© The Author(s) 2024 This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License Vol. 13, No. 1 (2024), pages 85-102 https://doi.org/10.17979/ejge.2024.13.1.9788 Submitted: June 21, 2023 Accepted: December 2, 2023 Published: June 6, 2024 Article Tourism, growth, and carbon emissions in Sub-Saharan Africa: a balancing act Merith Ifeoma Anaba,1,* Jayanthi R. Alaganthiran,2 Kafilah Lola Gold,3 Folorunso Obayemi Tamitope Obasuyi 4 1 University of Malaya, Malaysia; Veritas University Abuja, Nigeria. 2 University of Malaya; CABI, P.O.BOX 210, 43400 UPM Serdang, Selangor, Malaysia. 3 DSI/NRF South African Research Chair in Industrial Development, University of Johannesburg, South Africa; Kwara State College of Education, Ilorin, Nigeria 4 Bamidele Olumilua University of Education Science and Technology, Ekiti, Nigeria. *Correspondence: merithifeoma@yahoo.com Abstract. Tourism is one of the major determinants of global economic growth, creating jobs within the sector, and Africa is no exception. The target of the sub-Saharan African (SSA) countries is to consider tourism as an alternative means of economic expansion. However, tourism is a means of environmental imbalance. This study investigates the complex relationship between tourism, economic growth, and carbon emissions in 47 Sub-Saharan African countries from 2005 to 2020. While economic growth significantly increases carbon emissions, tourism revenue shows a potential mitigating effect. Trade openness also contributes to emissions, while employment shows a negative correlation. These findings highlight the need for stricter environmental regulations and policies that leverage the region's labor surplus for sustainable tourism practices. Implementing such measures is crucial for minimizing the environmental damage associated with foreign direct economic activities and ensuring long-term sustainability. Keywords: tourism revenues; carbon dioxide emissions; economic growth; trade openness; Sub-Saharan Africa JEL classification : Z330, Q540, O4, F100, O55 1. Introduction Tourism affects the economy in three major ways, namely, its contribution to growth, leisure and carbon emissions (Wijesekara et al., 2022; Du, Lew, & Ng, 2016; Haller et al., 2021). Regarding growth, tourism destinations increase huge revenue through investment in several sectors of the industry. Also, globally, evidence shows that the tourism industry remains one of the most important contributors of carbon dioxide emissions to the environment (Chen, Thapa, & Yan, 2018; Jong, Soh, & Puah, 2022). This has been due to the normative effects that tourism usually brings to the environment. Predominantly, given the tourism industry's various sectors, including hotel and https://creativecommons.org/licenses/by-nc/4.0/ 86 Anaba et al. lodging, aviation and airline, tourist attractions, and food and drink, the tourism industry is known to be extremely energy-dependent (Adedoyin, et al., 2021). Therefore, tourism activities are known for their high cause of climate change because of environmental degradation and a negative rise in energy consumption (Gyamfi, et al., 2021a; Kyara et al., 2022). Tourism has continued to have direct contributions to the growth of the world economies through tourist arrivals and receipt which could have effect on the physical environment. In addition, tourism contributes to economic growth and reduces poverty (Kyara et al., 2022) However, the complexity of the relationship between environmental sustainability and tourism has continued to be explored in diverse ways among scholars (Pal & Mitra, 2017). Africans have been facing excessive carbon emissions, which are scientifically and empirically proven to contaminate the atmosphere and harm human beings and the natural environment in Sub-Sahara Africa (SSA). One of the sustainable development goals (SDG’s) is focused on climate change. Thus, there is a need to research and identify numerous solutions to combat climate change issues effectively among SSA countries. Across countries, carbon dioxide emissions are continuously causing externalities issues to human existence and environmental inequality. For example, previous studies confirmed that air pollution accounted for 1.1 million deaths across African countries in 2019 (Fisher et al., 2021) and approximately 7 million premature deaths in 2017 in Eastern Africa (Wipfli et al., 2021) as well as bronchial asthma increased due to air pollution among SSA countries (Ku et al., 2021). It has been empirically established that the African continent is suffering because of contaminated air (Fisher, et al., 2021). The particulate matter (PM) 2.5 was highly recorded in Kampala and Uganda (Awokola et al., 2020) and the dispersion modelling and spatial analysis confirmed that PM10 concentrations were higher during the day and eventually distributed to wide areas at night (Tshehla & Wright, 2019). In particular, economic indicators have an impact on carbon dioxide emissions. In Africa alone, empirical research has confirmed that foreign direct investment, economic growth, tourism and governance positively increased carbon dioxide emissions (Agyeman, et al., 2022). Similarly, findings by Djellouli et al. (2022) reveal that economic growth and foreign direct investment among twenty SSA countries determined carbon dioxide emissions between 2000 and 2015. Besides, low socioeconomic status caused women to be exposed to poor air quality in Adama, Ethiopia (Flanagan et al., 2022). Furthermore, Al-mulali & Binti Che Sab (2012) contend that energy consumption in the thirty SSA economies played a significant