The relationship between renewable energy consumption, international tourism, trade openness, innovation and carbon dioxide emissions: international evidence

ABSTRACT

The main concerns for nations worldwide in accomplishing long-term development goals are avoiding the adverse effects of climate change, lowering emissions, and enhancing environmental sustainability. This study examines the effects of 53 nations' use of renewable energy, trade openness, global tourism, and innovation on carbon emissions from 1990–2019 by using FMOLS, DOLS, GMMSystem estimator. The results demonstrate that renewable energy, trade openness, and innovation reduce emissions, whereas global travel increase environmental degradation. The findings have major policy implications for adopting trade openness policies to enhance environmental quality. Innovation that enhances the environment's quality for both country groups may be a remarkable discovery for the authorities. Findings suggest that renewable energy and technological innovation are essential for sustainable development.

KEYWORDS:

• Renewable energy
• trade openness
• tourism
• innovation

1. Introduction

The consumption of energy and economic expansion are closely related. Energy use causes a rise in glasshouse gas (GHG) emissions. According to United Nations climate change studies, fossil energy is the main contributor to global climate change, responsible for approximately 90% of all CO₂ emissions and more than 75% of all glasshouse gas emissions.¹ As a result, relying on energy use to fuel economic expansion could harm the environment. Recently, growing worries about the close link between climate change and economic growth have been sparked by the acceleration of GHG emissions in developing nations. Hope (2006) argued that although climate change might originally have some positive influences for many developed nations, it will be harmful to the environment in the long run. However, according to Grossman and Krueger (1995) and Hitz and Smith (2004), economic growth can bring an initial degradation period. Still, because of the adoption of better technologies may enhance the environmental quality. There have been many studies examining the progress and success of Government programs related to investment and development of innovative technology to mitigate the effects of climate change. Some studies, such as Raihan and Voumik (2022) for India; Ibrahim and Mohammed (2022) for Gulf countries, suggest that technological innovation has a detrimental impact on carbon emissions. Increasing the number of patents may enhance environmental quality. However, some researchers such as Demircan Çakar et al. (2021) claim that innovation positively affects carbon emissions. According to certain research, innovation has no effect on CO₂ emissions (Álvarez-Herránz et al. 2017; Amri, Bélaïd, and Roubaud 2018; Khattak et al. 2020). Furthermore, innovation is one of the major factors that promote the economic growth of many countries (Khan et al. 2022). According to Anakpo and Oyenubi (2022), innovation positively impacts economic growth. However, economic development is often accompanied by environmental degradation because some policies that promote economic growth lead to increasing CO₂ emissions (Raihan and Tuspekova 2022a; Raihan and Tuspekova 2022b). This is harmful to the quality of the environment and stimulates climate change. This study focuses on the world's 53 countries and will examine whether innovation helps lessen negative environmental impacts.

In addition, there are also many studies conducted on carbon dioxide emissions and their different determinants, such as financial development, renewable energy consumption, urban population growth, trade openness, foreign direct investment, international tourism, economic growth and innovation for many different regions and countries such as Ișik et al. (2020) for G7 countries, Raihan and Voumik (2022) for India, Raihan and Tuspekova (2022c) for Russia, Zhang et al. (2022) for China Furthermore, the connection between renewable energy consumption, trade openness, innovation and carbon dioxide emissions is one of the key concerns for the researchers and policymakers to combat the climate change. According to Li and Haneklaus (2022b), Usman et al. (2022) trade openness has a positive influence on carbon emissions. Trade openness is considered a major factor in promoting economic growth. However, activities related to export and import also cause environmental degradation in some countries. The rising trade openness, foreign direct investment, and urban population growth lead to the reduction of environmental quality in some countries due to such countries encouraging economic growth through poor policies. Moreover, many studies also argued that tourism is a vital factor in enhancing economic growth (Isik, Dogru, and Turk 2018; Dogru, Isik, and Sirakaya-Turk 2019; Işık et al. 2021; Işık et al. 2021; Işık et al. 2022), but tourism-related activities lead to increasing the climate change (Ișik et al. 2020). In addition, according to Batool et al. (2022), financial development and economic growth positively impact energy consumption in South Asian countries. This means economic growth leads an increase in energy consumption. However, energy consumption from fossil fuels might cause environmental degradation because of the increase in carbon emissions and negatively influence the quality of the environment in some countries (Teng et al. 2021). According to Cao et al. (2022), Teng et al. (2021) and Khan et al. (2021), renewable energy consumption make to decrease carbon dioxide emissions and help to improve the quality of the environment. When using energy consumption from fossil fuels is replaced by using renewable energy to help reduce carbon emissions (Khan, Khan, and Binh 2020; Khan et al. 2020).

Our study contributes to the existing literature in several ways. First, previous studies on factors affecting CO₂ emissions only focused on areas such as OECD countries, BRICS, Mediterranean countries, G-20 countries, South Asian countries, ASEAN countries, Sub-Saharan African (SSA) countries, Gulf countries, China and Indian, and China and Indonesia countries. This study is the first international evidence. Second, few previous studies are limited by factors affecting CO₂ emissions for the world countries. For example, Raihan and Voumik (2022) only focused on financial development, renewable energy use, technological innovation, economic growth, and urbanisation on carbon dioxide (CO₂) emissions in India, Adebayo et al. (2022) focused on renewable energy consumption and trade openness on carbon emissions in Sweden, Azam, Rehman, and Ibrahim (2022) only concentrated on the relationship between industrialisation, urbanisation, trade openness, and carbon emissions for OPEC economies. Chhabra, Giri, and Kumar (2022) only focused on the nexus between technological innovations, trade openness and CO₂ emissions for middle-income countries. Wang, Zhang, and Li (2023) concentrated on trade openness, human capital, renewable energy, natural resource rent and CO₂ emissions in 208 counties. While Weili, Bibi, and Khan (2022) investigate the effect of carbon dioxide, energy consumption, and economic growth on innovations. Therefore, this research will comprise an all-inclusive analysis considering a range of factors that contribute to CO₂ emissions, including renewable energy consumption, trade openness, urban population growth, international tourism, foreign direct investment, economic growth, and innovation. Finally, in this study, we examine the difference in factors affecting CO₂ emissions in two groups of developed and developing countries. Furthermore, the research results can help managers have a better overview of the factors affecting CO₂ emissions in countries, thereby some economic development policies should be suggested to ensure environmental sustainability.

