lorente & Leitão, 2020; Erdoğan et al., 2022). Therefore, the relationship between tourism development and environmental pollution remains debated.

Similar to tourism expansion, which involves infrastructure development, foreign direct investment (FDI) encompasses infrastructure construction, production, logistics, and transportation, all of which consume significant energy and contribute to increased $C O_{2}$ emissions (Y. Sun et al., 2022). However, according to the pollution halo hypothesis, FDI can mitigate its negative environmental impact by transferring advanced technologies, such as high-efficiency energy equipment, pollution control systems, and clean production processes (Abid et al., 2022; Y. Sun et al., 2022). These environmentally friendly technologies enhance resource utilization efficiency (Saqib & Dincă, 2024), reducing pollution in the production process and partially alleviating FDI’s environmental burden. As global emphasis on sustainable development grows, green technology innovation (GTI) has become crucial for fostering economic growth and environmental protection. GTI improves resource utilization and reduces pollution to improve ecological quality (Hu et al., 2022; Yang et al., 2021). It also optimizes industrial structures, promotes industrial upgrading, and significantly reduces $C O_{2}$ emissions, thereby contributing to environmental sustainability (Wu & Liu, 2021; Yue et al., 2021). Technological innovation is crucial for industries that rely on natural resources, such as tourism, The GTI can effectively lower pollutant emissions, support eco-tourism, and advance sustainable tourism (Avcı et al., 2024; Erdoğan et al., 2022). Despite research on the impacts of technological innovation and environmental technologies, the role of GTI’s role in improving the tourism industry’s environmental performance remains underexplored.

This study focuses on OECD economies because of their well-established tourism industries and advanced infrastructure (Scott & Marzano, 2015). These countries lead in GTI, actively promoting sustainable technologies and contributing valuable experience to global emission reduction efforts (Z. Wang et al., 2023). As key participants in global $C O_{2}$ emissions governance, OECD countries have gained extensive experience in formulating and implementing environmental policies.

The study explores the impact of tourism development, GTI, FDI, economic growth, renewable energy consumption, and economic globalization on GHG emissions in the context of climate change and tourism. The key questions addressed are: (1) How does tourism development affect GHG emissions, what are the relationships among tourist numbers, tourism receipts, and GHG emissions? (2) What is the impact of FDI on GHG emissions in OECD countries? (3) How do economic globalization and renewable energy consumption contribute to emission reduction? (4) Do GTI and tourism development interact to slow the pace of GHG emissions?

This study enhances understanding of the tourism industry’s environmental effects by examining the following aspects. First, it investigates how tourist numbers and tourism revenue affect GHG emissions, to better understand tourism’s environmental effects. Second, it assesses the role of FDI in GHG emissions and explores potential challenges to ecological protection. Third, it analyzes how economic globalization and renewable energy consumption contribute to emission reduction. Fourth, this study incorporates GTI as a moderating variable in a nonlinear model and verifies its effects through threshold tests. This study examines whether GTI can moderate the impact of tourism development on GHG emissions, thus contributing to ecological and sustainable tourism development and improving environmental quality. These analyses offer new insights into the effects of various factors on GHG emissions and provide theoretical support for relevant policy formulation.

The remainder of the paper is organized as follows: Chapter 2 provides a literature review; Chapter 3 includes variable description, model specification, theoretical framework, and preliminary regression tests; Chapter 4 integrates empirical results analysis, asymmetry tests, robustness checks, the moderating effects of nonlinear relationships, and threshold tests; finally, Chapter 5 summarizes the research findings and presents policy recommendations.

Literature Review

Tourism and Environmental Sustainability

Tourism significantly contributes to economic growth, and job creation, making it vital for national economic development (Usman et al., 2020). However, it also impacts $C O_{2}$ emissions while promoting economic development (Adedoyin & Bekun, 2020). To limit global warming to 1.5°C to 2.0°C and meet international goals, the tourism sector must collaborate with other economic sectors to reduce emissions. This involves halving emissions by 2030 and achieving net-zero emissions by mid-century (Gössling et al., 2023). Recent studies have examined the environmental impacts of tourism development across various countries. Paramati et al. (2017) found that tourism boosts economic growth and $C O_{2}$ emissions, with a stronger impact on emissions in developed economies.

Conversely, Voumik et al. (2023) posited that increased inbound tourism reduces $C O_{2}$ emissions in 40 Asian countries. Thi et al. (2023) highlighted that while international tourism worsens environmental degradation, renewable energy, trade openness, and innovation help reduce emissions. Additionally, Liu et al. (2023) argued that in China, tourism revenue does not significantly impact environmental quality; instead, economic growth and energy use are the primary drivers of pollution. Y. Sun et al. (2021) indicated that in Malaysia, tourism promotes economic growth and helps mitigate $C O_{2}$ emissions at higher levels, with bidirectional causality between tourism, economic growth, and emissions. Demirtaş (2024) analyzed the top 10 countries in international tourism and concluded that increased international tourists are associated with reduced green growth. Ozturk et al. (2023) revealed that the impact of tourism on $C O_{2}$ emissions varies across 20 major global tourist destinations, showing both positive and negative effects. This suggests the need for country-specific policies to balance economic growth and environmental impact. Koçak et al. (2020) argued that while an increase in inbound tourists raises $C O_{2}$ emissions, tourism revenue helps reduce them, demonstrating a long-term relationship between tourist numbers, tourism revenue, and emissions. Similarly, Wei and Ullah (2022) found that tourism development in 37 Asian countries improves overall environmental quality, while its impact on $C O_{2}$ emissions varies; they initially increase and then decrease depending on tourism activity levels.

Scholars have increasingly examined the nonlinear relationship between tourism and environmental impact. Raza et al. (2021) noted an inverted U-shaped relationship in the top 20 global tourist destinations, where tourism initially increased environmental degradation but later reduced it. Zeng et al. (2021) observed an inverted U-shape for PM2.5 and a U-shape for industrial $S O_{2}$ emissions in China’s prefecture-level cities. Ochoa-Moreno et al. (2022) identified a U-shaped relationship between tourism revenue and $C O_{2}$ emissions in 20 Latin American countries. Purwono et al. (2024) discovered an inverted N-shape in 77 countries, where $C O_{2}$ emissions first decreased, increased, and then decreased, with renewable energy playing a significant role. These studies underscore the complex and nonlinear nature of tourism’s environmental impacts. These nonlinear relationships validate the robustness of the research methodology and provide a clearer theoretical foundation for the research hypothesis. Therefore, we propose the following research hypotheses.

H1: A non-linear relationship exists between tourist numbers/tourism revenue and GHG emissions in OECD countries.

FDI and Environmental Sustainability

Research on the impact of FDI on $C O_{2}$ emissions offers two primary perspectives. The pollution-haven hypothesis suggests that firms from industrialized countries relocate to regions with looser environmental regulations to reduce costs and evade stringent domestic standards (Walter & Ugelow, 1979). Conversely, the pollution halo hypothesis argues that multinational corporations introduce advanced environmental technologies to host countries that improve local environmental standards through technology spillovers (Levinson & Taylor, 2008). Both perspectives highlight the trade-offs between environmental and economic factors, with tourism playing a significant role. Lee and Brahmasrene (2013) found that tourism and FDI both foster economic growth and reduce $C O_{2}$ emissions in the EU by identifying a long-term equilibrium among these factors. Zhuang et al. (2023) illustrated that in Belt and Road countries, FDI and technological innovation can reduce $C O_{2}$ emissions, while tourism and its interaction with FDI have increased emissions. Rahaman et al. (2022) discovered a long-term inverted U-shaped relationship between $C O_{2}$ emissions and economic development. Tourism helps reduce $C O_{2}$ emissions, whereas FDI increases them, with a one-way causal relationship between FDI and $C O_{2}$ emissions.

Chen (2023) employed a nonlinear autoregressive distributed Lag (NARDL) model to examine the impact of policies on China’s tourism industry, finding that economic and tourism policies promote tourism growth, whereas environmental factors hinder sustainability. Abid et al. (2022) noted that FDI, financial development, and technological innovation significantly reduce $C O_{2}$ emissions in G8 countries, emphasizing the role of high-quality FDI in industrial and technological progress. Conversely, Kayani et al. (2023) reported that FDI, tourism, economic growth, and urbanization increased $C O_{2}$ emissions in the top 10 most polluted countries, indicating significant environmental pressure. Saqib and Dincă (2024) revealed that positive shocks, FDI, environmental technology, and renewable energy reduce $C O_{2}$ emissions, whereas negative shocks may increase long-term. J. Wang and Choi (2024) examined the dynamic impact of political crises on Chinese tourism, highlighting the tension between security concerns and national sovereignty. Their study revealed that political crises can significantly affect tourism flows and, consequently, the environmental impacts associated with tourism. Their research highlights the importance of considering geopolitical factors when analyzing the interplay between tourism development, FDI, and environmental sustainability. These studies demonstrate that while the impacts of FDI and tourism on the environment vary, they collectively influence the relationship between economic development and environmental protection. We therefore proposed the following hypothesis:

H2: FDI significant influences GHG emissions in OECD countries.

GTI and Environmental Sustainability

GTI is essential for achieving environmental sustainability by enhancing resource efficiency and reducing emissions (Hu et al., 2022; Hui & Choi, 2024; Xu et al., 2021; Yang et al., 2021). In the tourism industry, eco-friendly measures and green technologies can mitigate negative environmental impacts. Thus, advancements in sustainable tourism and technological innovation can significantly alleviate the pressure from environmental degradation (Ullah et al., 2023). Razzaq et al. (2020) found that in China tourism development reduces $C O_{2}$ emissions across all levels, whereas technological innovation has a more pronounced effect on high-emission scenarios.

Similarly, Yue et al. (2021) concluded that green innovation and tourism reduce $C O_{2}$ emissions in various regions, including Turkey and ASEAN countries, recommending their promotion to improve environmental quality. Y. Sun et al. (2021) utilized a quantile autoregressive distributed lag model to analyze the impact of tourism and ecological innovation on $C O_{2}$ emissions and ecological footprints in Turkey. These results indicate that both tourism and ecological innovation significantly enhance Turkey’s environmental quality, suggesting that promoting these areas may be an effective strategy to reduce $C O_{2}$ emissions and improve environmental conditions. Lv et al. (2023) found that while financial development and economic growth increase China’s ecological footprint, sustainable tourism and GTI help reduce it. Avcı et al. (2024) similarly suggested that in the top 15 tourist countries, both tourism development and GTI effectively suppress $C O_{2}$ emissions.