role in boosting economic growth and financial development, but at the cost of excessive pollution. Along with various other factors, the level of income generally correlates with an increase in CO2 emissions. Delving into these previous studies, however, as a matter of importance, these existing findings on the relationships between CO2 emissions and economic growth in the SSA countries, have rarely produced any study on tourism's impact on economic expansion in the region. Simply, examining the relationships between tourism revenue, economic growth and carbon dioxide is uncommon among scholars despite the gains of growth as well as contributions of tourism to growth. Thus, this study examines the impact of tourism revenue and economic growth on carbon dioxide emissions in the 47 SSA countries. Specifically, the study examines the linear associations among tourism arrivals, receipts, environmental pollution and economic growth in the SSA countries’ tourism industry. The study estimates the correlation between carbon dioxide and confounding Tourism revenue, economic growth and carbon dioxide emissions nexus variables, including GDP, EC, Tourism revenue, Trade Openness and labour surplus. From the empirical findings of this study, the selected SSA countries’ economic growth significantly contributed to carbon dioxide emissions. Moreover, African countries’ tourism revenue shows a potential indication that this sector could reduce carbon dioxide emissions. The rest of this study are structured as follows: the first section reviews previous research on the topic that is both current and pertinent; section three discusses empirical technique; section four gives the findings and discussion; and section five closes the study. 2. Literature review 2.1. Tourism and economic growth In most nations globally, including Africa, the tourism sector is without a doubt one of the energy- intensive sectors that significantly boosts national GDP and creates jobs. To strengthen an economy, many developing nations, especially those in SSA, are increasingly focusing on the growth of tourism (El Menyari, 2021; Adedoyin & Bekun, 2020). Kyara et al. (2022) used time series data from 1995 to 2017 to examine the environmental impacts of tourism growth in Tanzania. The study employs Autoregressive Distributed Lag Bounds Testing, Vector Error Correction Model (VECM), and Granger causality test for analysis and the Wild Bootstrap approach to check the accuracy of the computed statistics. Hence, the VECM Granger causality test indicates that environmental degradation in Tanzania is compacted by foreign tourist arrivals and trade openness, while it is accelerated by urbanization and primary energy use. Furthermore, although the variables have long-term cointegration, the environmental Kuznets curve hypothesis was not ascertained in Tanzania. Su et al. (2023) study examined the environmental effect of financial stability in Iceland from 1995 to 2019. Using the nonlinear ARDL and Fourier-based techniques. The results of nonlinear bound tests and Fourier-based approaches show that CO2 emissions and financial stability frequently cointegrate. The NARDL results demonstrated that a positive variation in financial stability reduces CO2 emissions, whereas a negative variation has no effect. Additionally, positive income variation causes CO2 emissions, whereas negative income variation has no influence on CO2 emissions. Conversely, a decrease in trade openness has increasing effects on CO2 emissions, whereas an increase in CO2 emission-mitigation effects is a positive development. Also, Sun et al. (2022) reviewed 81 Environmental Kuznets Curve research published between 2013 and 2021 to determine whether tourism has an impact on carbon emissions and its consequences on development plans. However, none of the researchers examined international aviation emissions. Nonetheless, the results show that there is a paradoxical relationship between tourism and emissions, with divergent findings reported across nations, income levels, and the sector's economic significance. Indicating the need to critically re-evaluate the ways in which tourism and carbon are related, as well as the techniques employed in empirical research. The World Travel and Tourism Council (2019) confirm that the tourism industry provides 330 million job opportunities for the world generally and increases the world gross domestic product (GDP) by US$8.9 trillion, representing 10.3% of the world (GDP). This has shown that tourism creates jobs and eventually increases the economic growth of such nations. According to the European Union 88 Anaba et al. (EU, 2012), tourism plays an important role in most sectors of the economy, including the creation of jobs and a source of economic development. Also, it contributes positively to a country’s balance of payment. An increase in both international and domestic tourism arrivals has boosted the country’s revenue, which has indirectly led to growth in the energy consumption sector (Dogru & Bulut, 2018). For instance, the channel of growth has been by increasing tourism activities such as transportation facilities and hotel stays. However, if a country is an export industry, tourism creates export revenues and contributes to economic growth. Furthermore, a country can experience an economic growth process mainly through tourism activities (Nyasha et al., 2021). In comparison, China and Turkey have experienced tourism-led growth over time, while Russia and Spain experienced growth-led tourism (Ongan, et al., 2017). 