This study examines the effects of 53 country use of renewable energy, trade openness, global tourism, and innovation on carbon dioxide emissions. Using FMOLS, the DOLS model, and the GMM-System estimator, panel data from 1990 to 2019 were employed. The results demonstrate that the consumption of renewable energy, trade openness, and innovation negatively influence carbon emissions, whereas global travel and economic expansion have a positive impact.

The structure of this research will be presented as follows: the empirical literature will be shown in Section 2, Section 3 will present the study models and methods, the results and the discussions of the findings will be illustrated in Section 4, and Section 5 is the study conclusion.

2. Literature review

There are much research conducted on carbon dioxide emissions and its different determinants such as renewable energy consumption, urban population growth, trade openness, foreign direct investment, international tourism, economic growth and innovation. Many studies show that energy consumption help to rise economics growth, but it also influences environmental quality and using renewable energy is one of the beneficial solutions for environmental quality. Likely, Many types of research indicate that trade openness, foreign direct investment, international tourism, and innovation negatively affect environment degradation while some argue that such factors lead to increase carbon emissions. We will conduct a step-by-step literature review to examine the relationship between the factors in this study as follows:

2.1. Renewable energy consumption (REC), foreign direct investment (FDI), economic growth, and carbon dioxide emissions

Economic growth is the top goal of all countries, but its impact on environmental quality is also a matter of concern for policy makers (Banerjee 2022). The improvement of economic growth (GDP) leads to the increase in CO₂ emissions (Ali, Abdul-Rahim, and Ribadu 2017; Mensah et al. 2018; Xu et al. 2016; Ali, Law, and Zannah 2016; Raihan and Tuspekova 2022b, 2022a). However, Khan et al. (2021) illustrate that economic growth negatively affect CO₂ emissions. According to Batool et al. (2022), financial development and economic growth have a positive impact on energy consumption in South Asian countries. This means economic growth influences the quality of environment due to the increase in energy consumption in such countries. Besides, Khan et al. (2022) explore that carbon neutrality has a positive relationship with green economic growth (GEG). Furthermore, there have also been many studies that analysed the effect of renewable energy on carbon dioxide emissions. For instance, Liu, Zhang, and Bae (2017) found that there is a negative impact of renewable energy and agriculture on CO₂ emissions, while non-renewable energy has a positive effect on carbon. Moreover, renewable energy consumption leads to mitigating CO₂ emissions in OECD countries found in the research of Mensah et al. (2018). According to Hu et al. (2018), the rising share of renewable energy consumption makes the decreasing of CO₂ emissions. Zhang, Ajide, and Ridwan (2021) suggest that the use of non-renewable energy should be limited towards environmental sustainability and human capital development should be carried out to promote environmental quality across all levels of education. Furthermore, renewable energy use hurts CO₂ emissions is also found in the research of Ișik et al. (2020); Teng et al. (2021); Muhammad et al. (2021); Khan et al. (2021); Cao et al. (2022); Raihan and Voumik (2022). Besides, Yu et al. (2023) explore that renewable energy promotion plays an important role to solve the growth of oil dependence.

In addition, much research have also been done to explore the relationship between FDI and CO₂ emissions. For instance, Khan et al. (2021) indicated that FDI positively affect CO₂ emissions in developed countries and the opposite was true in developing countries. They suggest using renewable energy should be encouraged to enhancing environmental quality in such countries. The positive impact of foreign direct investment on CO₂ emissions is also found in the research of Muhammad et al. (2021) and Teng et al. (2021). In contrast, according to Muhammad et al. (2021) foreign direct investment hurts CO₂ emissions, this factor causes a decreasing of environmental degradation in developed countries, while it leads to increase environmental degradation in BRICS, developing countries. Besides, economic growth also leads to an increase in the environment degradation by applying the model of fixed effect and GMM for the period of 1991–2018. A negative impact of FDI on CO₂ emissions is also found in the study of Zhang et al. (2022). Energy consumption and industrialisation have a positive impact on CO₂ emissions (Raihan and Tuspekova 2022c). They suggest that the use of renewable energy sources, green industry, and sustainable forest management should be done to ensure environmental sustainability.

2.2. Tourism and carbon dioxide emissions

Many studies have explored that tourism development leads to reducing the environmental depression. For example, Balogh and Jámbor (2017) indicate that tourism arrivals and international trade allow improve environmental quality. A similar result was found in the study of Alam and Paramati (2017). They have shown that trade openness and tourism help to reduce carbon dioxide emissions by employing the FMOLS model. A negative association between tourist arrivals and carbon dioxide emissions was also found in the research of Lee and Brahmasrene (2013), Aïssa, Jebli, and Youssef (2014), Katircioglu, Feridun, and Kilinc (2014), Leità and Shahbaz (2016), Ben Jebli, Youssef, and Apergis (2019), Balsalobre-Lorente et al. (2020). According to Ben Jebli, Youssef, and Apergis (2019), tourism, renewable energy, and FDI hurt carbon emissions. Besides Dogru et al. (2020) tourism development hurts carbon dioxide emissions in Canada, Czechia, and Turkey.

On the other hand, Khan et al. (2021) have found tourism is one of the factors that cause the increase of CO₂ emissions. In addition, according to Muhammad et al. (2021), tourism and foreign direct investment positively affect CO₂ emissions, while governance helps to improve the quality of the environment. A positive effect of tourism on carbon emissions was also found in research by Shakouri, Yazdi, and Ghorchebigi (2017), Işik, Kasımatı, and Ongan (2017), Sharif, Afshan, and Nisha (2017), Ișik et al. (2020), Ibrahim and Mohammed (2022). Ișik et al. (2020) show that the receipt of international touristm positively impacts on CO₂ emissions in Italy. They suggest renewable energy consumption should be considered to ensure tourism and environmental sustainability.

2.3. Trade openness and carbon emission

There are some studies analysed the impact of trade openness on CO₂ emission. Some of them found that trade openness help to reduce CO₂ emission, but others stated that trade openness is harmful to environmental quality. For example, Ali, Law, and Zannah (2016) show that trade openness negatively affect carbon emissions, while economic growth and energy consumption positively affect carbon emissions. Similarly, Bernard and Mandal (2016) indicate that trade openness enhances the quality of the environment. According to Zhang et al. (2017), trade openness has a negative impact on CO₂ emissions. The study was conducted in 10 industrialised countries from 1971 to 2013. According to Khan et al. (2021), trade openness plays a vital role in economics growth and it reduces CO₂ emissions in developed countries. A similar result is also found in the study of Cole, Elliott, and Okubo (2010); Roy (2017); Yu et al. (2019); Sun et al. (2019); Du, Li, and Yan (2019); Leitão and Balogh (2020); Adebayo et al. (2022); Li and Haneklaus (2022a); Wang, Zhang, and Li (2023).