Conversely, Guan et al. (2022) argued that tourism, globalization, and economic growth increase the ecological footprint, whereas technological innovation alleviates environmental burdens. Ahmad et al. (2022) found that stronger technological innovation in more developed provinces leads to higher $C O_{2}$ emissions, with an inverted U-shaped relationship between international tourism and $C O_{2}$ emissions. Chau et al. (2023) indicated that increased international tourism income and visitor numbers in China raise $C O_{2}$ emissions, while ecological innovations improve environmental quality. Similarly, Lv et al. (2023) noted that financial development and economic growth increase China’s ecological footprint; however, sustainable tourism and GTI reduce it. Kumail et al. (2024) highlighted that while tourism and technology initially increase $C O_{2}$ emissions in major Asian destinations, surpassing an innovation threshold gradually reduces them, thereby emphasizing the integration of green technologies in tourism.

Researchers have analyzed the interaction between technological innovation and tourism in reducing $C O_{2}$ emissions. Erdoğan et al. (2022) discovered that international tourism initially increases $C O_{2}$ emissions but decreases after a threshold, with eco-friendly transportation technologies effectively mitigating tourism’s environmental impacts. Nazneen et al. (2023) indicate that while tourism and technological innovation significantly reduce $C O_{2}$ emissions and energy consumption increase them. Furthermore, the interaction between technological innovation and tourism positively moderates $C O_{2}$ emissions, leading to an increase in emission levels. Balsalobre-Lorente et al. (2024) found an inverted U-shaped relationship between economic complexity and ecological footprint in G7 countries. Additionally, they revealed that human development, innovation, and renewable energy consumption help reduce the ecological footprint, promoting environmental sustainability. Other scholars have emphasized the role of consumer behavior and corporate social responsibility in promoting sustainability (Farah & Shahzad, 2020; N. R. Park et al., 2024; Shahzad et al., 2019; M. Wang et al., 2020; Xiao et al., 2020; Z. Yin et al., 2022; Z. H. Yin et al., 2023; Yuan et al., 2024). Therefore, we therefore proposed the following hypothesis.

H3: GTI moderates the relationship between tourism development and GHG emissions.

H4: Economic globalization and renewable energy consumption contribute to reducing GHG emissions in OECD countries.

Research Gap

This study analyzes OECD countries using advanced econometric methods, including FMOLS, DOLS, CCR and FGLS for regression analysis. Additionally, the MMQR model is employed for asymmetric analysis. Unlike many studies that typically use carbon dioxide as an environmental proxy, this study focuses on GHG emissions as an indicator, offering a comprehensive analysis of the effects of tourism development and other control variables on GHG emissions.

Furthermore, this study incorporates an interaction term between GTI and tourism development, employing a panel threshold model to re-evaluate whether their combined effect contributes to the GHG reduction process. The existing literature primarily overlooks this interaction, particularly in exploring how tourism development, in conjunction with GTI, influences environmental sustainability. By addressing this gap, the study provides a fresh and innovative research perspective.

Data and Methodology

Variable

This study examines the impact of tourism and FDI on GHG emissions, using tourism as the core explanatory variable and GTI as a moderating variable. We utilized balanced panel data over 26 years (1995–2020) for 37 OECD countries, excluding Costa Rica owing to the absence of GTI indicators. Data were sourced from the World Development Indicators (WDI), the KOF Financial Globalization Index (KOF), and the OECD. Table 1 details the variables used in this analysis.

Table 1.

Variables List.

Variables	Description	Source
LnGHG	Total greenhouse gas emissions (kt of $C O_{2}$ equivalent)	WDI
$LnC O_{2}$	Carbon emissions (kt)	WDI
Lntourism	International tourism, number of arrivals	WDI
Lntravel	International tourism, receipts for travel items (current US$)	WDI
LnGDP	GDP per capita (current US$)	WDI
LnFDI	Foreign direct investment, net inflows (BoP, current US$)	WDI
LnEGL	Economic Globalization: Economic Globalization Index	KOF
LnREC	Renewable energy consumption (% of total final energy consumption)	WDI
LnGTI	Green technology innovation: Patents in environment-related technologies: Applicant(s)’s country(ies) of residence (Total patents)	OECD

Dependent Variable

We used total GHG emissions (LnGHG) as the dependent variable, measured in kilotonnes of $C O_{2}$ equivalent. This measure includes $C O_{2}$ from biomass burning and anthropogenic sources of $C H_{4}$ , $N_{2} O$ , and F-gases. Using LnGHG as the dependent variable provides a comprehensive view of environmental impact and ensures relevance to policymaking aimed at reducing overall emissions.

Independent Variable

We selected international tourism (number of arrivals; Lntourism) as the core explanatory variable because it directly reflects the scale of tourism activities and their ecological footprint. The number of international tourist arrivals accurately captures the influx of tourists, which is associated with increases in transportation, accommodation, and other tourism-related activities, that significantly affect GHG emissions. Additionally, using international tourism receipts (Lntravel) as a control variable provides a more comprehensive assessment of tourism’s economic impact, reflecting tourist consumption and the industry’s economic contribution (Rasool et al., 2021). Incorporating both variables into the model offers a more accurate evaluation of tourism’s overall impact on GHG emissions, thereby enhancing the analysis’s accuracy and credibility.

We incorporated green technology innovation (LnGTI) as a moderating variable in the model. Investing in green technology can transform the tourism industry and promote sustainable and eco-friendly tourism (Gavrilović & Maksimović, 2018). Understanding whether GTI influences GHG emissions through its impact on tourism is crucial for balancing economic growth with environmental sustainability.

This study includes the following control variables: GDP per capita (current US$) to measure economic growth; FDI net inflows (BoP, current US$) to capture investment levels; the KOF Economic Globalization Index to represent economic globalization; and the percentage of renewable energy consumption in total final energy consumption to account for sustainable energy use.

Variable Statistics

Table 2 presents the descriptive statistics of the variables, all transformed using natural logarithms. Except for LnGHG, $LnC O_{2}$ , Lntourism, and Lntravel, all variables have a median greater than the mean, indicating a negatively skewed distribution. Conversely, LnGDP, LnFDI, LnEGL, LnREC, and LnGTI show positive skewness. The Shapiro-Wilk test confirms that the dataset is non-normal. The coefficient of variation (CV) is highest for LnGTI, thus reflecting significant variability among OECD countries. Finally, the positive kurtosis coefficients for all variables indicate that their distributions are leptokurtic.

Table 2.

Summary Statistics.

Variable	Obs.	Mean	Median	Std. dev.	Min.	Max.	CV	Skewness	Kurtosis	S-W	Prob.
LnGHG	962	11.732	11.393	1.528	7.952	15.73	0.13	0.064	3.059	0.974	.000
$LnC O_{2}$	962	11.446	11.103	1.576	7.396	15.57	0.138	0.088	3.078	0.976	.000
Lntourism	962	16.112	15.937	1.46	12.26	19.2	0.091	0.069	2.395	0.986	.000
Lntravel	962	22.589	22.642	1.378	16.81	26.03	0.061	−0.3	3.2	0.993	.000
LnGDP	962	10.057	10.12	0.838	7.681	11.73	0.083	−0.7	2.993	0.956	.000
LnFDI	962	22.809	22.99	1.84	14.51	27.32	0.081	−0.49	3.583	0.986	.000
LnEGL	962	4.261	4.299	0.188	3.398	4.533	0.044	−1.34	4.982	0.894	.000
LnREC	962	2.513	2.601	0.986	−0.813	4.395	0.392	−0.617	3.174	0.971	.000
LnGTI	962	3.977	4.041	2.236	−1.504	8.967	0.562	−0.03	2.397	0.989	.000

Model Specification and Theoretical Framework

Econometric Model

This study examines the nonlinear impact of tourism on GHG emissions in OECD countries. The number of tourist arrivals served as the core explanatory variable, while tourism receipts, GDP per capita, FDI, economic globalization, and renewable energy consumption were used as control variables. GTI was employed as the moderating variable. The basic econometric model is presented in Equation 1. To explore the heterogeneous impact of tourism on GHG emissions, Model (2) utilized MMQR. Additionally, the impact of GTI on the relationship between tourism and GHG emissions was analyzed by including GTI in the basic econometric model, as shown in Equation 3. Equation 4 represents the threshold effect model, with GTI as the threshold variable, where “ $ω_{1}$ ” denotes the estimated threshold value, “ $μ_{i}$ ” represents country-fixed effects, and “ $γ_{i}$ ” represents year-fixed effects.

\begin{matrix} LnGH G_{it} = α_{i} + β_{1} Lntouris m_{it} + β_{2} Lntouris {m^{2}}_{it} \\ + β_{3} Lntrave l_{it} + β_{4} Lntrave {l^{2}}_{it} \\ + β_{5} LnGD P_{it} + β_{6} LnFD I_{it} + β_{7} LnEG L_{it} \\ + β_{8} LnRE C_{it} + ε_{it} \end{matrix}

(1)

\begin{matrix} Q_{LnGH G_{it}} (τ_{j} | α_{i}, X_{it}) = α_{i} + β_{1 τ} Lntouris m_{it} \\ + β_{2 τ} Lntouris {m^{2}}_{it} + β_{3 τ} Lntrave l_{it} \\ + β_{4 τ} Lntrave {l^{2}}_{it} + β_{5 τ} LnGD P_{it} \\ + β_{6 τ} LnFD I_{it} + β_{7 τ} LnEG L_{it} \\ + β_{8 τ} LnRE C_{it} \end{matrix}

(2)

\begin{array}{l} {LnGHG}_{it} = α_{i} + β_{1} {Lntourism}_{it} + β_{2} {Lntourism}^{2}_{it} \\ + β_{3} {Lntourism}_{it} \times {LnGTI}_{it} + β_{4} {Lntourism}^{2}_{it} \\ \times {LnGTI}_{it} + β_{5} {LnGTI}_{it} + β_{2} {Lntravel}_{it} \\ + β_{6} {Lntravel}^{2}_{it} + β_{3} {LnGDP}_{it} + β_{4} {LnFDI}_{it} \\ + β_{5} L n {EGL}_{it} + β_{5} {LnREC}_{it} + ε_{it} \end{array}

(3)

\begin{matrix} LnGH G_{it} = α_{i} + β_{1} Lntouris m_{it} (β_{2} LnGT I_{it} \leq ω_{1}) \\ + β_{2} Lntouris m_{it} (LnGT I_{it} > ω_{1}) + β_{3} Lntrave l_{it} \\ + β_{4} Lntrave {l^{2}}_{it} + β_{5} LnGD P_{it} + β_{6} LnFD I_{it} \\ + β_{7} LnEG L_{it} + β_{8} LnRE C_{it} + μ_{i} + γ_{i} + ε_{it} \end{matrix}

(4)

Theoretical Framework

This study’s theoretical framework is based on various econometric tests, as shown in Figure 1, which outlines the analytical process. First, we introduced the research background and hypotheses. We then conducted regression pre-checks, including slope homogeneity, cross-sectional dependence (CD), unit roots, and cointegration. Subsequently, we performed regression analysis using the FMOLS, DOLS, CCR, and FGLS methods. Additionally, we conducted an asymmetry test using MMQR and performed a robustness check with an alternative dependent variable. Interaction terms are added to analyze the moderating effect of GTI, and the panel threshold model is used for retesting. Finally, we draw conclusions based on the research findings and propose policy recommendations.