2.2 Tourism and environmental degradation In light of the aforementioned, Adedoyin and Bekun (2020) study indicated that, as of 2019, the tourist sector had surpassed the building sector to rank among the top environmental polluters in the world, accounting for 8% of global CO2 emissions. However, the transportation sector, like air transportation, contributes significantly to the increase in energy consumption and emissions. The study by Isik, et al. (2020) found that tourism has a negative impact on the environment in Greece. Suess et al. (2020) found that tourism has both negative and positive impacts on emissions in different countries. Using the World Bank data indicators database from 1990 to 2016, Bekun (2022) investigates the impact of economic growth, investment in the energy sector, non-renewable energy, renewable energy, and renewable energy on CO2 emissions in India. The study employs fully modified least squares (FMOLS), dynamic least squares (DOLS), and canonical cointegration regression (CCR) techniques, And the empirical analysis indicates that a positive relationship exists between CO2 emissions and non-renewable and GDP growth. However, a negative relationship exists between CO2 emissions and renewable energy. For the Granger analysis, the findings show a one- way causality among renewable energy and CO2 emissions, economic development, and energy investment. Using a different technique and sector-specific findings of Bekun et al. (2019) from the Pooled Mean Group-Autoregressive Auto regressive distributive lag model (PMG-ARDL estimations show that “overdependence on natural resource rent affects environmental sustainability if conservation and management options are ignored”. This is also reflected in the non-renewable energy consumption and economic growth, both of which contribute to carbon dioxide emissions in the panel countries. Kiracı and Bakır (2019) used panel data covering low, lower-middle, upper-middle- and high-income countries from 1995-2015. A fully modified ordinary least square (FMOLS) was used, and it was found that tourism and corruption are the main contributors to C02 emissions. However, the contributions of the C02 emissions have more effect in the lesser-income nations than in high- income nations. Furthermore, Anser et al. (2020) utilize Dynamic GMM and Granger causality estimate with panel data spanning from 1665-2018 covering 132 countries comprising Algeria, Albania, Angola, Benin, Belgium, Chile, Canada, Egypt, Ecuador, Georgia, Ghana and Iraq etc. The Tourism revenue, economic growth and carbon dioxide emissions nexus result shows that the cost incurred on C02 emission decreases inbound tourism and international tourist receipts. In other words, the C02 emission translates to increasing international tourist expenditure in these selected countries. In Nyashay et al. (2020), panel data covering SSA from 2002- 2018 was used to explore tourism and economic growth using the generalized method of moments (GMM). The findings reveal that tourism expenditure has a negative effect on economic growth, while tourism receipt has a positive effect on economic growth. While tourism receipts are robust in low- income countries, tourism expenditures are robust in the middle-income sub-sample countries. Also, using Panel smooth transition regression (PSTR), as recommended by Nosheen et al. (2021), discover the link between tourism, growth, and environmental degradation, which is important in the current era. The study demonstrates that when tourism development declines, environmental deterioration rises. Second, as tourism grows, the environment is not being harmed as much. However, as the populace increases, the environment degenerates. While urbanisation has a temporary and large positive impact on environmental degradation, it was later changed to negative by using panel data from 1995-2017 covering 20 countries Austria, Canada, China, France, Germany, Greece, Hong-Kong, Italy, Japan, Malaysia, Mexico, Netherland, Poland, Portugal, Russian, Federation Spain, Thailand, Turkey, and USA. 2.3. Tourism and economic indicators It is imperative to understand that foreign direct investment (FDI) in tourism-hosting countries usually affects tourism development. Hence, by testing for long and short-run effects and examining the environmental Kuznets Curve using time series data spanning from 1971 through 2012 in India and China, Pal and Mitra (2017) establish a long-run effect on economic activity and trade openness. Also, the short-run effect of energy use on C02 emission was discovered to be positive. Furthermore, instead of the U-shaped expected, the result placed emphasis on the N-shaped relationship between C02 emission and per capita GDP. They argued in favour of the sampled countries that, as per capita GDP increases, the C02 emission also increases, but CO2 emission decreases as per capita GDP reaches a certain level. Yusuf et al. (2023) examined Australia’s energy use and its relatedness with trade liberalisation, CO2 emission, GDP and industrialisation using autoregressive distributed lag (ARDL) technique and a vector error correction model (VECM). The findings indicate that energy use and GDP positively and significantly affect CO2 emissions. However, trade liberalization has a significantly adverse influence on emissions. Likewise, the relationship between industrialisation and CO2 emissions is insignificant but positive in Australia. Bekun et al. (2022) examine the function of international tourism influx on E7 countries, an EKC environment economy. Its nexus on income, trade, and institutional quality on CO2 emission