On the other hand, according to Le, Chang, and Park (2016) trade openness leads to reduce the quality of the environment. However, there are different results between high-income countries and middle- and low-income countries. Trade openness negatively affects environmental degradation in high-income countries, while it is harmful to the environment in middle- and low-income countries. Likely, Ertugrul et al. (2016) show a similar result in Turkey, India, China and Indonesia countries. They found that Trade openness leads to a rise in carbon emissions in Turkey, India, China and Indonesia countries. Furthermore, Mahrinasari, Haseeb, and Ammar (2019) show the positive impact of trade liberalisation on carbon emission by employing FMOLS and DOLS models. A positive impact of trade openness on CO₂ emissions is indicated in the findings of Li and Haneklaus (2022b) for G7 countries; Usman et al. (2022) for Pakistan; Chhabra, Giri, and Kumar (2022) for selected middle-income countries; Li and Haneklaus (2022c) for China; Azam, Rehman, and Ibrahim (2022) for six OPEC countries;

2.4. Innovation and carbon dioxide emissions

There are many types of research investigated the nexus between innovation and environment degradation. Some researchers found that innovation does not have an impact on CO₂ emissions (Álvarez-Herránz et al. 2017; Amri, Bélaïd, and Roubaud 2018; Khattak et al. 2020). Others explored that innovation makes to decrease CO₂ emissions. For instance, Irandoust (2016) indicates the important role of renewable energy and technological innovation in improving the quality of environment. According to Zhang et al. (2017), resource innovation, knowledge innovation, and environmental innovation negatively affect CO₂ emissions. The same result was found in the research of Samargandi (2017). Similarly, Mensah et al. (2018) indicated that innovation can reduce CO₂ emissions in 28 OCED countries. Likely, Danish (2019) has shown that the ICT reduce carbon emissions in the 59 countries along Belt and Road for the period of 1990–2016. Moreover, Du, Li, and Yan (2019) found that green technology innovations do not make to decrease CO₂ emissions for countries whose level of income is below the threshold. However, there is a reducing effect on carbon emissions for the economies whose income level is above the threshold. It was found technological innovations negatively affect carbon emissions in China (Shahbaz et al. 2020). In addition, Shahbaz et al. (2020) showed that technological innovations negatively affect CO₂ emissions. According to Nguyen, Pham, and Tram (2020), energy price, FDI, and trade openness, technology and spending on innovation make to decrease CO₂ emissions by researching 13 selected G-20 countries for the period of 15 years from 2000 to 2014. A similar result is also found in the study by Wen et al. (2020).

On the other hand, some studies show that there is a positive relationship between innovation and CO₂ emissions. According to Su and Moaniba (2017), the rising of carbon emissions leads to more innovations linked to climate change. They suggest that public funds should be used for innovative activities to struggle with the change of climate. Moreover, Demircan Çakar et al. (2021) examined the effect of innovation on CO₂ emissions in 8 developing countries and 6 developed countries in Mediterranean countries by applying the PMG and DFE methods for the period of 1997–2017 and 2003–2017, respectively. They have found that there is a positive relationship between the level of innovation and CO₂ emission in both developed and developing countries. In addition, Weili, Bibi, and Khan (2022) show that CO₂ emissions and economic growth increase technological innovations while FDI reduces innovations.

2.5. Urban population growth and carbon dioxide emissions

There have been some studies that analysed the impact of urbanisation on CO₂ emission. Some of them found that urbanisation helps to reduce CO₂ emission, but others stated that urbanisation leads to increase CO₂ emission. For instance, Ali, Abdul-Rahim, and Ribadu (2017) show that urbanisation leads to decrease CO₂ emissions in Singapore. Furthermore, Li et al. (2018) show that urbanisation negatively affect carbon dioxide emissions efficiency. Wang et al. (2021) have investigated the effect of urbanisation on CO₂ emissions in OECD countries by employing the autoregressive distribution lag (ARDL) model. They indicated that urbanisation negatively affects carbon emissions for developed countries.

On the contrary, Anwar, Younis, and Ullah (2020) indicate that urbanisation, GDP, and trade openness have a positive impact on CO₂ emissions. Their study suggests the government should support green and sustainable urbanisation and encourage using renewable energy to help economic development and reduce the depression of the environment. The same result is also found in the research of Raihan and Voumik (2022). Furthermore, Lee et al. (2022) have evaluated the effect of urbanisation on carbon dioxide emissions in China from 1996 to 2018. They showed that increasing urbanisation causes rising of carbon dioxide emissions. However, their finding also found that after having a certain level of foreign capital, this negative effect gets weaker. when the technology, finance, and government become more developed, urbanisation can be accelerated to reduce CO₂ emissions.

A review of existing studies on CO₂ emissions reveals several research gaps that this study searches to fill. First, previous studies on factors affecting CO₂ emissions only focused on areas such as OECD countries, BRICS, Mediterranean countries, G-20 countries, South Asian countries, ASEAN countries, in Sub-Saharan African (SSA) countries, in Gulf countries, China and Indian and China and Indonesia countries. Second, Little is known about the factors affecting CO₂ emissions in available studies for the world countries. The few previous studies are limited by factors affecting CO₂ emissions for the world countries. For example, Raihan and Voumik (2022) only focused on financial development, renewable energy use, technological innovation, economic growth, and urbanisation on carbon dioxide (CO₂) emissions in India, Adebayo et al. (2022) focused on renewable energy consumption and trade openness on carbon emissions in Sweden, Azam, Rehman, and Ibrahim (2022) only concentrated on the relationship between industrialisation, urbanisation, trade openness, and carbon emissions for OPEC economies. Chhabra, Giri, and Kumar (2022) only focused on the nexus between technological innovations, trade openness and CO₂ emissions for middle-income countries. Wang, Zhang, and Li (2023) concentrated on trade openness, human capital, renewable energy, natural resource rent, and CO₂ emissions in 208 counties. while Weili, Bibi, and Khan (2022) investigate the effect of carbon dioxide, energy consumption, and economic growth on innovations in the world's 181 countries. Finally, in this study, we examine the difference in factors affecting CO₂ emissions in two groups of developed and developing countries. Therefore, this research will comprise an all-inclusive analysis considering a range of factors that contribute to CO₂ emissions including renewable energy consumption, trade openness, urban population growth, international tourism, foreign direct investment, economic growth, and innovation for the world's 53 countries.