Figure 1.

Conceptual framework of this study.

Regression Precheck

Before conducting the overall regression analysis, we performed several diagnostic checks to ensure data and model reliability: variance inflation factor (VIF) checks for multicollinearity issues and prevent inaccurate coefficient estimates, slope homogeneity tests for sample heterogeneity, CD tests for cross-dependence, and. panel unit root and cointegration tests to confirm data stability and long-term relationships between the variables.

First, the model was assessed for multicollinearity using VIF. Table 3 presents a mean VIF of 4.46, indicating that the multicollinearity level is low and acceptable (VIF < 5). Therefore, it does not pose a significant problem for estimation with these variables (Haldar et al., 2023).

Table 3.

Multicollinearity Check.

Variable	VIF	1/VIF
Lntourism	3.24	0.31
Lntravel	4.46	0.22
LnGDP	3.29	0.3
LnFDI	2.08	0.48
LnEGL	1.82	0.55
LnREC	1.22	0.82
LnGTI	2.00	0.5
Mean VIF	2.59

Table 4 presents the results of the slope homogeneity test (Blomquist & Westerlund, 2013; Pesaran & Yamagata, 2008) and the CD test results (Pesaran (2004) CD test, Pesaran (2004) scaled LM test, Breusch and Pagan (1980) LM test, and Frees (1995). The null hypothesis of slope homogeneity assumes that all slope coefficients are the same to determine whether the slopes are homogeneous. At the 1% significance level, this analysis led us to accept the alternative hypothesis, indicating that the coefficients of the model were not consistent and that the slopes varied across countries, suggesting the presence of heterogeneity in the model. The CD test results for CD indicate that the null hypothesis of no CD is valid across the entire sample.

Table 4.

Slope Homogeneity and Cross-sectional Dependence (CD).

Dependent variable: LnGHG	Slope homogeneity		Cross-sectional dependence
Dependent variable: LnGHG	Test	Statistic	Test	Statistic
Pesaran and Yamagata (2008)	$\tilde{Δ}$ test	21.537***	Pesaran CD test	3.127***
Pesaran and Yamagata (2008)	$\tilde{Δ}$ adj test	25.884***	Pesaran scaled LM test	3.837***
Blomquist and Westerlund (2013)	$\tilde{Δ}$ test	18.613***	Breusch-Pagan LM test	6,060***
Blomquist and Westerlund (2013)	$\tilde{Δ}$ adj test	22.37***	Frees test	9.799***

Note.***, **, and * are significant at the 1%, 5%, and 10% levels, respectively.

After confirming CD in the panel data variables, we employed the unit root test method to investigate whether a unit root existed in the panel data variables. The CIPS method effectively addresses heterogeneity and CD in panel data. The cross-sectional Augmented Dickey-Fuller (CADF) test, an extension of the traditional Dickey-Fuller unit root test, is designed for panel data analysis with CD. Table 5 presents the CIPS panel unit root test results: some variables are stationary at level (I(0)), while others are stationary at the first difference (I(1)). This mixed integration necessitates using Pedroni’s (2004) cointegration method to avoid potential biases in model building and estimation.

Table 5.

Unit Root Tests.

Variable	Level		First difference		Order
Variable	Intercept	Intercept and trend	Intercept	Intercept and trend	Order
CIPS
LnGHG	−2.519	−1.595	−5.115***	−4.918***	I(1)
$LnC O_{2}$	−2.413	−1.655	−5.045***	−4.833***	I(1)
Lntourism	−1.943	−1.917	−3.979***	−3.881***	I(1)
Lntravel	−2.308	−2.208**	−4.273***	−4.3***	I(0)
LnGDP	−2.265	−1.984	−4.051***	−3.91***	I(1)
LnFDI	−3.808***	−3.566***	−5.949***	−5.821***	I(0)
LnEGL	−2.797***	−2.292**	−5.124***	−4.993***	I(0)
LnREC	−2.812***	−2.49***	−5.339***	−5.122***	I(0)
LnGTI	−3.735***	−3.469***	−5.818***	−5.673***
CADF
LnGHG	−1.595	−2.519*	−3.677***	−3.798***	I(0)
$LnC O_{2}$	−1.655	−2.413	−3.639***	−3.766***	I(1)
Lntourism	−1.917	−1.943	−3.007***	−3.094***	I(0)
Lntravel	−2.208***	−2.308	−2.966***	−3.077***	I(0)
LnGDP	−1.984*	−2.265	−3.373***	−3.417***	I(0)
LnFDI	−3.566***	−3.808***	−4.394***	−4.41***	I(0)
LnEGL	−2.292***	−2.797***	−3.598***	−3.858***	I(0)
LnREC	−2.49***	−2.812***	−3.815***	−4.093***	I(0)
LnGTI	−3.469***	−3.735***	−4.162***	−4.292***	I(0)

Note.***, **, and * are significant at the 1%, 5%, and 10%, respectively.

Pedroni’s (2004) panel cointegration test effectively addresses heterogeneity in panel data, allowing for the testing of cointegration among non-stationary variables while considering individual differences. The results presented in Table 6 are significant at the 1% level, indicating a strong cointegration relationship among the variables.

Table 6.

Cointegration Testing.

Test		Within dimension		Between dimension
		Statistic	p	Statistic	p
Dependent variable: LnGHG	$ν$ -statistic	−10.083	.000
	$ρ$ -statistic	7.247	.000	9.336	.000
	PP-statistic	−6.02	.000	−6.239	.000
	ADF-statistic	−6.43	.000	−5.491	.000

Note. All test statistics are typically distributed under a null hypothesis of no cointegration.

Benchmark Regression

FMOLS, DOLS, CCR and FGLS Regression

This study employs FMOLS, DOLS, and CCR methods to assess long-term relationships between variables and utilizes the FGLS model to address potential heteroscedasticity and autocorrelation issues. FMOLS, an enhancement of the ordinary least squares (OLS), corrects endogeneity by incorporating lagged values of explanatory variables and error terms, thus providing consistent and efficient estimations (Phillips & Hansen, 1990). DOLS addresses endogeneity by incorporating the first-difference terms (leads and lags) of explanatory variables in the regression equation (Phillips & Loretan, 1991; Saikkonen, 1991; Stock & Watson, 1993). Conversely, the CCR method employs a stationary transformation of the ( $y_{i}$ , ${x'}_{i}$ ) data to derive least square estimates, addressing long-term dependence and asymptotic bias arising from contemporaneous correlations between errors and stochastic regressors (Al-mulali et al., 2013; J. Y. Park, 1992). In the data analysis, different variance and correlation structures among the data may lead to suboptimal fitting of the OLS model. The FGLS model improves upon OLS by estimating the covariance matrix of error terms, effectively addressing heteroscedasticity and autocorrelation.

The results in Table 7 exhibit an inverted U-shaped relationship between the number of tourists and GHG emissions. Initially, an increase in inbound tourists leads to higher demand for energy-intensive activities, such as transportation (e.g., flights, cars), accommodation (e.g., hotels, resorts), and other related services (e.g., catering and entertainment facilities; Balsalobre-Lorente & Leitão, 2020; Razzaq et al., 2020). Additionally, rapid tourism development often outpaces environmental regulations, leading to increased GHG emissions (Zeng et al., 2021). However, as the industry matures, it adopts more efficient and eco-friendly technologies (Majid et al., 2023; Sustacha et al., 2023), and governments implement stricter environmental measures (Purwono et al., 2024; Raza et al., 2021). This results in decreased emissions per tourist and a decline in total emissions (Zhang et al., 2023). Conversely, the relationship between tourism receipts and GHG emissions follows a U-shape. Initially, as income from tourism receipts increases, the scale effect becomes evident, improving resource utilization efficiency and reducing GHG emissions (Zhang et al., 2023). However, beyond a certain level, consumption patterns shift toward high-energy and high-emission services (Pablo-Romero et al., 2023). As tourism consumption rises further, exceeding the ecological capacity of the region.

Table 7.

FGLS, FMOLS, DOLS and CCR Regression Results.