is also assessed from 1995 to 2016. The study utilised data from the World Bank Development Indicators database and employs second generational panel estimator with the Driscoll-Kraay robust estimator to analyse the data. The findings indicate that non-renewable energy and per capita GDP diminish the environment quality. As well, CO2 emissions in E7 economies is due to an increase in non-renewable energy and tourism influx. The quality of institutions improves the quality of the environment. Qin et al. (2023) employ 90 Anaba et al. the time-varying parameter-stochastic volatility-vector auto-regression (TVP-SV-VAR) model to examine the interactions among blockchain market (BCM), green finance (GF) and carbon neutrality in China (CNP). The study, from June 2017 to August 2022, used weekly time series data to achieve this objective. The results show that blockchain market development creates long-term inducement that has both positive and negative effects on carbon neutrality in China. Green finance development can continually enhance carbon neutrality, though not at the same pace as the blockchain market. Likewise, green finance has a positive influence on BCM, as compared to BCM on GF. In Shaheen et al. (2019) and Ozpolat et al. (2021), both studies discovered a link between rising FDI as a result of tourism, energy use, and CO2 emissions. The authors reaffirmed that there is a convincing relationship between the growth of foreign tourism and an increase in energy consumption, which has a direct detrimental outcome on the environment. In a similar study, Hanif (2018) examines the relationship between environmental degradation and GDP in sub-Saharan Africa using GDP, consumption of fossil fuels, sustainable energy, and carbon emissions. It was found that South Africa, Madagascar, Nigeria, Mauritius, Ghana, Uganda, and Cameroon are among the top- ranked developing nations of Sub-Saharan Africa, increasing environmental pollution, CO2 and GHG emissions. This correlates with The World Health Organization (2019) position that a half million people die each year in sub-Saharan Africa as a result of this increase in environmental pollution. This accounts for the increase in serious biological damage. Evidence also revealed a mixed relationship between CO2 and other environmental pollutants, energy use, GDP, and foreign direct investment (Bataka, 2020). Furthermore, considering the importance of CO2 emissions on economic growth, particularly in African regions, Kyara et al. (2022) used time series data from 1995 to 2017 to examine the environmental impacts of tourism growth in Tanzania. However, as important as their study is, the scope is limited to a single country in the African region solely. Other recent studies of Sun et al. (2022), Su et al. (2023), Su et al. (2022), Qin et al. (2023), Bekun et al. (2022), Yusuf et al. (2023), and Bekun (2022) emphasise the importance of CO2 emissions, trade liberalisation, technology innovation, blockchain market, industrialisation and energy usage on economic growth and tourism in other regions and countries, except on the tourism's impact on economic expansion in African region. Also, Bataka (2021) applies the panel specification with the estimation approach by Hoechle. It is used to account for auto-correlation spatial dependency and heteroscedasticity. Plane data were used to cover the Sub-Saharan African countries from 1980 to 2017, and C02 emission data was sourced from the emission database of the Global Atmospheric Research Base (CEGAR). However, the impact of energy consumption on tourism cannot be underestimated because the policy perspective clarifies the significant effect of energy consumption on economic growth, because it serves as a preliminary stage for industrial society. It provides facilities for household consumption, industrial production, resource mining, and transportation sectors, which have shown that economic growth and development cannot be achievable without the proper significant of energy. Hence, this study aims to look into the following objectives: 1. To examine whether economic expansion in the SSA countries contributes to carbon dioxide emissions 2. To investigate whether the tourism industry’s revenue contributes to the carbon dioxide emissions in the SSA countries.3. To analyse the extent of tourism inducement on environmental pollution in the SSA countries. 4. To determine if labour-intensive technique through employment generation contributes to the rising of Tourism revenue, economic growth and carbon dioxide emissions nexus greenhouse emissions in the SSA countries. The following section of this paper discusses and explains the empirical techniques and econometrics used in this investigation's analysis and presentation. Therefore, it is deemed crucial to comprehend how tourism revenue and economic growth contribute to carbon dioxide emissions in Sub-Saharan African countries. Thus, within the context of the SSA economies, this study provides a clearer empirical analysis of the topic. 