3. Methodology

3.1. Data and variables

This study investigates the effect of renewable energy consumption, trade openness, urban population growth, international tourism, foreign direct investment, economic growth, and innovation on carbon dioxide emissions in the world's 53 countries for the period of 1990–2019. The data of all variables have been collected from World Bank. In this research, carbon dioxide emissions are the dependent variable, renewable energy consumption, trade openness, urban population growth, international tourism, foreign direct investment, economic growth, and innovation are explanatory variables. Table 1 displays variables, measurement, and data sources for this study.

Table 1. Variables, measurement, and data sources.

Variables	Abb.	Variables measurement	Data source
Carbon dioxide emissions	CO2	Carbon dioxide emissions (metric tons per capita)	(WDI, 2022)
Renewable energy consumption	REC	Renewable energy consumption (% of total final energy consumption)	(WDI, 2022)
Urban population growth	UPG	Annual (%)	(WDI, 2022)
Trade openness	TO	${\displaystyle \frac{Exports+imports}{GDP}}$(constant 2015 US$)	(WDI, 2022)
Foreign direct investment	FDI	Foreign direct investment, net inflows (% of GDP)	(WDI, 2022)
International tourism	IT	International tourism, number of arrivals	(WDI, 2022)
Gross domestic product	GDP	GDP per capita (constant 2015 US$)	(WDI, 2022)
Innovation	INNOVA	Patent applications, residents, number of patent applications	(WDI, 2022)

Note: WDI = World Development Indicators.²

3.2. Empirical model

Before analysing the impact of all the above variables on carbon dioxide emissions, we use different sets of panel unit root tests such as Levin–Lin–Chu, Im-Pesaran-Shin, Breitung and Fisher to check for the stationarity of all variables at the level or first differences for the period of 1990–2019. If the variables have the unit root (not stationary) at level, the studies should be confirmed if the variables are integrated in the first differences I(1). The next step, checking the cointegration relationship in long run among variables will be confirmed by Westerlund, Kao, and Pedroni tests. If there is a cointegration relationship between the variables in the model, FMOLS (fully modified ordinary least square) and DOLS (dynamic ordinary least square) models will be employed to evaluate the impact of all variables on carbon dioxide emissions in the world's 53 countries from 1990 to 2019. Moreover, estimation results of OLS—ordinary least squares, fixed effects, and random effects models often have problems with series correlation and endogeneity of independent variables (Leitao 2012; Leitão 2013; Jambor and Carlos Leitão 2016). The estimator of the system generalised moments method (GMM-System) allows solving econometric problems such as serial correlation and the endogeneity of independent variables.

Research of Adebayo et al. (2022), Azam, Rehman, and Ibrahim (2022), Chhabra, Giri, and Kumar (2022), Raihan and Tuspekova (2022a); Raihan and Tuspekova (2022b), Cao et al. (2022), Raihan and Voumik (2022), Lee et al. (2022), Ibrahim et al. (2022), Ibrahim and Mohammed (2022), Zhang et al. (2022) constitute the theoretical foundation of this research, which focuses mainly on the relationship between renewable energy consumption, trade openness, urbanisation, international tourism, foreign direct investment, economic growth, and innovation on carbon dioxide emissions. Renewable energy consumption plays an important role in reducing environmental degradation, if renewable energy use is stressed, then renewable energy consumption may have an impact on the quality of environment (Raihan and Tuspekova 2022c). Moreover, trade openness, urbanisation, and economic growth are interrelated, urbanisation, and trade openness may contribute to economic growth, which can affect CO₂ emissions (Anwar, Younis, and Ullah 2020; Raihan and Tuspekova 2022c). Furthermore, tourism development plays an important role in promoting economic growth, if tourism development is done properly, it will promote economic development and may improve environmental quality (Işık, Sirakaya-Turk, and Ongan 2020). Besides, attracting foreign direct investment is one of the ways to help economic development but the quality of the environment may be affected as many industrial parks and manufacturing enterprises are established. Technological innovation may help to promote environmental sustainability, climate change is one of the major problems that countries around the world are facing, technological innovation is one of the effective ways to reduce the impact of climate change (Zhang et al. 2022). The CO₂ emission model built in this study is as follows: (1) ${\mathrm{C}\mathrm{O}}_{2_{\mathrm{i}\mathrm{t}}}=\mathrm{f}(\mathrm{R}\mathrm{E}{\mathrm{C}}_{\mathrm{i}\mathrm{t}},\mathrm{U}\mathrm{P}{\mathrm{G}}_{\mathrm{i}\mathrm{t}},\mathrm{T}{\mathrm{O}}_{\mathrm{i}\mathrm{t}},\mathrm{F}\mathrm{D}{\mathrm{I}}_{\mathrm{i}\mathrm{t}},\mathrm{I}{\mathrm{T}}_{\mathrm{i}\mathrm{t}},\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{i}\mathrm{t}},\mathrm{I}\mathrm{N}\mathrm{N}\mathrm{O}\mathrm{V}{\mathrm{A}}_{\mathrm{i}\mathrm{t}})$(1)

where CO₂ refers to carbon dioxide emissions. REC, UPG, TO, FDI, IT, GDP, and INNOVA are the explanatory variables, representing renewable energy consumption, urban population growth, trade openness, foreign direct investment, international tourism, Gross domestic product per capita (constant 2015 US$), and number of patent applications, respectively.

i indicates country (i = 1, … , N)

t indicates time (t = 1990 … 2019)

The trade openness variable is determined as follows: $Tradeopenness={\displaystyle \frac{X+M}{GDP}}$

where X signifies exports of goods and services (constant 2015 US$), M represents Imports of goods and services (constant 2015 US$), and GDP gross domestic product (constant 2015 US$).