Variable	FGLS		FMOLS		DOLS		CCR
Lntourism	2.173*** (0.427)	3.325*** (0.285)	4.423*** (1.291)	4.91*** (1.002)	2.153*** (0.44)	3.723*** (1.291)	6.933*** (2.264)	5.126*** (1.057)
$Lntouris m^{2}$	−0.06*** (0.009)	−0.1*** (0.009)	−0.13*** (0.067)	−0.15*** (0.031)	−0.06*** (0.014)	−0.11*** (0.04)	−0.2*** (0.07)	−0.15*** (0.033)
Lntravel	−2.18*** (0.55)	−2.94*** (0.352)	−5.87* (2.775)	−5.43*** (1.249)	−2.08*** (0.578)	−3.12* (1.658)	−7.53*** (2.984)	−5.89*** (1.356)
$Lntrave l^{2}$	0.064*** (0.012)	0.079*** (0.008)	0.152** (0.063)	0.134*** (0.028)	0.062*** (0.013)	0.082** (0.037)	0.189*** (0.067)	0.139*** (0.03)
LnGDP		−0.01 (0.039)		0.063 (0.136)		0.014 (0.181)		−0.045 (0.139)
LnFDI		0.144*** (0.015)		0.218*** (0.051)		0.207*** (0.079)		0.53*** (0.061)
LnEGL		−3.36*** (0.137)		−4.48*** (0.483)		−3.71*** (0.644)		−4.54*** (0.514)
LnREC		−0.29*** (0.021)		−0.24*** (0.073)		−0.244** (0.095)		−0.07 (0.076)
Cons.	8.855*** (5.45)	21.78*** (3.503)	29.39 (27.53)	39.46*** (12.436)	7.805 (5.69)5	20.702 (16.507)	26.39*** (28.428)	39.05*** (13.117)
$Wald χ^{2}$	1,764***	5,842***
$R^{2}$			.197	.66	.65	.879	.452	.711
Validation of the EKC hypothesis for LnTourism and LnGHG using the U-test
Lower bound	12.26		12.26		12.26		12.26
Upper bound	19.2		19.2		19.2		19.2
Turning point	16.93		16.84		16.93		16.66
Overall test	7.67***		3.36***		1.94**		3.69***
Structure	Inverted U shape		Inverted U shape		Inverted U shape		Inverted U shape
Validation of the EKC hypothesis for Lntravel and LnGHG using the U-test
Lower bound	16.81		16.81		16.81		16.81
Upper bound	26.03		26.03		26.03		26.03
Turning point	18.66		20.33		18.77		21.16
Overall test	3.22***		2.95***		3.28***		3.43***
Structure	U shape		U shape		U shape		U shape

Note.***, **, and * mean significance at the level of 1%, 5%, and 10%, respectively.

The coefficient for economic development is not significant, indicating an unclear relationship with GHG emissions. Therefore, we used quantile regression. Our analysis shows that increased FDI leads to higher GHG emissions, thus supporting the pollution halo hypothesis. FDI introduces high-quality capital and expands industrial infrastructure and production, thereby stimulating economic growth and energy demand. This increase in fossil fuel consumption increases GHG emissions (Omri & Kahouli, 2014; Tariq et al., 2023). Therefore, while FDI promotes economic growth, it also stresses the environment, highlighting the need for energy structure optimization and enhanced environmental management. Conversely, globalization and renewable energy consumption have reduced GHG emissions. Globalization, a key driver of economic growth, enhances international market access and service availability, thereby promoting environmental improvement (Balsalobre-Lorente & Leitão, 2020). It facilitates technology and knowledge transfer, leading to more efficient and eco-friendly practices, thus reducing GHG emissions (Elfaki & Ahmed, 2024; Guan et al., 2022). Renewable energy consumption significantly contributes to mitigating environmental degradation by providing clean energy, reducing dependence on fossil fuels, and directly lowering GHG emissions (Cao et al., 2022; Naseem & Guang Ji, 2021; Shao et al., 2021).

While traditional regression models with a quadratic term can identify U-shaped or inverted U-shaped relationships, they have limitations (Lind & Mehlum, 2010). If the true relationship is convex but monotonic across the data range, the quadratic term might erroneously indicate an extremum point (Lind & Mehlum, 2010). Additionally, the strong correlation between the quadratic and linear terms can lead to misleading results when testing the quadratic term’s significance, leading to incorrect inferences of U-shaped or inverted U-shaped relationships (Sarkodie & Ozturk, 2020). To address these issues and examine the inverted U-shaped relationship between trade openness and GTI, this study employs the U-test algorithm (Sarkodie & Ozturk, 2020; Sarkodie & Strezov, 2018; Trinugroho et al., 2021) following the benchmark regression. The results of the U-test show that a statistically significant extreme point (at the 10% level) within the data interval rejects the null hypothesis, confirming an inverted U-shaped relationship between the number of tourists and GHG emissions, as well and a U-shaped relationship between tourism receipts and GHG emissions.

Asymmetric Analysis

Panel quantile regression models offer a more detailed analysis than traditional regression models by examining relationships across different conditional quantiles of the dependent variable. This approach reveals the conditional probability distribution of the dependent variable and provides specific insights into the relationship between independent and dependent variables. Given that the Shapiro-Wilk test indicates non-normal data distribution, this study employs the MMQR method proposed by Machado and Santos Silva (2019) for asymmetric analysis of the relationship between tourism development and environmental pollution. MMQR is advantageous because it distinguishes individual effects within panel data, thereby providing detailed insights into how regression coefficients affect the entire conditional distribution (Padhan et al., 2020). It addresses issues related to location, individual, and time factors, as well as endogeneity (Sultana et al., 2023).

Table 8 presents the MMQR model results, indicating that the relationship between tourist numbers and GHG emissions is inverted U-shaped at all quantiles, whereas tourism receipts and GHG emissions follow a U-shape. Economic growth exhibits asymmetric effects on GHG emissions, with the impact varying across quantiles. Specifically, the coefficient for economic growth changes from negative to positive as the quantile increases. At the 15% to 30% quantiles, economic growth shows a significant negative correlation with GHG emissions, while at the 75% and 90% quantiles, it shows a significant positive correlation. This suggests that countries at lower quantiles may have more effectively implemented environmental policies and technologies, while those at higher quantiles experience less effective environmental measures despite advanced technologies and financial support (Yu et al., 2023). FDI increases GHG emissions across all quantiles, with a more significant impact on emissions at higher quantiles (in countries or regions with higher emission levels). By contrast, both economic globalization and renewable energy consumption have a significant negative impact on GHG emissions across all quantiles, indicating that as the level of economic globalization and renewable energy consumption increases, GHG emissions decrease significantly. The effects of economic globalization and renewable energy consumption are more pronounced in economies with lower GHG emissions, suggesting that these factors directly and effectively reduces emissions. However, in economies with higher GHG emissions, although economic globalization and renewable energy consumption still have a negative impact on emissions, their marginal effects decrease as emission levels increase, leading to a smaller overall impact.

Table 8.

Method of Moments Quantile Regression (MMQR).

Variable	Location	Scale	Quantiles
			$Q_{0.15}$	$Q_{0.30}$	$Q_{0.45}$	$Q_{0.60}$	$Q_{0.75}$	$Q_{0.90}$
Lntourism	3.325*** (0.303)	0.225 (0.188)	3.042*** (0.321)	3.179*** (0.289)	3.251*** (0.289)	3.368*** (0.316)	3.534*** (0.394)	3.709*** (0.505)
$Lntouris m^{2}$	−0.098*** (0.009)	−0.008 (0.006)	−0.089*** (0.01)	−0.09*** (0.009)	−0.096*** (0.009)	−0.1*** (0.01)	−0.105*** (0.012)	−0.11*** (0.016)
Lntravel	−2.937*** (0.31)	0.002 (0.193)	−2.94*** (0.328)	−2.94*** (0.296)	−2.938*** (0.297)	−2.94*** (0.324)	−2.935*** (0.404)	−2.93*** (0.518)
$Lntrave l^{2}$	0.079*** (0.007)	0.0001 (0.004)	0.079*** (0.007)	0.079*** (0.007)	0.079*** (0.007)	0.079*** (0.007)	0.079*** (0.009)	0.079*** (0.012)
LnGDP	−0.001 (0.045)	0.14*** (0.028)	−0.186*** (0.048)	−0.1** (0.043)	−0.056 (0.043)	0.017 (0.048)	0.12** (0.059)	0.229*** (0.075)
LnFDI	0.144*** (0.016)	0.002 (0.01)	0.142*** (0.017)	0.143*** (0.015)	0.143*** (0.015)	0.144*** (0.017)	0.145*** (0.021)	0.146*** (0.027)
LnEGL	−3.362*** (0.163)	−0.6*** (0.101)	−2.549*** (0.177)	−2.94*** (0.154)	−3.15*** (0.156)	−3.49*** (0.175)	−3.963*** (0.213)	−4.47*** (0.273)
LnREC	−0.288*** (0.022)	0.04*** (0.014)	−0.34*** (0.023)	−0.31*** (0.021)	−0.3*** (0.021)	−0.28*** (0.023)	−0.25*** (0.029)	−0.22*** (0.037)
Cons.	21.78*** (3.42)	−0.065 (2.124)	21.86*** (3.618)	21.8*** (3.261)	21.8*** (3.269)	21.8*** (3.57)	21.72*** (4.451)	21.67*** (5.709)
Obs.	962	962	962	962	962	962	962	962

Note.***, **, and * mean significance at the level of 1%, 5%, and 10%, respectively.

Robustness Test

We replaced GHG emissions with $C O_{2}$ emissions for robustness checks. The results, presented in Tables 9 and 10, include FGLS, FMOLS, DOLS, CCR, and MMQR estimations. The coefficients of Lntourism and $Lntouris m^{2}$ consistently show an inverted U-shaped relationship, similar to the baseline regression. Additionally, Lntravel and $Lntouris m^{2}$ exhibit a significant U-shaped relationship. Economic globalization and renewable energy utilization both reduce $C O_{2}$ emissions. These results confirm the robustness of our baseline results.

Table 9.

Robustness Test (Y = $LnC O_{2}$ ; FGLS, FMOLS, DOLS, and CCR).