3. Methodology 3.1 Data and source This study explores the contributions of tourism revenue and economic growth impacted on carbon dioxide in the 47 SSA countries (see Appendix of the list of countries used in the study). In other words, the study examines the nexus between greenhouse gas emissions and economic growth between 2009 and 2020 using a panel data set. The study used CO2 emissions, trade openness, international tourism revenue, GDP, tourism receipts, fossil fuels, electricity generation and labour force in SSA countries. The labour force was selected because there is labour surplus in the SSA countries that could be used instead of capital-intensive equipment capable of generating emissions. The carbon dioxide emission in this study represents greenhouse gas emission, and it measures thousands of tons. In detail, carbon dioxide emissions are derived from burning fossil fuels and consumption of solid, liquid and gas fuels and gas flaring. The study focused on the economic growth variable, GDP, which is measured in billions of U.S. dollars. Moreover, the study introduced regressors, namely energy consumption, tourism revenue, trade openness, and labour. Hence, the data were downloaded from the Global Economy website on the stated indicators for 47 SSA countries. The list of the 47 sampled countries of the SSA is in Appendix I. Table 1 describes the variables’ symbols, measurements and expected signs. Table 3. Operationalization of factors Variables Description Measurement Expected sign Dependent Variable CO2E Carbon Dioxide Emissions thousands of tonnes kt Independent Variables: GDP Gross Domestic Product billions of U.S. dollars + EC Fossil fuels electricity generation billion kilowatt hours + TorismR International tourism revenue million USD - TradeO Trade Openness percent + Labour Labour force million people + 92 Anaba et al. Furthermore, previous studies have shown neoclassical growth theory, which confirmed economic growth association with environmental degradation, namely air pollution and water pollution (Gao, et al., 2021). Also, econometric analysis employs the Cobb-Douglas production function to examine the effect of economic growth on carbon dioxide emissions (Chaabouni & Saidi, 2017; Gao, et al., 2021). Thus, this study replicated the above similar theoretical framework to examine economic growth and carbon dioxide emissions. Again, the prior study focused on only the top ten tourism countries (Destek & Aydın, 2022). Although the tourism sector has contributed to economic growth, it is a factor that causes environmental degradation through many channels. Unlike other studies, this study classified SSA countries into regions, namely Eastern Africa, Middle Africa, North Africa, South Africa and Western Africa. This is to cover the gap in Ohajionu et al. (2022) who urged future researchers to examine potential variables that might cause environmental degradation in SSA countries. Model estimation This section explains various econometrics estimation techniques to examine the relationships between the tourism industry and greenhouse emissions. There are three level estimations in this study. First, the study determines the correlation between the examined variables to understand the extent to which each variable affects one another. Also, the basic correlation analysis provides some basic hints on variable association regardless of the dependent and independent variables' status. Second, the study analysed pooled ordering least square (POLS) which allows small variation in estimation that obeys best linear unbiased estimation. In this instance, we assumed that there are no unobservable entity-specific effects. Simply, we proposed that all the data set of member countries of SSA had the same underlying characteristics during the period. Finally, the study employed Least Square Dummy Variable (LSDV), with the assumption that each region in the SSA countries could control its carbon dioxide emission levels differently. Correlation Analysis This study performed pairwise correlation analysis to determine the correlation coefficient range among variables and identify coefficient signs. Pooled OLS The study applies the pooled panel Ordinary Least Square (OLS) to measure the overall influence of economic growth on carbon dioxide emissions without controlling for countries' heterogeneity. The study considered the Poole OLS, considering that all the data set had the same underlying characteristics during the period. The Pooled OLS model is specified in equation [1]. 𝐶𝐶𝐶𝐶2𝐸𝐸𝑖𝑖𝑖𝑖 = 𝜙𝜙𝑖𝑖 + 𝜙𝜙1𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 + 𝜙𝜙2𝐸𝐸𝐶𝐶𝑖𝑖𝑖𝑖 + 𝜙𝜙3𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 + 𝜙𝜙4𝑇𝑇𝐶𝐶𝑖𝑖𝑖𝑖 + 𝜙𝜙5𝐿𝐿𝑖𝑖𝑖𝑖 + ɛ𝑖𝑖𝑖𝑖 [1] Tourism revenue, economic growth and carbon dioxide emissions nexus In Model 1, 𝐶𝐶𝐶𝐶2𝐸𝐸𝑖𝑖𝑖𝑖 is the dependent variable and 𝜙𝜙𝑖𝑖is the intercept, 𝜙𝜙denotes slope coefficients and independent variables are 𝐺𝐺𝐺𝐺𝐺𝐺 represents Gross Domestic Product, 𝐸𝐸𝐶𝐶 denotes Energy Consumption, 𝑇𝑇𝑇𝑇 refers Tourism Revenue, 𝑇𝑇𝐶𝐶 describes Trade Openness and 𝐿𝐿is Labor. On the other hand, pooled OLS estimation subjects biased and inefficient outcomes. Finally, the study employed the Least Square Dummy Variable (LSDV). We assumed that each region in SSA could be determining carbon dioxide emission levels differently. Also, different SSA region groups might have different patterns regarding economic growth, energy consumption, and tourism revenue generation. This section is dedicated to formulating LSDV estimation methodology that pooled 47 SSA countries data information from qualitative variables in econometric model. The model contains dummy variables to measure qualitative influence by coding the different possible outcomes with continuous variables. The dummy variable has dichotomized the possible outcomes and assigned the values of 0 and 1. Thus, SSA regions have been coded as 𝐺𝐺𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖= 1 for Middle Africa, 0 otherwise; 𝐺𝐺𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖= 1 for Northern Africa, 0 otherwise; 𝐺𝐺𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖= 1 for Northern Africa, 0 otherwise.