The empirical model of the variables is represented by the following equation: (2) $\mathrm{C}\mathrm{O}{2}_{it}={\alpha }_0+{\alpha }_1\mathrm{R}\mathrm{E}{C}_{it}+{\alpha }_2\mathrm{U}\mathrm{P}{\mathrm{G}}_{\mathrm{i}\mathrm{t}}+{\alpha }_3\mathrm{T}{O}_{it}+{\alpha }_4\mathrm{F}\mathrm{D}{\mathrm{I}}_{it}+{\alpha }_5\mathrm{I}{\mathrm{T}}_{it}+{\alpha }_6\mathrm{G}\mathrm{D}{\mathrm{P}}_{it}+{\alpha }_7\mathrm{I}\mathrm{N}\mathrm{N}\mathrm{O}\mathrm{V}{A}_{it}$(2) The econometric model is presented as follows, Equation (2) is substituted for (3) $\mathrm{C}{\mathrm{O}}_{2_{it}}={\alpha }_0+{\alpha }_1\mathrm{R}\mathrm{E}{\mathrm{C}}_{it}+{\alpha }_2\mathrm{U}\mathrm{P}{\mathrm{G}}_{\mathrm{i}\mathrm{t}}+{\alpha }_3\mathrm{T}{\mathrm{O}}_{it}+{\alpha }_4\mathrm{F}\mathrm{D}{\mathrm{I}}_{it}+{\alpha }_5\mathrm{I}{\mathrm{T}}_{it}+{\alpha }_6\mathrm{G}\mathrm{D}{\mathrm{P}}_{it}+{\alpha }_7\mathrm{I}\mathrm{N}\mathrm{N}\mathrm{O}\mathrm{V}{\mathrm{A}}_{it}+{ϵ}_{it}$(3)

where ${\alpha }_0$ and ${ϵ}_{\mathrm{i}\mathrm{t}}$ are intercept and error term. Besides, $\alpha 1,\alpha 2,\alpha 3,\alpha 4,\alpha 5,\alpha 6\mathrm{a}\mathrm{n}\mathrm{d}\alpha 7$ indicate the coefficients.

The econometric model of the system GMM to explore the dynamic influence of renewable energy consumption, trade openness, urban population growth, international tourism, foreign direct investment, economic growth, and innovation on carbon dioxide emissions as follows: (4) $\mathrm{C}{\mathrm{O}}_{2_{it}}={\alpha }_0+{\alpha }_1\mathrm{C}{\mathrm{O}}_{2_{it}}-1+{\alpha }_2\mathrm{R}\mathrm{E}{\mathrm{C}}_{it}+{\alpha }_3\mathrm{U}\mathrm{P}{\mathrm{G}}_{\mathrm{i}\mathrm{t}}+{\alpha }_4\mathrm{T}{\mathrm{O}}_{it}+{\alpha }_5\mathrm{F}\mathrm{D}{\mathrm{I}}_{it}+{\alpha }_6\mathrm{I}{\mathrm{T}}_{it}+{\alpha }_7\mathrm{I}\mathrm{N}\mathrm{N}\mathrm{O}\mathrm{V}\mathrm{A}+{\alpha }_8{X}_{it}+{ϵ}_{it}$(4)

where gross domestic product per capita (constant 2015 US$) are the control variables of the study and are deputised by X_it.

Based on the literature review, we construct the following hypothesis:

Hypothesis 1 (H1): Renewable energy consumption leads to reduce the environmental degradation.

Renewable energy consumption plays an important role in reducing environmental degradation, if renewable energy use is stressed, then renewable energy consumption may have a positive impact on the quality of environment (Cao et al. 2022; Raihan and Voumik 2022; Raihan and Tuspekova 2022c).

Hypothesis 2 (H2): Trade openness promotes the environmental quality.

Research of Adebayo et al. (2022), Li and Haneklaus (2022a) and Wang, Zhang, and Li (2023) found a negative effect of trade openness on environmental degradation, trade openness encourages environmental sustainability. In some countries, the standard environmental policy and the common commercial policy encourage sustainable practices to decrease CO₂ emissions and the change of climate.

Hypothesis 3 (H3): International tourism effect to CO₂ emissions

Many studies have explored the impact of international tourism on the environmental quality. The studies of Ben Jebli, Youssef, and Apergis (2019), Balsalobre-Lorente et al. (2020) and Dogru et al. (2020) found a negative nexus between international tourism and CO₂ emissions. Others support a positive relationship (Ișik et al. 2020; Ibrahim and Mohammed 2022).

Hypothesis 4 (H4): Innovation has an impact on CO₂ emissions.

Technological innovation may help to promote environmental sustainability, climate change is one of the major problems that countries around the world are facing, technological innovation is one of the effective ways to reduce the impact of climate change (Wen et al. 2020; Shahbaz et al. 2020; Zhang et al. 2022).

4. Results and discussion

In this part, we indicate the outcome of the relationship between renewable energy consumption, urban population growth, trade openness, foreign direct investment, international tourism, economic growth, innovation and carbon dioxide emissions. Firstly, the outcome of analysing the general statistics is presented at Tables 2 and 3 shows the outcome of panel unit root test at level and first different for all variables. Fourth line: The outcome from Table 3 indicate that there is cointegration relationship between variables in this research, please correct as: The outcome from Table 4 indicate that there is cointegration relationship between variables in this research. The tests of Levin–Lin–Chu, Im-Pesaran-Shin, Breitung, and Fisher are applied in this study to consider the variables stationary at level or at the first difference. The next step, we consider the cointegration relationship between variables by applying Kao, Pedroni, and Westerlund test. The outcome from Table 3 indicates that there is cointegration relationship between variables in this research. Therefore, the panel fully modified least squares (FMOLS) and panel dynamic least squares (DOLS) are considered to estimate the model. Then we also use the generalised method of moments (GMM) to ensure the result of the study.

Table 2. Description of the variables.

Variables	Mean	Minimum	Maximum	Std. Dev.
CO2	5.220	0.102	20.469	3.989
REC	22.806	0.194	85.628	20.423
UPG	1.472	−2.282	7.925	1.457
TO	0.681	0.007	3.525	0.471
FDI	167.66037446^1	−3.443^1	7.338^1	50416624602^1
IT	18757522.4^1	14000^1	217877000^1	35205147.8^1
GDP	17047.33^1	512.0966^1	88413.19^1	18364.06^1
INNOVA	15696.57	1	387364	56223.31
Observations	1590	1590	1590	1590

¹These values are converted in $US trillion for ease of interpretation.

The data is collected by the World Bank dataset.

Description statistics of all variables in this empirical study are shown in Table 2. The mean values of the variables show large differences. Moreover, the maximum values of the variables of international tourism (IT), economic growth (GDP), and innovation (INNOVA) are higher than other variables.

Table 5 presents the correlation between the variables in the model, renewable energy (Re), urban population growth (UPG), and innovation are negatively relative to CO₂ emissions (CO₂). In contrast, trade openness (TO), foreign direct investment (FDI), international tourism (IT), economic growth (GDP) are positively relative with CO₂ emissions (CO₂).