Variable	FGLS		FMOLS		DOLS		CCR
Lntourism	2.481** (0.429)	3.51*** (0.298)	4.737** (2.103)	5.322** (1.105)	2.468*** (0.441)	3.96*** (1.434)	4.078*** (0.444)	5.144*** (1.19)
$Lntouris m^{2}$	−0.07** (0.013)	−0.1*** (0.009)	−0.14** (0.066)	−0.16*** (0.034)	−0.069*** (0.014)	−0.12*** (0.044)	−0.12*** (0.014)	−0.15*** (0.037)
Lntravel	−2.188** (0.552)	−3.05*** (0.367)	−5.691** (2.728)	−5.71*** (1.376)	−2.063*** (0.58)	−3.21*** (1.841)	−3.36*** (0.53)	−5.2*** (1.517)
$Lntrave l^{2}$	0.065** (0.012)	0.081*** (0.008)	0.148** (0.062)	0.139*** (0.031)	0.062*** (0.013)	0.084*** (0.201)	0.087*** (0.012)	0.127*** (0.034)
LnGDP		0.046 (0.04)		0.135 (0.15)		0.077 (0.201)	0.189*** (0.068)	0.097*** (0.161)
LnFDI		0.128 (0.015)		0.196*** (0.057)		0.183*** (0.088)	0.143*** (0.023)	0.24*** (0.064)
LnEGL		−3.2*** (0.143)		−4.37*** (0.151)		−3.55*** (0.716)	−3.83*** (0.247)	−4.19*** (0.554)
LnREC		−0.35*** (0.022)		−0.3*** (0.08)		−0.31*** (0.106)	−0.31*** (0.033)	−0.25*** (0.085)
Cons.	5.732 (5.47)	20.29 (3.656)	24.18 (27.06)	38.1*** (13.71)	4.349*** (5.716)	18.33*** (18.33)	20.81*** (5.509)	32.67*** (14.96)
$Wald χ^{2}$ / $R^{2}$	1,917	5,684	0.227	0.622	0.668	0.874	0.517	0.747
Obs.	962	962	962	962	962	962	962	962

Note.***, **, and * mean significance at the level of 1%, 5%, and 10%, respectively

Table 10.

Robustness Test (Y = $LnC O_{2}$ ; MMQR).

Variable	Location	Scale	Quantiles
			$Q_{0.15}$	$Q_{0.30}$	$Q_{0.45}$	$Q_{0.60}$	$Q_{0.75}$	$Q_{0.90}$
Lntourism	3.51*** (0.316)	0.629*** (0.212)	2.74*** (0.293)	3.029*** (0.275)	3.3*** (0.287)	3.567*** (0.327)	4.078*** (0.444)	4.584*** (0.586)
$Lntouris m^{2}$	−0.103*** (0.01)	−0.02*** (0.007)	−0.08*** (0.009)	−0.09*** (0.008)	−0.1*** (0.009)	−0.1*** (0.01)	−0.12*** (0.014)	−0.14*** (0.018)
Lntravel	−3.05*** (0.377)	−0.344 (0.253)	−2.63*** (0.351)	−2.79*** (0.328)	−2.93*** (0.343)	−3.08*** (0.39)	−3.36*** (0.53)	−3.64*** (0.701)
$Lntrave l^{2}$	0.081*** (0.009)	0.0007 (0.006)	0.072*** (0.008)	0.076*** (0.007)	0.079*** (0.008)	0.082*** (0.009)	0.087*** (0.012)	0.093*** (0.016)
LnGDP	0.046 (0.048)	0.158*** (0.032)	−0.147*** (0.045)	−0.074* (0.042)	−0.008 (0.044)	0.061 (0.05)	0.189*** (0.068)	0.316*** (0.089)
LnFDI	0.128*** (0.017)	0.017 (0.011)	0.108*** (0.015)	0.116*** (0.014)	0.123*** (0.015)	0.13*** (0.017)	0.143*** (0.023)	0.157*** (0.031)
LnEGL	−3.196*** (0.173)	−0.7*** (0.016)	−2.335*** (0.161)	−2.66*** (0.151)	−2.96*** (0.159)	−3.26*** (0.182)	−3.83*** (0.247)	−4.4*** (0.323)
LnREC	−0.354*** (0.024)	0.051*** (0.016)	−0.416*** (0.022)	−0.39*** (0.021)	−0.37*** (0.022)	−0.35*** (0.024)	−0.31*** (0.033)	−0.27*** (0.044)
Cons.	20.291*** (3.93)	0.572 (2.638)	19.59*** (3.649)	19.85*** (3.417)	20.1*** (3.571)	20.34*** (4.05)	20.81*** (5.509)	21.27*** (7.295)
Obs.	962	962	962	962	962	962	962	962

Note.***, **, and * mean significance at the level of 1%, 5%, and 10%, respectively.

Further Analysis

Moderating Effect of GTI

In today’s globalized world, the rapidly growing tourism industry is closely connected to climate change (Nazneen et al., 2023). GTI has emerged as an effective tool for reducing environmental impacts. The analysis indicates that the relationship between the number of tourists and GHG emissions follows an inverted U-shape. Further research is warranted to determine how to drive low-carbon effects in the tourism industry and achieve goals such as eco-tourism. GTI plays a crucial role in promoting economic growth and environmental protection. Investing in green technology within the tourism sector can contribute to developing sustainable, smart, and green tourism. Therefore, investigating the impacts of GTI on the tourism industry and its role in regulating GHG emissions is crucial. This study uses the number of tourists as a proxy for tourism development and incorporates GTI into the research framework to explore its moderating impacts of tourism on GHG emissions.

Based on Haans et al. (2016) and incorporating Model 3, when $β_{1} β_{4} - β_{2} β_{3} > 0$ , the moderating variable shifts the curve’s inflection point to the right; however, it shifts to the left. If $β_{4}$ is significantly positive, this indicates that the moderating effect makes the original U-shaped curve steeper and the inverted U-shaped curve flatter. Conversely, if $β_{4}$ is significantly negative, the moderating effect makes the U-shaped curve flatter and the inverted U-shaped curve steeper. The results in Table 11 show that the coefficient of $Lntouris m^{2}$ × LnGTI is significantly positive, indicating that GTI exerts a significant positive moderating effect on the inverted U-shaped relationship between tourism development and GHG emissions. Specifically, as tourism development reaches a certain level, GHG emissions first increase and then decrease. GTI can significantly enhance this trend, thereby decreasing GHG emissions more significantly after reaching a turning point during tourism development. Taking the FGLS results as an example, $β_{1} β_{4} - β_{2} β_{3} > 0$ (0.0046), indicating that the moderating effect of GTI shifts the inflection point of the original inverted U-shaped curve to the right; the coefficient of $Lntouris m^{2}$ × LnGTI is positive (>0), indicating that the moderating effect of GTI makes the inverted U-shaped curve flatter. Although the GTI delayed the peak impact of tourism on GHG emissions, it also slowed the growth of these emissions.

Table 11.

Moderating Effect Testing Result.

Variable	FGLS	FMOLS	DOLS	CCR
Lntourism	4.125*** (0.404)	5.722*** (1.381)	4.169*** (0.41)	5.524*** (1.406)
$Lntouris m^{2}$	−0.131**** (0.013)	−0.178*** (0.044)	−0.133*** (0.013)	−0.174*** (0.045)
Lntourism × LnGTI	−0.437*** (0.9)	−0.678** (0.308)	−0.421*** (0.092)	−0.666** (0.314)
$Lntouris m^{2} \times$ LnGTI	0.015*** (0.003)	0.022** (0.009)	0.014*** (0.003)	0.022** (0.01)
LnGTI	3.383*** (0.731)	5.352** (2.507)	3.212*** (0.753)	5.163** (2.568)
Lntravel	0.033 (0.375)	−1.02 (1.305)	0.273 (0.402)	0.413 (1.365)
$Lntrave l^{2}$	0.01 (0.009)	0.033 (0.03)	0.004 (0.01)	−0.002 (0.031)
LnGDP	−0.242*** (0.037)	−0.163 (0.128)	−0.249*** (0.038)	−0.279** (0.129)
LnFDI	0.132*** (0.013)	0.205*** (0.043)	0.153*** (0.014)	0.456*** (0.05)
LnEGL	−3.086*** (0.121)	−4.048*** (0.415)	−3.144*** (0.124)	−4.096*** (0.435)
LnREC	−0.342*** (0.018)	−0.304*** (0.062)	−0.337*** (0.018)	−0.165** (0.064)
Cons.	−13.61*** (4.956)	−13.124 (17.051)	−16.6*** (5.18)	−30.2* (17.63)
$Wald χ^{2}$ / $R^{2}$	8,165	0.791	0.899	0.841

Note.***, **, and * mean significance at the level of 1%, 5%, and 10%, respectively.

Threshold Effect

Based on a previous analysis of GTI’s moderating effect on the relationship between tourism and GHG emissions, we employed Hansen’s (1999) threshold model to examine how GTI influences carbon reduction in eco-tourism.

The threshold test results presented in Table 12 indicate that the single-threshold model, with GTI as the threshold variable, exhibits a significant F-statistic, confirming the presence of a structural break in the relationship between tourism development and GHG emissions at a threshold value of 5.3091. Figure 2 visually validates this structural change, reinforcing the existence of the identified threshold within the model. Table 13 outlines the effects of tourism development on GHG emissions across different GTI levels. When LnGTI is less than or equal to the threshold value of 5.3091, tourism development has a significant positive effect on GHG emissions (coefficient = 0.026**). However, when LnGTI exceeds this threshold, the positive effect remains significant (coefficient = 0.0201*), but the reduced magnitude indicates a weaker impact. According to the threshold effect theory, surpassing this critical GTI value significantly mitigates the positive effect of tourism development on GHG emissions. This implies that higher levels of GTI investment can effectively reduce the environmental pressure caused by tourism activities, highlighting the importance of technological advancement in promoting sustainable tourism development.

Table 12.

Results of the Threshold Tests.

Threshold variable	Threshold type	Threshold estimator	95% confidence interval	F-statistic	p-Value	Critical value
Threshold variable	Threshold type	Threshold estimator	95% confidence interval	F-statistic	p-Value	10%	5%	1%
LnGTI	Single threshold	5.3091	[5.2393, 5.3526]	69.19*	.08	53.17	62.77	106.9

Note.***, **, and * mean significance at the level of 1%, 5%, and 10%, respectively.

Figure 2.

Single threshold LR graph.

Table 13.

Regression Results of the Threshold Model.

Variable	Threshold model
Lntourism (LnGTI ≤ 5.3091)	0.026** (0.012)
Lntourism (LnGTI > 5.3091)	0.0201* (0.012)
Lntravel	−0.541*** (0.082)
$Lntrave l^{2}$	0.012*** (0.002)
LnGDP	0.098*** (0.013)
LnFDI	0.006* (0.003)
LnEGL	0.14*** (0.043)
LnREC	−0.208*** (0.008)
Cons.	16.131*** (0.91)
Year/Country FE	Yes
$R^{2}$	.458
F-test	1,187***

Note.***, **, and * mean significance at the level of 1%, 5%, and 10%, respectively.

Based on the moderating and threshold effects analysis findings, GTI not only delays the peak environmental impact of tourism development on GHG emissions but also flattens the inverted U-shaped curve, signifying its role in mitigating the negative environmental consequences of tourism. Specifically, the threshold effect results indicate that when GTI surpasses a critical threshold, the positive correlation between tourism development and GHG emissions significantly weakens. This highlights the significance of substantial investment in GTI to mitigate the adverse environmental impacts associated with tourism activities effectively.

GTI proves to be a critical mechanism for reducing $C O_{2}$ emissions, conserving resources, and fostering economic growth through energy-efficient and sustainable practices (Hu et al., 2022; Yang et al., 202191). In the tourism sector, technological innovation plays a pivotal role in optimizing destination planning, accommodations, attractions, and online services, including weather and climate forecasting, economic insights, travel bookings, and digital transactions (Law et al., 2018; Razzaq et al., 2020). By adopting eco-friendly technologies and improving energy-saving and emission-reduction methods, GTI not only enhances the industry’s capacity to offer safer, healthier, and more comfortable travel experiences but also significantly reduces the environmental footprint of tourism. This progress plays a vital role in promoting the sustainable development of the tourism industry by harmonizing economic growth with environmental conservation.

Conclusion

This study utilizes panel data from OECD countries spanning from 1995 to 2020 to explore the nonlinear relationship between tourism development and GHG emissions, with a particular focus on ecotourism and environmental protection. Additionally, it examines the moderating and threshold effects of GTI on this relationship. The findings reveal that tourism development significantly impacts GHG emissions. Specifically, the number of tourists exhibits an inverted U-shaped relationship with GHG emissions, while tourism revenue shows a U-shaped relationship.

FDI promotes GHG emissions, whereas economic growth has an asymmetric effect on GHG emissions. Furthermore, economic globalization and renewable energy consumption significantly reduce GHG emissions. The moderating effect of GTI flattens the inverted U-shaped curve, somewhat alleviating the negative environmental impact of tourism development. The threshold effect analysis confirms that when GTI surpasses a certain threshold, the positive impact of tourism development on GHG emissions is significantly reduced. This finding aligns with the literature that emphasizes the importance of technological innovation in mitigating environmental impacts (Erdoğan et al., 2022; Kumail et al., 2024).

Based on the research findings, we propose several policy recommendations to promote sustainable tourism development and mitigate environmental impacts. First, policymakers should focus on promoting sustainable tourism development. This can be achieved by formulating green tourism policies that encourage low-carbon tourism projects and establishing green certification standards. Tourism businesses should be incentivized to adopt environmentally friendly practices through various means, such as tax breaks or preferential treatment in licensing. Additionally, promoting eco-tourism projects can help protect the natural environment and raise public awareness of environmental conservation, thereby enhancing overall environmental protection. Second, efforts should be made to encourage green consumption in tourism. The tourism industry should actively promote eco-friendly options such as sustainable accommodations, low-carbon transportation, and eco-tourism initiatives. Governments can play a crucial role by implementing tax incentives for enterprises investing in green tourism facilities and environmental protection technologies. These measures can help guide tourism revenue toward environmentally friendly development and encourage tourists to make more sustainable choices. Third, there should be a strong emphasis on enhancing GTI in tourism. We recommend supporting and promoting advanced environmental technologies, such as renewable energy systems and pollution control mechanisms, by integrating them into tourism facilities and services. Investments should focus on green infrastructure, including energy-efficient buildings and low-emission transportation systems. Encouraging research and application of green technologies is crucial; this can be achieved by providing financial support and technical guidance to help tourism enterprises adopt current environmental innovations (Kumail et al., 2024; Lv et al., 2023). Furthermore, collaborating with international organizations and other countries to share green technologies and environmental practices will collectively reduce the tourism industry’s negative environmental impact and accelerate the adoption of sustainable practices globally.

This study has several limitations that provide opportunities for future research. First, the analysis is constrained by data availability, as it utilizes data only up to 2020. The post-2020 period, characterized by significant global events such as the COVID-19 pandemic and its aftermath, may have altered the dynamics between GTI, tourism development, and environmental impacts. Incorporating more recent data in future studies could provide a clearer understanding of these evolving relationships.

Second, the study focuses exclusively on OECD countries, which are predominantly developed economies, thereby excluding developing countries. This limits the generalizability of the findings to a global context. Future research should broaden the scope to include both developed and developing countries, enabling a comparative analysis that reflects diverse economic structures, policy priorities, and environmental challenges.

Third, while the econometric approach employed in this study yields robust results, it primarily relies on traditional quantitative methods. Future research could explore applying advanced econometric techniques, such as machine learning algorithms and nonparametric methods, to capture nonlinear and complex relationships.

Finally, the study does not fully account for emerging trends, such as shifting consumer preferences toward sustainable tourism or advancements in GTI. Future research could benefit from integrating these dynamic factors into the analysis, which may enhance the accuracy of predictions and provide valuable insights for developing more targeted and effective ecotourism strategies.

Future research will build on the current findings by addressing these limitations, aiming to provide deeper, more globally relevant, and practical insights into the relationships among tourism development, GTI, and environmental sustainability.

Footnotes

ORCID iDs

Xuan Zhou

Chang Hwan Choi

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

Abid

Mehmood

Tariq

Haq

Z. U.

(2022). The effect of technological innovation, FDI, and financial development on CO2 emission: Evidence from the G8 countries. Environmental Science and Pollution Research, 29(8), 11654–11662.

Adedoyin

F. F.

Bekun

F. V.

(2020). Modelling the interaction between tourism, energy consumption, pollutant emissions and urbanization: Renewed evidence from panel VAR. Environmental Science and Pollution Research, 27(31), 38881–38900. https://doi.org/10.1007/s11356-020-09869-9

Ahmad

Zhu

(2022). The criticality of international tourism and technological innovation for carbon neutrality across regional development levels. Technological Forecasting and Social Change, 182, 121848. https://doi.org/10.1016/j.techfore.2022.121848

Al-mulali

Lee

J. Y.

Hakim Mohammed

Sheau-Ting

(2013). Examining the link between energy consumption, carbon dioxide emission, and economic growth in Latin America and the Caribbean. Renewable and Sustainable Energy Reviews, 26, 42–48. https://doi.org/10.1016/j.rser.2013.05.041

Avcı

Sarıgül

S. S.

Karataşer

Çetin

Aslan

(2024). Analysis of the relationship between tourism, green technological innovation and environmental quality in the top 15 most visited countries: Evidence from method of moments quantile regression. Clean Technologies and Environmental Policy, 26(7), 2337–2355. https://doi.org/10.1007/s10098-023-02708-8

Balsalobre-Lorente

Leitão

N. C.

(2020). The role of tourism, trade, renewable energy use and carbon dioxide emissions on economic growth: Evidence of tourism-led growth hypothesis in EU-28. Environmental Science and Pollution Research, 27(36), 45883–45896. https://doi.org/10.1007/s11356-020-10375-1

Balsalobre-Lorente

Nur

Topaloglu

E. E.

Evcimen

(2024). Assessing the impact of the economic complexity on the ecological footprint in G7 countries: Fresh evidence under human development and energy innovation processes. Gondwana Research, 127, 226–245. https://doi.org/10.1016/j.gr.2023.03.017

Blomquist

Westerlund

(2013). Testing slope homogeneity in large panels with serial correlation. Economics Letters, 121(3), 374–378. https://doi.org/10.1016/j.econlet.2013.09.012

Breusch

T. S.

Pagan

A. R.

(1980). The Lagrange multiplier test and its application to model specification in Econometrics. Review of Economic Studies, 47(1), 239–253. https://doi.org/10.2307/2297111

10.

Cao

Khan

M. K.

Rehman

Dagar

Oryani

Tanveer

(2022). Impact of globalization, institutional quality, economic growth, electricity and renewable energy consumption on carbon dioxide emission in OECD countries. Environmental Science and Pollution Research, 29(16), 24191–24202. https://doi.org/10.1007/s11356-021-17076-3

11.

Chau

K. Y.

Lin

C. H.

Tufail

Tran

T. K.

Van

Nguyen

T. T. H.

(2023). Impact of eco-innovation and sustainable tourism growth on the environmental degradation: The case of China. Economic Research-Ekonomska Istraživanja, 36(3), 2150258. https://doi.org/10.1080/1331677X.2022.2150258

12.

Chen

(2023). The impact of economic and environmental factors and tourism policies on the sustainability of tourism growth in China: Evidence using novel NARDL model. Environmental Science and Pollution Research, 30(7), 19326–19341. https://doi.org/10.1007/s11356-022-22925-w

13.

Demirtaş

(2024). Does tourism opportunity or threat to green economic growth? Evidence from the top 10 countries. Journal of Tourism and Gastronomy Studies, 12(2), 791–803. https://doi.org/10.21325/jotags.2024.1411

14.

Elfaki

K. E.

Ahmed

E. M.

(2024). Digital technology adoption and globalization innovation implications on Asian Pacific green sustainable economic growth. Journal of Open Innovation Technology Market and Complexity, 10(1), 100221. https://doi.org/10.1016/j.joitmc.2024.100221

15.

Erdoğan

Gedikli

Cevik

E. I.

Erdoğan

(2022). Eco-friendly technologies, international tourism and carbon emissions: Evidence from the most visited countries. Technological Forecasting and Social Change, 180, 121705. https://doi.org/10.1016/j.techfore.2022.121705

16.

Farah

M. F.

Shahzad

M. F.

(2020). Fast-food addiction and anti-consumption behavior: The moderating role of consumer social responsibility. International Journal of Consumer Studies, 44(4), 379–392. https://doi.org/10.1111/ijcs.12574

17.

Frees

E. W.

(1995). Assessing cross-sectional correlation in panel data. Journal of Econometrics, 69(2), 393–414. https://doi.org/10.1016/0304-4076(94)01658-M

18.

Gavrilović

Maksimović

(2018). Green innovations in the tourism sector. Strategic Management, 23(1), 36–42. https://doi.org/10.5937/StraMan1801036G

19.

Gössling

Balas

Mayer

Sun

Y. Y.

(2023). A review of tourism and climate change mitigation: The scales, scopes, stakeholders and strategies of carbon management. Tourism Management, 95, 104681. https://doi.org/10.1016/j.tourman.2022.104681

20.