Table 5. Correlations between variables.

Variables	CO2	Re	UPG	TO	FDI	IT	GDP	Innovation
CO2	1.000
Re	−0.615	1.000
UPG	−0.354	0.394	1.000
TO	0.004	−0.255	−0.158	1.000
FDI	0.014	−0.036	−0.003	0.051	1.000
IT	0.016	−0.044	−0.090	0.149	0.037	1.000
GDP	0.129	−0.077	0.005	0.100	0.065	0.102	1.000
Innovation	−0.039	−0.064	−0.051	−0.083	0.033	0.085	−0.046	1.000

The data is collected by the World Bank dataset.

The outcome of the panel unit root test is indicated in Table 3 by applying the test of Levin–Lin–Chu, Im-Pesaran-Shin, Breitung, and ADF–Fischer chi-square. The results in Table 5 show the variables are not stationary in level I(0) such as carbon dioxide emissions, renewable energy consumption, and economic growth, but all variables (renewable energy consumption, urban population growth, trade openness, foreign direct investment, international tourism, economic growth, innovation, and carbon dioxide emissions) are stationary at the first different I(1).

Table 3. Test of panel unit root.

Variables	LLC		Im-Pesaran-Shin		Breitung		Fisher (chi-squared)
	Level	First difference	Level	First difference	Level	First difference	Level	First difference
CO2	−0.502	−14.402***	2.664	−20.285***	6.969	−11.230***	210.506***	1280.581***
REC	2.596	−14.206***	7.265	−20.182***	9.051	−16.570***	97.287	1307.350***
UPG	−7.248***	−12.190***	−4.268***	−16.753***	4.394	−7.457***	358.534***	1203.989***
TO	−7.470***	−35.699***	−1.149	−21.416***	7.866	−16.116***	126.390*	1416.183***
FDI	−10.886***	−24.226***	−16.721***	−26.337***	−13.564***	−22.116***	832.693***	2683.397***
IT	−4.356***	−18.213***	−3.698***	−21.567***	1.466	−26.301***	180.900***	1475.262***
GDP	−1.280	−5.066***	5.251	−17.500***	11.578	−11.308***	109.892	1014.003***
INNOV	−3.562***	−18.992***	−8.018***	−23.802***	−2.245**	−16.117***	479.305***	2081.023***

*** Significant at 1%, ** 5% and * 10%.

The testing of Westerlund, Kao, and Pedroni panel cointegration is illustrated in Table 4. The results illustrate that there is a cointegration association between variables in the model. The outcomes confirm a long-run relationship among the study variables.

Table 4. Panel cointegration test results.

Westerlund	Statistics	Probability
Variance ratio (all panels)	2.274	0.011
Kao
Modified-DF	−2.012	0.022
Dickey–Fuller (DF)	−4.969	0.000
Augmented-DF	−1.236	0.108
Unadjusted modified-DF	−1.775	0.037
Unadjusted-DF	−4.844	0.000
Pedroni
Modified variance ratio	−9.264	0.000
Modified Phillips-Perron	5.768	0.000
Phillips-Perron	−1.519	0.000
Augmented Dickey–Fuller	−1.103	0.134

The outcomes of fully modified least squares (FMOLS) and dynamic least squares (DOLS) are shown in Table 6. The coefficients estimated by FMOLS and DOLS models are mostly the same. Based on the outcomes in FMOLS and DOLS estimators, using renewable energy help to reduce carbon dioxide emissions, with a 1% rise in renewable energy consumption decrease of 0.112% and 0.120%, respectively. Our outcomes are the same as the findings of Cao et al. (2022) for 36 OECD countries as well as those discovered by Raihan and Voumik (2022) for India. Based on the outcomes of this study, which suggest that the use of renewable energy should be considered as a beneficial policy tool to reduce CO₂ emissions and combat climate change in the world’s 53 countries.

Table 6. Long run estimation of FMOLS and DOLS models.

Variables	FMOLS	DOLS
REC	−0.112*** (0.011)	−0.120*** (0.009)
UPG	−0.491*** (0.161)	−0.341*** (0.053)
TO	−0.001*** (0.000)	−0.001*** (0.000)
FDI	−0.000 (0.000)	−0.000 (0.000)
IT	−0.000 (0.000)	−0.000 (0.000)
GDP	0.0008* (0.0004)	0.0009*** (0.0001)
Innov	−0.001* (0.000)	−0.001*** (0.000)

*** Significant at 1%, and * 10%.

Furthermore, as in previous studies Ali, Abdul-Rahim, and Ribadu (2017) for Singapore; Li et al. (2018) for China; Wang et al. (2021) for OECD countries; the outcome obtained by urban population growth shows a negative influence on carbon emissions. Wang et al. (2021) suggest that it is necessary to accelerate the process of urbanisation for developing countries, combined with improving energy efficiency, applying scientific and technological advances may help to reduce carbon emissions. In FMOLS and DOLS estimators of this study, 1% increase in urban population growth makes CO₂ emissions decrease 0.491% and 0.341%, respectively and significant at 1% level. This result is different from those of Raihan and Voumik (2022) for India, they show that UPG has a positive impact on environmental degradation in India. Because the increase in the number of urban people leads to an increase in the demand for cars and personal vehicles. When the demand for cars and personal vehicles increases, the demand for fossil fuels will increase. This negatively affects the quality of the environment in India. Furthermore, Lee et al. (2022) illustrate that increasing urbanisation cause rising in carbon dioxide emissions but when technology, finance, and government become more developed, urbanisation can be accelerated to reduce CO₂ emissions. Therefore, to develop the nation`s economy in terms of minimising negative impacts on environmental quality, urbanisation should be promoted in combination with technological innovation and environmentally friendly energy use.

In addition, it has also been found that the increase in trade openness will reduce CO₂ emissions for both FMOLS and DOLS estimators. This result can be explained by some environmental-related policies well have been implemented. The standard environmental policy and the common commercial policy encourage sustainable practices to decrease CO2 emissions and the change of climate. The result is similar to the findings of Yu et al. (2019) for CIS countries; Adebayo et al. (2022) for Sweden; Li and Haneklaus (2022a) for India; Wang, Zhang, and Li (2023) for the world 208 countries and oppose Li and Haneklaus (2022b) for in G7 countries; Usman et al. (2022) for Pakistan; Chhabra, Giri, and Kumar (2022) for selected middle-income countries; Li and Haneklaus (2022c) for China; Azam, Rehman, and Ibrahim (2022) for six OPEC countries; Wang, Rehman, and Fahad (2022) for G-7 economies. The management authorities need to control environmental pollution to achieve the goal of sustainable economic development with environmentally friendly policies.