Guan

Rani

Yueqiang

Ajaz

Haseki

M. I.

(2022). Impact of tourism industry, globalization, and technology innovation on ecological footprints in G-10 countries. Economic Research-Ekonomska Istraživanja, 35(1), 6688–6704. https://doi.org/10.1080/1331677X.2022.2052337

21.

Haans

R. F. J.

Pieters

(2016). Thinking about U: Theorizing and testing U- and inverted U-shaped relationships in strategy research. Strategic Management Journal, 37(7), 1177–1195. https://doi.org/10.1002/smj.2399

22.

Haldar

Sucharita

Dash

D. P.

Sethi

Chandra Padhan

(2023). The effects of ICT, electricity consumption, innovation and renewable power generation on economic growth: An income level analysis for the emerging economies. Journal of Cleaner Production, 384(15), 135607. https://doi.org/10.1016/j.jclepro.2022.135607

23.

Hansen

B. E.

(1999). Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics, 93(2), 345–368. https://doi.org/10.1016/S0304-4076(99)00025-1

24.

Jiao

Tang

Zha

(2022). How global value chain participation affects green technology innovation processes: A moderated mediation model. Technology and Society, 68, 101916. https://doi.org/10.1016/j.techsoc.2022.101916

25.

Hui

Choi

C. H.

(2024). Is carbon emission trading policy a panacea? The implications of promoting green total factor productivity. Asian-Pacific Economic Literature, 38(2), 42–55. https://doi.org/10.1111/apel.12418

26.

Kaya

M. G.

Onifade

S. T.

Akpinar

(2022). Terrorism and Tourism: An empirical exemplification of consequences of terrorist attacks on tourism revenues in Turkey. Tourism: An International Interdisciplinary Journal, 70(1), 28–42. https://doi.org/10.37741/t.70.1.2

27.

Kayani

U. N.

Sadiq

Aysan

A. F.

Haider

S. A.

Nasim

(2023). The impact of investment, economic growth, renewable energy, urbanisation, and tourism on carbon emissions: Global evidence. International Journal of Energy Economics and Policy, 13(1), 403–412. https://doi.org/10.32479/ijeep.14042

28.

Koçak

Ulucak

Z. Ş

. (2020). The impact of tourism developments on CO2 emissions: An advanced panel data estimation. Tourism Management Perspectives, 33, 100611. https://doi.org/10.1016/j.tmp.2019.100611

29.

Kumail

Mandić

Sadiq

(2024). Uncovering the interconnectedness of tourism growth, green technological advancements and climate change in prominent Asian tourism destinations. Tourism Management Perspectives, 53, 101284. https://doi.org/10.1016/j.tmp.2024.101284

30.

Law

Chan

I. C. C.

Wang

(2018). A comprehensive review of mobile technology use in hospitality and tourism. Journal of Hospitality Marketing & Management, 27(6), 626–648. https://doi.org/10.1080/19368623.2018.1423251

31.

Lee

J. W.

Brahmasrene

(2013). Investigating the influence of tourism on economic growth and carbon emissions: Evidence from panel analysis of the European Union. Tourism Management, 38, 69–76. https://doi.org/10.1016/j.tourman.2013.02.016

32.

Levinson

Taylor

M. S.

(2008). Unmasking the pollution haven effect. International Economic Review, 49(1), 223–254. https://doi.org/10.1111/j.1468-2354.2008.00478.x

33.

Lind

J. T.

Mehlum

(2010). With or without U? The appropriate test for a U-shaped relationship. Oxford Bulletin of Economics and Statistics, 72(1), 109–118. https://doi.org/10.1111/j.1468-0084.2009.00569.x

34.

Liu

Huang

(2023). Green innovation for the ecological footprints of tourism in China. Fresh evidence from ARDL approach. Economic Research-Ekonomska Istraživanja, 36(2), 2172600. https://doi.org/10.1080/1331677X.2023.2172600

35.

Wang

Cui

(2023). Influence of green technology, tourism, and inclusive financial development on ecological sustainability: Exploring the path toward green revolution. Economic Research-Ekonomska Istraživanja, 36(1), 2116349. https://doi.org/10.1080/1331677X.2022.2116349

36.

Machado

J. A. F.

Santos Silva

J. M. C.

(2019). Quantiles via moments. Journal of Econometrics, 213(1), 145–173. https://doi.org/10.1016/j.jeconom.2019.04.009

37.

Majid

G. M.

Tussyadiah

Kim

Y. R.

Pal

(2023). Intelligent automation for sustainable tourism: A systematic review. Journal of Sustainable Tourism, 31(11), 2421–2440. https://doi.org/10.1080/09669582.2023.2246681

38.

Naseem

Guang Ji

. (2021). A system-GMM approach to examine the renewable energy consumption, agriculture and economic growth’s impact on CO2 emission in the SAARC region. GeoJournal, 86(5), 2021–2033. https://doi.org/10.1007/s10708-019-10136-9

39.

Nazneen

Hong

Ud Din

Jamil

Hussain

(2023). The moderating role of technological innovation between tourism and carbon emission: Short and long-run panel analysis. Environmental Science and Pollution Research, 30(18), 53103–53114.

40.

Nepal

Indra

Irsyad

Nepal

S. K.

(2019). Tourist arrivals, energy consumption and pollutant emissions in a developing economy–implications for sustainable tourism. Tourism Management, 72, 145–154. https://doi.org/10.1016/j.tourman.2018.08.025

41.

Ochoa-Moreno

Quito

Enríquez

D. E.

Álvarez-García

(2022). Evaluation of the environmental Kuznets curve hypothesis in a tourism development context: Evidence for 15 Latin American countries. Business Strategy and the Environment, 31(5), 2143–2155. https://doi.org/10.1002/bse.3012

42.

Omri

Kahouli

(2014). Causal relationships between energy consumption, foreign direct investment and economic growth: Fresh evidence from dynamic simultaneous-equations models. Energy Policy, 67(C), 913–922. https://doi.org/10.1016/j.enpol.2013.11.067

43.

Ozturk

Sharif

Godil

D. I.

Yousuf

Tahir

(2023). The dynamic nexus between international tourism and environmental degradation in top twenty tourist destinations: New insights from quantile-on-quantile approach. Evaluation Review, 47(3), 532–562. https://doi.org/10.1177/0193841X221149809

44.

Pablo-Romero

M. P.

Sánchez-Braza

García-Soto

M. A.

(2023). The impact of tourism on energy consumption: A sectoral analysis for the most visited countries in the world. Economies, 11(10), 263. https://doi.org/10.3390/economies11100263

45.

Padhan

Padhang

P. C.

Tiwari

A. K.

Ahmed

Hammoudeh

(2020). Renewable energy consumption and robust globalization(s) in OECD countries: Do oil, carbon emissions and economic activity matter? Energy Strategy Reviews, 32, 100535. https://doi.org/10.1016/j.esr.2020.100535

46.

Paramati

S. R.

Alam

M. S.

Chen

C. F.

(2017). The effects of tourism on economic growth and CO2 emissions: A comparison between developed and developing economies. Journal of Travel Research, 56(6), 712–724. https://doi.org/10.1177/0047287516667848

47.

Park

J. Y.

(1992). Canonical cointegrating regressions. Econometrics, 60(1), 119–143. https://doi.org/10.2307/2951679

48.

Park

N. R.

Yun

H. S.

Choi

C. H.

(2024). Green trade and cultural innovation: Examining the impact on GTFP and greenhouse gas emissions in OECD countries. Sustainability, 16(19), 8339. https://doi.org/10.3390/su16198339

49.

Pedroni

(2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis, econometric theory. Cambridge University Press.

50.

Pesaran

M. H.

(2004). General diagnostic tests for cross section dependence in panels. Faculty of Economics. https://doi.org/10.17863/CAM.5113

51.

Pesaran

M. H.

Yamagata

(2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142(1), 50–93. https://doi.org/10.1016/J.JECONOM.2007.05.010

52.

Phillips

P. C. B.

Hansen

B. E.

(1990). Statistical inference in instrumental variables regression with I(1) processes. Review of Economic Studies, 57(1), 99–125. https://doi.org/10.2307/2297545

53.

Phillips

P. C. B.

Loretan

(1991). Estimating long-run economic equilibria. Review of Economic Studies, 58(3), 407–436. https://doi.org/10.2307/2298004

54.

Purwono

Sugiharti

Esquivias

M. A.

Fadliyanti

Rahmawati

Wijimulawiani

B. S.

(2024). The impact of tourism, urbanization, globalization, and renewable energy on carbon emissions: Testing the inverted N-shape environmental Kuznets curve. Social Sciences and Humanities Open, 10, 100917.

55.

Rahaman

M. A.

Hossain

M. A.

Chen

(2022). The impact of foreign direct investment, tourism, electricity consumption, and economic development on CO2 emissions in Bangladesh. Environmental Science and Pollution Research, 29(25), 37344–37358. https://doi.org/10.1007/s11356-021-18061-6

56.

Ramkissoon

(2023). Perceived social impacts of tourism and quality-of-life: A new conceptual model. Journal of Sustainable Tourism, 31(2), 442–459. https://doi.org/10.1080/09669582.2020.1858091

57.

Rasool

Maqbool

Tarique

(2021). The relationship between tourism and economic growth among BRICS countries: A panel cointegration analysis. Future Business Journal, 7(1), 2–11. https://doi.org/10.1186/s43093-020-00048-3

58.

Raza

S. A.

Qureshi

M. A.

Ahmed

Qaiser

Ali

Ahmed

(2021). Non-linear relationship between tourism, economic growth, urbanization, and environmental degradation: Evidence from smooth transition models. Environmental Science and Pollution Research, 28(2), 1426–1442.

59.

Razzaq

Sharif

Ahmad

Jermsittiparsert

(2020). Asymmetric role of tourism development and technology innovation on carbon dioxide emission reduction in the Chinese economy: Fresh insights from QARDL approach. Sustainable Development, 29(1), 176–193. https://doi.org/10.1002/sd.2139

60.

Saikkonen

(1991). Asymptotically efficient estimation of cointegration regressions. Economic Theory, 7(1), 1–21. https://www.jstor.org/stable/3532106

61.

Saqib

Dincă

(2024). Exploring the asymmetric impact of economic complexity, FDI, and green technology on carbon emissions: Policy stringency for clean-energy investing countries. Geoscience Frontiers, 15(4), 101671. https://doi.org/10.1016/j.gsf.2023.101671

62.