Moreover, our results show that economic growth positively effects on carbon dioxide emissions at 1% and 5% significant levels for FMOLS and DOLS estimators, respectively. The explanation for this is that fossil fuels are used for production activities which lead to damage to the quality of the environment. Moreover, urbanisation causes a shift from rural to urban activities, which also affects the quality of the environment in some countries. The finding is similar to the results of Muhammad et al. (2021); Teng et al. (2021); Raihan and Voumik (2022); Raihan and Tuspekova (2022a, 2022b); Raihan and Tuspekova (2022a) who indicated a positive nexus between GDP and CO₂ emissions. Therefore, an important task for countries around the world is economic development accompanied by the improvement of environmental quality. To achieve these goals, the government should have appropriate policies to reduce dependence on fossil fuel sources in production and living activities.

Finally, We also found that innovation is a negative relationship with CO₂ emissions, the increase in the number of patent applications will reduce CO₂ emissions. Because countries have increased spending on research and development (R&D), which helps to increase production efficiency and resource using efficiency. This outcome is similar to the findings of Zhang et al. (2022) for China; Ibrahim et al. (2022) for BRICS countries; Ibrahim and Mohammed (2022) for Gulf countries; Raihan and Voumik (2022) for India and oppose Demircan Çakar et al. (2021). The outcomes of this research suggest that technological innovation development can be an effective policy for mitigating environmental degradation.

To validate the research results, the authors continue to use OLS, FEM, and GMM system estimators to evaluate the effect of renewable energy consumption, urban population growth, trade openness, foreign direct investment, international tourism, economic growth, innovation on carbon dioxide emissions. The results from OLS and FEM models have heteroskedasticity and autocorrelation are illustrated in Table 7. From the results of the Hansen test, AR1 and AR2 p-values show the suitability of the GMM model. According to the results, except UPG variable, all explanation variables are significant at 1% level. The lagged variable of carbon dioxide emissions assesses the impact in the long run. The coefficient of the lagged variable of carbon dioxide emissions shows a positive sign in the long term and has significant at 1% level. These results found the same trend as the study of Leitão and Lorente (2020). Furthermore, it is similar to the results estimated by DOLS and FMOLS methods, renewable energy consumption, trade openness, innovation hurt CO₂ emissions and GDP has a positive effect on environmental degradation. Moreover, FDI has a negative impact on CO₂ emissions, a similar result to the findings of Zhang et al. (2022) who also indicate the inflow of foreign direct investment lead to reduce carbon emissions, this may be due to the number of projects use the renewable energy is high. It is beneficial to the quality of the environment. Finally, the coefficient of international tourism (IT) presents a positive effect on carbon emissions, it is confirmed by previous research such as Ișik et al. (2020) for G7 countries; Ibrahim and Mohammed (2022) for Gulf countries. This result can be explained by attracting tourists contributing to economic development, however, some tourism-related activities have not yet paid attention to the environmental quality. Increasing the number of international tourists contributes to income improvement for middle and low-income countries but will reduce the quality of the environment. In the future development of tourism, therefore, the government needs to improve to include environmentally friendly activities.

Results in Table 8 show the impact of renewable energy consumption, trade openness, international tourism, and innovation on carbon dioxide emissions in high-income countries and middle-low-income countries.

Table 7. Generalised method of moments (GMM)-system estimator.

Variables	OLS	FEM	GMM system
CO2t-1			0.649*** (0.020)
Re	−0.117*** (0.004)	−0.068*** (0.004)	−0.044*** (0.002)
UPG	−0.418*** (0.056)	−0.010 (0.031)	−0.007 (0.008)
TO	−0.001*** (0.000)	−0.0002** (0.000)	−0.0004*** (0.0000)
FDI	−0.000 (0.000)	−0.000* (0.000)	−0.000*** (0.000)
IT	0.000 (0.000)	0.0001** (0.000)	0.000*** (0.000)
GDP	0.0008*** (0.0001)	−0.0002*** (0.000)	0.0003*** (0.000)
INNOV	−0.001*** (0.000)	−0.0001** (0.000)	−0.000*** (0.000)
C	9.740*** (0.297)	7.212*** (0.125)	2.997*** (0.143)
Observations	1590	1590	1537
R^2	0.440	0.198
Adj R^2	0.439
Breusch–Pagan	chi2(1) = 150.74 Prob > F = 0.000
Wooldridge test	F(1,52) = 131.250 Prob > F = 0.000	F(1,52) = 131.250 Prob > F = 0.000
Modified Wald		chi2(53) = 35123.51 Prob > chi = 0.0000
AR(1)			−4.78 (0.000)
AR(2)			−0.69 (0.493)
Hansen test			51.04 (0.160)

 *** Significant at 1%, ** 5% and * 10%.

Table 8. Generalised Method of Moments (GMM)-System Estimator for high-income and middle and low-income countries.

Variables	High income	Middle–Low income
CO2t-1	0.945*** (0.016)	0.924*** (0.031)
Re	−0.013*** (0.004)	−0.000 (0.002)
UPG	0.023* (0.012)	0.118** (0.046)
TO	−0.000*** (0.000)	0.001** (0.000)
FDI	−0.000** (0.000)	−0.001* (0.000)
IT	0.000*** (0.000)	0.001** (0.000)
GDP	0.000 (0.000)	−0.000 (0.000)
INNOV	−0.000***	−0.000* (0.000)
C	0.502*** (0.156)	−0.428*** (0.133)
Observations	1160	377
AR(1)	(−4.60) 0.000	(−1.95) 0.052
AR(2)	(−1.12) 0.263	(1.15) 0.251
Hansen test	(28.26) 0.249	(3.38) 0.336

 *** Significant at 1%, ** 5% and * 10%.

The outcomes of system GMM for high-income countries and middle-low-income countries in Table 8 indicate the applicability of the model because the lag of the dependent variable is statistically meaningful and the p values of AR1, AR2, and Hansen test also show model applicability. Results in Table 8 show that there is a negative relationship between renewable energy consumption and carbon emission in both groups. The consumption of renewable energy allows to reduce harmful effects on the environment in both high-income countries and middle-low-income countries. Likely, international tourism has a positive impact on carbon emission at 1% significant level in both groups. Our outcome is the same with the findings of Muhammad et al. (2021); Ibrahim and Mohammed (2022) who also indicate rising the number of international tourism lead to reduce environmental quality. This result can be explained due to attracting tourists contributes to economic development, however, some tourism-related activities have not yet paid attention to the environment quality. Similarly, trade openness in high-income countries hurts carbon emission at the significant level of 1%.

The results show that trade openness help to improve the environmental quality in high-income countries. Our results are similar to the findings of Yu et al. (2019) for CIS countries; Adebayo et al. (2022) for Sweden; Li and Haneklaus (2022a) for India; Wang, Zhang, and Li (2023) for the world 208 countries. They confirmed that trade openness decrease carbon emission and help to improve environmental degradation, this result can be explained by some environmental-related policies well have been implemented in high-income countries. On the contrary, trade openness in middle and low countries has a positive impact on carbon emissions. This result can be explained due to energy consumption related to the import and export of goods activities leading to an increase in carbon emissions in middle and low-income countries. The explanation for this is that the activities for production and trade use more resources which cause a negative impact on the environmental quality. This outcome is supported by the findings of Li and Haneklaus (2022b); Usman et al. (2022); Chhabra, Giri, and Kumar (2022); Li and Haneklaus (2022c); Azam, Rehman, and Ibrahim (2022); Wang, Rehman, and Fahad (2022). The management authorities need to control environmental pollution to achieve the goal of sustainable economic development with environmentally friendly policies. Similarly, it is found that innovation negatively impacts on carbon emissions at 1% and 10% significant levels in high-income countries and middle and low-income countries, respectively. The results show that increasing number of patent applications by residents helps to improve the environmental quality. This is supported by the research of Zhang et al. (2022) for China; Ibrahim et al. (2022) for BRICS countries; Ibrahim and Mohammed (2022) for Gulf countries; Raihan and Voumik (2022) for India and oppose Demircan Çakar et al. (2021). The outcomes of this research suggest that technological innovation development can be an effective policy for mitigating environmental degradation.

5. Conclusion

This study investigates the nexus between renewable energy consumption, trade openness, international tourism, innovation, and carbon dioxide emissions in the world's 53 countries for the period of 1990–2019 by employing the FMOLS model, DOLS model, and GMM-System estimator. The empirical outcomes indicated that the variables are integrated in the first differences I(1) based on the results of the unit root test of Levin–Lin–Chu, Im-Pesaran-Shin, Breitung, and Fisher. The results of Kao, Pedroni, and Westerlund cointegration test illustrated that the variables in this study are cointegrated in the long run. The results of FMOLS model, DOLS model, and GMM-System estimator are similar. The results of the GMM-System estimator illustrated the suitability of the GMM model based on the Hansen test, AR1 and AR2 p-values. The lagged variable of CO₂ emissions presents a positive effect, it shows that climate change gets a rise in the long run.

The empirical results of the study indicate that renewable energy consumption, trade openness, urban population growth, foreign direct investment, and innovation negatively effect on carbon emissions while international tourism and economic growth positively influence on CO₂ emissions. The findings of results present that increasing of the consumption of renewable energy, trade openness, urban population growth, foreign direct investment, and the number of patent applications which may improve the quality of the environment. On the contrary, the rising of international tourism and economic growth lead to reduce environmental quality. Further, we also have compared the research results for both groups of high-income countries and middle–low-income countries. It is found that the consumption of renewable energy and innovation show a negative effect CO₂ emission in both groups. On the contrary, international tourism has a positive impact on carbon emission in high-income countries and middle- and low-income countries. Besides, trade openness in high-income countries negatively affect CO₂ emission, but the opposite is true in middle- and low-income countries.

From the research results, we have considered giving out some suggestions such as The government should invest in renewable energy resources and encourage the use of renewable energy, it can be a vital factor to improve the environmental quality for all countries. Additionally, the findings show a negative connection between innovation and carbon emissions giving us to look that countries should encourage to invest in innovation development such as creating many patent applications to promote economic growth as well as help reduce environmental degradation and combating climate change. Besides, the negative relationship between CO₂ emissions and trade openness for high-income countries suggests that some environmental-related policies well have been implemented in such countries, as a result of more effective transportation industries. In contrast, for middle- and low-income countries, trade openness witnessed a positive influence on environment damage. Because environmental policies have not been effectively implemented in these countries, therefore, in these countries, it is necessary to encourage the use of renewable energy as an alternative to fossil fuels for commercial development activities. The efficient use of natural resources and promotion of the efficient use of energy resources is essential for sustainable economic development and climate change reduction in these countries. Lastly, the findings indicated a positive impact of tourism and environmental degradation. Therefore, tourism development-related policies could be encouraged to enlarge the use of renewable energy for tourist-related activities which can be beneficial for the quality of the environment and nation economy development. In addition, countries should reorient their tourism development policies and environmental protection policies in the post-COVID-19 era, promoting sustainable tourism is essential for achieving sustainable economic growth. In short, economic growth and attracting foreign direct investment are necessary for each country's economy development, but to achieve the goal of sustainable economic development, the policymakers need to promote energy efficiency and renewable sources consumption to reach COP26 agreements. Besides promoting technological innovation should be encouraged for climate change reduction.

Although this study has found empirical evidence about the impact of renewable energy consumption, trade openness, international tourism, innovation on carbon dioxide emissions in the world's 53 countries, this study still has certain limitations. This research is only done for 53 countries in the world, further studies can be done for more countries. In addition, future studies may repeat this study to determine whether a causal relationship exists between the variables for the two groups of high- and middle- to low-income countries. Furthermore, future research should also investigate the impact of institutional quality and human capital on greenhouse gas emissions. Assessing two-way effects between variables can also be considered for future studies. Finally, in the context of the Covid 19 epidemic is still unpredictable, the economic policy uncertainty index (EPU) has a certain effect on the number of tourists, so future studies may also investigate the impact of the economic policy uncertainty (EPU) index on CO₂ emissions through tourism demand.

Disclosure statement

Authors bear no conflict of interest in any part of the paper including designing, results, interpretations, and policy implications. No third party is involved in the design of the study; in the collection, analyses, or interpretation of data; in writing of the manuscript, or in the decision to publish the results.

Additional information

Funding

This research is funded by the University of Economics and Law, Vietnam National University, Ho Chi Minh City, Vietnam.

Notes

1 https://www.un.org/en/climatechange/raising-ambition/renewable-energy

2 http://data.worldbank.org/indicator/NY.GDP.PCAP.CD