Sarkodie

S. A.

Ozturk

(2020). Investigating the environmental Kuznets curve hypothesis in Kenya: A multivariate analysis. Renewable and Sustainable Energy Reviews, 117, 109481. https://doi.org/10.1016/j.rser.2019.109481

63.

Sarkodie

S. A.

Strezov

(2018). Empirical study of the environmental Kuznets curve and environmental sustainability curve hypothesis for Australia, China, Ghana and USA. Journal of Cleaner Production, 201, 98–110. https://doi.org/10.1016/j.jclepro.2018.08.039

64.

Scott

Marzano

(2015). Governance of tourism in OECD countries. Tourism Recreation Research, 40(2), 181–193. https://doi.org/10.1080/02508281.2015.1041746

65.

Shahzad

M. F.

Tian

Xiao

(2019). “Drink it or not”: Soft drink anticonsumption behavior and the mediating effect of behavioral intentions. Sustainability, 11(12), 3279.

66.

Shang

Wei

Jiang

Taghizadeh-Hesary

Rasoulinezhad

(2023). Eco-tourism, climate change, and environmental policies: empirical evidence from developing economies. Humanities and Social Sciences Communications, 10(275), 275. https://doi.org/10.1057/s41599-023-01777-w

67.

Shao

Zhong

Liu

R. Y. M.

(2021). Modeling the effect of green technology innovation and renewable energy on carbon neutrality in N-11countries? Evidence from advance panel estimations. Journal of Environmental Management, 296, 113189. https://doi.org/10.1016/j.jenvman.2021.113189

68.

Stock

J. H.

Watson

M. W.

(1993). A simple estimator of cointegrating vectors in higher order integrated system. Econometrica, 61(4), 783–820. https://doi.org/10.2307/2951763

69.

Sultana

Hossain

M. S.

Voumik

L. C.

Raihan

(2023). Does globalization escalate the carbon emissions? Empirical evidence from selected next-11 countries. Energy Reports, 10, 86–98. https://doi.org/10.1016/j.egyr.2023.06.020

70.

Sun

Duru

O. A.

Razzaq

Dinca

M. S.

(2021). The asymmetric effect eco-innovation and tourism towards carbon neutrality target in Turkey. Journal of Environmental Management, 299(1), 113653. https://doi.org/10.1016/j.jenvman.2021.113653

71.

Sun

Guan

Mehmood

Yang

(2022). Asymmetric impacts of natural resources on ecological footprints: Exploring the role of economic growth, FDI and renewable energy in G-11 countries. Resources Policy, 79, 103026. https://doi.org/10.1016/j.resourpol.2022.103026

72.

Sun

Y. Y.

Cadarso

M. A.

Driml

(2020). Tourism carbon footprint inventories: A review of the environmentally extended input-output approach. Annals of Tourism Research, 82, 102928. https://doi.org/10.1016/j.annals.2020.102928

73.

Sustacha

Baños-Pino

J. F.

Del Valle

(2023). The role of technology in enhancing the tourism experience in smart destinations: A meta-analysis. Journal of Destination Marketing & Management, 30, 100817. https://doi.org/10.1016/j.jdmm.2023.100817

74.

Tariq

Sun

Fernandez-Gamiz

Mansoor

Pasha

A. A.

Ali

Khan

M. S.

(2023). Effects of globalization, foreign direct investment and economic growth on renewable electricity consumption. Heliyon, 9(3), 14635. https://doi.org/10.1016/j.heliyon.2023.e14635

75.

Thi

Tran

V. Q.

Nguyen

D. T.

(2023). The relationship between renewable energy consumption, international tourism, trade openness, innovation and carbon dioxide emissions: International evidence. International Journal of Sustainable Energy, 42(1), 397–416. https://doi.org/10.1080/14786451.2023.2192827

76.

Trinugroho

Law

S. H.

Lee

W. C.

Wiwoho

Sergi

B. S.

(2021). Effect of financial development on innovation: Roles of market institutions. Economic Modelling, 103, 105598. https://doi.org/10.1016/j.econmod.2021.105598

77.

Ullah

Raza

Mehmood

(2023). The impact of economic growth, tourism, natural resources, technological innovation on carbon dioxide emission: Evidence from BRICS countries. Environmental Science and Pollution Research, 30(32), 78825–78838. https://doi.org/10.1007/s11356-023-27903-4

78.

UN Tourism. (2024). UN Tourism: Latest Tourism Data. https://www.unwto.org/un-tourism-world-tourism-barometer-data

79.

Usman

Bekun

F. V.

Ike

G. N.

(2020). Democracy and tourism demand in European countries: Does environmental performance matter? Environmental Science and Pollution Research, 27(30), 38353–38359. https://doi.org/10.1007/s11356-020-10258-5

80.

Voumik

L. C.

Mimi

M. B.

Raihan

(2023). Nexus between urbanization, industrialization, natural resources rent, and anthropogenic carbon emissions in South Asia: CS-ARDL approach. Anthropocene Science, 2(4), 48–61. https://doi.org/10.1007/s44177-023-00047-3

81.

Walter

Ugelow

J. L.

(1979). Environmental policies in developing countries. Technology, Development and Environmental Impact, 8(2/3), 102–109. https://www.jstor.org/stable/4312437

82.

Wang

Choi

C. H.

(2024). Security or sovereignty: The dynamic impact of political crises on Chinese tourism. Current Issues in Tourism, 27(23), 4049–4065. https://doi.org/10.1080/13683500.2023.2273915

83.

Wang

Park

Choi

C. H.

(2020). The nexus between international trade, FDI and income inequality. Journal of Korea Trade, 24(4), 18–33. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3671105

84.

Wang

Sami

Khan

Alamri

A. M.

Zaidan

A. M.

(2023). Green innovation and low carbon emission in OECD economies: Sustainable energy technology role in carbon neutrality target. Sustainable Energy Technologies and Assessments, 59, 103401. https://doi.org/10.1016/j.seta.2023.103401

85.

Wei

Ullah

(2022). International tourism, digital infrastructure, and CO2 emissions: fresh evidence from panel quantile regression approach. Environmental Science and Pollution Research, 29(24), 36273–36280.

86.

Werikhe

(2022). Towards a green and sustainable recovery from COVID-19. Current Research in Environmental Sustainability, 4, 100124. https://doi.org/10.1016/j.crsust.2022.100124

87.

Liu

(2021). Higher education development, technological innovation and industrial structure upgrade. Technological Forecasting and Social Change, 162, 120400. https://doi.org/10.1016/j.techfore.2020.120400

88.

Xiao

Xie

Shahzad

M. F.

Khattak

J. K.

(2020). The moderating role of product type in network buying behavior. Sage Open, 10(2). https://doi.org/10.1177/2158244020917953

89.

Fan

Yang

Shao

(2021). Heterogeneous green innovations and carbon emission performance: Evidence at China’s city level. Energy Economics, 99, 105269. https://doi.org/10.1016/j.eneco.2021.105269

90.

Yang

Shao

Fan

Yang

(2021). Wage distortion and green technological progress: A directed technological progress perspective. Ecological Economics, 181, 106912. https://doi.org/10.1016/j.ecolecon.2020.106912

91.

Yin

Choi

C. H.

J. O.

(2022). Economic and non-economic determinants of environmental sustainability in the long run evidence from G20 economies. Journal of Korea Trade, 26(1), 1–19. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4619950

92.

Yin

Z. H.

Zhang

T. H.

Choi

C. H.

(2023). Toward sustainable development: Does digitalization narrow the gender gap in the labor market? Sustainable Development, 31(5), 3528–3539. https://doi.org/10.1002/sd.2608

93.

Yousaf

Jareño

Tolentino

(2023). Connectedness between defi assets and equity markets during COVID-19: A sector analysis. Technological Forecasting and Social Change, 187, 122174. https://doi.org/10.1016/j.techfore.2022.122174

94.

Yuan

Shahzad

M. F.

Waheed

Wang

(2024). Sustainable development in brand loyalty: Exploring the dynamics of corporate social responsibility, customer attitudes, and emotional contagion. Corporate Social Responsibility and Environmental Management, 31(2), 1042–1051.

95.

Yue

X. G.

Liao

Zheng

Shao

Gao

(2021). The role of green innovation and tourism towards carbon neutrality in Thailand: Evidence from bootstrap ADRL approach. Journal of Environmental Management, 292(15), 112778. https://doi.org/10.1016/j.jenvman.2021.112778

96.

Wang

Hou

Pan

(2023). The effect of economic growth pressure on green technology innovation: Do environmental regulation, government support, and financial development matter? Journal of Environmental Management, 330, 117172. https://doi.org/10.1016/j.jenvman.2022.117172

97.

Zeng

Wen

Feiock

(2021). Effect of tourism development on urban air pollution in China: The moderating role of tourism infrastructure. Journal of Cleaner Production, 280(1), 124397. https://doi.org/10.1016/j.jclepro.2020.124397

98.

Zhang

Marzuki

Liao

(2023). The coordination pattern of tourism efficiency and development level in Guangdong province under high-quality development. Humanities and Social Sciences Communications, 10(796), 1–10. https://doi.org/10.1057/s41599-023-02314-5

99.

Zhuang

Yang

Razzaq

Khan

(2023). Environmental impact of infrastructure-led Chinese outward FDI, tourism development and technology innovation: A regional country analysis. Journal of Environmental Planning and Management, 66(2), 367–399. https://doi.org/10.1080/09640568.2021.1989672

Tourism Development,Foreign Direct Investment,and Environmental Sustainability: How Green Technology Innovations Make a Difference

Abstract

Plain language summary

Keywords

Highlights

Introduction

Literature Review

Tourism and Environmental Sustainability

FDI and Environmental Sustainability

GTI and Environmental Sustainability

Research Gap

Data and Methodology

Variable

Dependent Variable

Independent Variable

Variable Statistics

Model Specification and Theoretical Framework

Econometric Model

Theoretical Framework

Regression Precheck

Benchmark Regression

FMOLS, DOLS, CCR and FGLS Regression

Asymmetric Analysis

Robustness Test

Further Analysis

Moderating Effect of GTI

Threshold Effect

Conclusion

Footnotes

ORCID iDs

Funding

Declaration of Conflicting Interests

Data Availability Statement

References