𝐺𝐺2012𝑖𝑖𝑖𝑖= 1 for 2012, 0 otherwise and 𝐺𝐺2018𝑖𝑖𝑖𝑖= 1 2018, 0 otherwise. Study has derived regression model that included dummy variables as below 𝐶𝐶𝐶𝐶2𝐸𝐸𝑖𝑖𝑖𝑖 = 𝜙𝜙𝑖𝑖 + 𝜙𝜙1𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 + 𝜙𝜙2𝐸𝐸𝐶𝐶𝑖𝑖𝑖𝑖 + 𝜙𝜙3𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 + 𝜙𝜙4𝑇𝑇𝐶𝐶𝑖𝑖𝑖𝑖 + 𝜙𝜙5𝐿𝐿𝑖𝑖𝑖𝑖 + 𝜙𝜙6𝐺𝐺𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 + 𝜙𝜙7𝐺𝐺𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖 + 𝜙𝜙8𝐺𝐺𝐷𝐷𝐷𝐷𝑖𝑖𝑖𝑖+ 𝜙𝜙9𝐺𝐺𝑊𝑊𝐷𝐷𝑖𝑖𝑖𝑖 + 𝜙𝜙10𝐺𝐺2012𝑖𝑖𝑖𝑖 + 𝜙𝜙11𝐺𝐺2018𝑖𝑖𝑖𝑖 + ɛ𝑖𝑖𝑖𝑖 [2] The descriptive statistics of the data collected is presented in Table 2. Table 2. Descriptive statistics 4. Results and discussions The results and discussions section is divided into three sub-sections, namely, the Pairwise Correlation coefficient and pooled OLS and LSDV estimations. Variable Obs Mean Std. Dev. Min Max CO2 658 1.002 1.907 .02 11.68 GDP 735 31.342 74.52 .14 546.68 EC 713 6.316 32.421 0 233.05 TourismR 551 4.455 7.292 0 42.18 TradeOp 683 73.996 37.632 9.96 311.35 Labour 736 7.727 10.849 .05 63.23 The descriptive statistics show that the data used in this study is unbalanced. This was accommodated by the software used. 94 Anaba et al. 4.1 The Pairwise Correlation coefficient This sub-section presents the results of the pairwise correlation coefficient and the p-value to understand the significant level of the relationship. The results id presented in Table 3. Table 3. Pairwise correlations Variables (1) (2) (3) (4) (5) (6) (1) CO2 1.000 (2) GDP 0.306* 1.000 (0.000) (3) EC 0.534* 0.683* 1.000 (0.000) (0.000) (4) TourismR 0.464* -0.154* -0.048 1.000 (0.000) (0.000) (0.259) (5) TradeOp 0.401* -0.215* -0.078* 0.591* 1.000 (0.000) (0.000) (0.046) (0.000) (6) Labour -0.042 0.702* 0.245* -0.210* -0.366* 1.000 (0.286) (0.000) (0.000) (0.000) (0.000) * Significant at p <0.05. We analysed pairwise correlation analysis to identify the strength of the partial relationship between carbon dioxide emissions, economic growth, energy consumption, tourism revenue, trade openness and labour. Table 3 presents pairwise correlation results. Thus, we have possible and negative correlations across variables. For example, energy consumption has shown a positive and substantial correlation with carbon dioxide emissions and GDP because EC coefficients lie between 0.50 to 0.69. Besides, tourism revenue has shown a positive coefficient at 0.464 and a moderate correlation with carbon dioxide emissions. However, tourism revenue has indicated negligible and negative correlation with GDP as well as insignificant correlation with energy consumption. 4.2 Pooled OLS results and discussion Table 4 presents pooled OLS model results that comprised 47 SSA countries. In detail, pooled OLS estimation has shown five statistically significant associations among economic growth, energy consumption, tourism revenue, trade openness and labour. Although the results have passed diagnostic tests, the model suffered from some limitations. We considered the limitations to be insufficient to explain the carbon dioxide emissions phenomenon without compromising the heterogeneity of the countries. Hence, the finding shows that a 1% increase in GDP increases carbon dioxide emissions by about 0.004%. Basically, growth often comes with intensive use of capital, where electricity and fossil fuels are considerably used. Earlier, we hypothesized that economic growth will increase carbon dioxide emissions in SSA countries, as validated in previous findings. The positive association is in line with the findings of Jiang et al. (2022), Liu et al. (2022) and Ouyang et al. (2019). The study has supported prior literature and neoclassical growth theory that decomposed economic growth that consisted of additional factors that could reduce the Tourism revenue, economic growth and carbon dioxide emissions nexus environmental quality in SSA countries. However, prior literature lists have always concentrated on non-linear estimation that emphasized EKC theory in countries such as China and OECD countries. Furthermore, researchers have focused on the causality between air pollution indicators such as PM 2.5 and GDP. This study has added to existing literature that 47 SSA countries air quality was reduced because of their lower economic activities. The fact that developing countries have fewer industrial activities compared with developed countries, their little economic activities have always neglected environmental quality, thereby breaching environmental law. As such, the SSA countries economic activities, legally or illegally, are expected to emit carbon dioxide that affects the environment. Table 4. Linear regression: pooled OLS CO2 Coef. St.Err. t-val p-val [95% Conf Interval] Sig GDP 0.004 0.001 4.91 0.000 0.002 0.005 * EC 0.03 0.001 25.23 0.000 0.028 0.033 * TourismR 0.03 0.007 4.61 0.000 0.017 0.043 * TradeOp 0.008 0.001 7.66 0.000 0.006 0.01 * Labour -0.03 0.004 -7.04 0.000 -0.039 -0.022 * Constant 0.086 0.083 1.03 .305 -0.078 0.249 Diagnostic Tests: Heteroscedasticity 12.11 Multicollinearity 2.40 Normality pass Mean dependent var 0.828 SD dependent var 1.454 R-squared 0.809 Number of obs 481 F-test 402.993 Prob > F 0.000 Akaike crit. (AIC) 939.076 Bayesian crit. (BIC) 964.132 *Significant at p<.05 The SSA African countries energy consumption reduced air quality. Again, the results in Table 4.2 showed that a 1% increase in fossil fuel consumption in the sampled SSA countries increased carbon dioxide emissions by 0.03%. In other words, the expected signs and findings show that fossil fuel usage deteriorates environmental quality (Chien, et al., 2021; Khan, et al., 2016). Again, and in no small measure, energy used by industries and households will emit harmful gases into the air, thereby reducing air quality. To this end, we introduced another important variable, i.e. electricity consumption, which used fossil fuel to generate power supply. The finding reveals that the higher the electricity consumption, the higher the air pollution. Further, on the assumption that cross-country trade activities will cause greenhouse gas emissions, we found that a 1% increase in trade openness increases carbon dioxide emissions by about 0.008%. Although the contribution of trade openness to greenhouse emissions is extremely reduced, the positive relationship between trade openness and CO2 is consistent with previous findings (Kukla-Gryz, 2009; Lin, et al., 2014). Most importantly, the SSA low energy consumption contributes more to carbon dioxide emissions (Agyeman, et al., 2022), as reflected in the developed countries, is accountable to the low carbon dioxide emissions in the sampled countries of SSA. Finally, there are two positions regarding the estimated labour variable. First, labour variable was introduced to understand that labour-intensive production might be better than using 96 Anaba et al. capital-intensive techniques. The more equipment used, the higher the emissions. Whereas using labour intensive techniques would reduce greenhouse emissions. Second, we argue that urbanization tends to increase emissions through pollution channels. The migration from rural to urban centres to join the accumulation of industries effect, CO2 would increase considerably. However, the result showed a negative relationship. Although at a minimal percentage, a unit increase in the labour indicator caused 0.03% reduction in carbon dioxide emissions in the selected SSA countries. This rather supports the labour-intensive technique of production (Li et al., 2020) against the assumption that labour increases air pollution. 4.2 Least Square Dummy Variable (LSDV) approach This section presents the results of the Least Square Dummy Variable (LSDV). Hence, we performed LSDV regression as presented in Table 5. Table 5. LSDV Regression results In Table 5, we computed the LSDV to explain the increment in carbon dioxide emissions levels in SSA countries between 2005 and 2020. As shown there, there are three parts of the LSDV analysis comprising continuous variables association, countries interaction with economic growth and dummy for years. First, the LSDV estimation produced results similar to those of pooled OLS except for the tourism revenue. The tourism revenue supported the earlier hypothesis that the tourism CO2 Coef. St. Err. t-value p-value [95% Conf Interval] Sig GDP 0.007 .003 2.54 0.012 .002 .013 ** EC 0.047 .008 5.77 0.000 .031 .063 *** TourismR -0.039 .013 -3.04 0.003 -.064 -.014 *** TradeOp 0.001 .001 1.40 0.163 0 .003 Labour -0.057 .014 -3.92 0.000 -.085 -.028 * region1#co: base 1 0 . . . . . Middle Africa -0.007 .003 -2.00 0.047 -.013 0 * East Africa -0.005 .006 -0.83 0.405 -.017 .007 South Africa -0.01 .003 -3.06 0.002 -.017 -.004 * West Africa -0.009 .003 -2.97 0.003 -.015 -.003 * 2009b 0 . . . . . 2010 0.016 .036 0.46 0.645 -.054 .086 2011 0.035 .036 0.95 0.341 -.037 .106 2012 0.061 .037 1.67 0.095 -.011 .134 * 2013 0.078 .038 2.06 0.04 .003 .152 * 2014 0.097 .038 2.52 0.012 .021 .172 * 2015 0.091 .039 2.34 0.02 .014 .168 * 2016 0.112 .04 2.78 0.006 .033 .191 * 2017 0.147 .042 3.49 0.001 .064 .229 * 2018 0.173 .045 3.86 0.000 .085 .262 * Constant 0.964 .143 6.75 0.000 .683 1.245 * Mean dependent var 0.854 SD dependent var 1.457 R-squared 0.214 Number of obs 342 F-test 4.316 Prob > F 0.000 Akaike crit. (AIC) -373.141 Bayesian crit. (BIC) -300.280 *Significant at p<.05. Tourism revenue, economic growth and carbon dioxide emissions nexus sector could improve environmental quality. Simply, tourism revenue demonstrates a negative relationship with carbon dioxide emissions at 1% significance level. it shows that 1% increase in tourism revenue decreased carbon dioxide emissions in about 0.039%. Second, we decompose the SSA countries into regions to determine the regional dummy interaction with economic growth. Unlike previous studies that lumped a large list of countries into single estimation, we categorized SSA countries into sub-regions comprising Eastern Africa, Central Africa, Southern Africa and Western Africa where Eastern African region remains the base dummy to avoid dummy trap. Table 4.3, the constant value explains that carbon dioxide emissions from the controlled group Eastern Africa is 0.964. The GDP coefficient has shown marginal effects of economic growth for the control group Di= Eastern Africa. In other words, Eastern Africa contributed an additional 0.07 carbon dioxide emissions mt / % for an additional year of economic growth. Likewise, the Central Africa interaction with economic growth demonstrated that countries from this region emitted carbon dioxide [0.964- (0.007=0.957 units) for extra years of economic growth. As such, Central Africa region emitted carbon dioxide lesser units of 0.007 below the 0.964 threshold. Moving to South Africa region, considering the South Africa region interaction with GDP, we established that the region emitted [0.964- (0.01=0.95 units] for extra years of economic growth. This shows that South Africa region emitted carbon dioxide lesser units of 0.01 below the threshold. Finally, in the Western African region, the economic growth interaction with West African countries indicated that those countries from the region emitted [0.964- (0.009=0.955 units] for extra years of economic growth. This simply shows that the West African countries’ carbon dioxide emissions contributed lesser units 0.01 below the threshold. Third, we introduced a dummy year variable to explain the period that the carbon dioxide emissions actually hit the selected countries. We made 2009 the base year. Hence, the dummy year estimation reveals that the average carbon dioxide emissions were 0.016 mt / % higher in 2010 but not significant. After that, in 2012, carbon dioxide emission was given as 0.061, which was higher than 2010. In general, the coefficients of the dummy year have indicated a statistically significant increment in carbon dioxide emissions in 2013 (0.078 mt), 2014 (0.097 mt), 2015 (0.091 mt), 2016 (0.11 mt), 2017 (0.147 mt) and 2018 (0.173 mt). While other dummy years’ results were high, the 2015 revealed that carbon dioxide decreased slightly to about 0.091, but later increased to 0.178 in 2018. This is to understand that the dummy year coefficients explained that holding dependent and independent variables constant, on average, carbon dioxide had greater emissions between 2012 and 2018 in the SSA countries. 5. Conclusions and policy implications The study investigates the relationship between tourism’s revenue, economic growth and carbon dioxide emissions in the SSA countries. It further decomposes the SSA region into sub-regions and examines countries’ interaction with economic growth and dummy years to understand each region's contributions to the accumulation of greenhouse emissions in SSA countries. Finally, the study determines the labour force involvement in greenhouse emissions across the sampled countries. As such, four independent conclusions were drawn. First, although the economic activities 98 Anaba et al. of the SSA countries are low compared with some Asia and Europe economies, the revenue realized from the tourism industry contributes greatly to the total emissions of the region. This is accountable to the reinvestment of growth gains into the tourism industry, which triggers the uncontrolled fossil fuels being used around the year and the trade openness through FDI channel. However, the world market competition does not and should not discourage considerable investment into the tourism industry in the SSA countries, otherwise, the region would remain undeveloped. Second, with the decomposition of the region into sub-regions, we conclude that, although at varying degrees, the sub-regions jointly contributed to the high rate of greenhouse emissions in the SSA countries. For example, southern African countries contributed the highest percentage of greenhouse emissions compared to the other sub-regions due to heavy use of industrial equipment and high use of electricity. While the SSA sub-regions should not restrain from heavy investment into the real sector, the government could integrate into the global value chains for optimal use of equipment and resources. Third, before the digital age (IR4.0), countries relied solely on capital-intensive techniques in the production process. This period of IR3.0 was a means of capital-intensive use of machines that de-emphasized the labour-intensive technique, which created a labour surplus in the long run. ., Although at a minimal rate, we conclude in favour of the labour-intensive technique as the result of this study showed, where it triggered the reduction of greenhouse emissions in the SSA countries rather than the capital-intensive use of production. Fourth, developing sustainable tourism is one of the 2030 sustainable development goals (SDG) has an indirect effect on poverty reduction. This realization that the positive effect of tourism cannot be altered due to the infinitesimal incremental effect of tourism revenue on CO2 in the SSA countries. Thus, rather than curtailing tourism expansion in the SSA countries, the governments of the sampled countries should study those elements to increase emissions by developing or adopting alternatives to emission reduction. For example, if electricity consumption increases CO2 in a country or sub-region, it is imperative that the government consider the installation of Solar resources as a strong alternative policy to electricity consumption to mitigate against excess greenhouse emissions in such a country or sub-region. Finally, the government of the sampled SSA countries should carefully consider acceptance of foreign direct investment (FDI) absorption into the economic system. This is especially necessary that bringing in an industry that would trigger carbon emissions into the country should be technically avoided. Rather, the government may consciously integrate itself into the digital age movement through intensive knowledge transfer in the space/digital exploration and intensive computer educational courses (e.g. Artificial Intelligence (AI), cloud computing, etc.) that allow for digital marketing, business development and physical services and construction. Crucially, the empirical results demonstrated that the economic expansion of African nations had a major impact on carbon dioxide emissions. Furthermore, the tourism industry's potential to reduce carbon dioxide emissions is indicated by the revenue it generates for African nations. Therefore, it is recommended that the countries included in the sample implement stringent policies that prioritize environmental regulations. This will help to mitigate the negative impact of foreign direct economic activities on both the natural and human environments. Tourism revenue, economic growth and carbon dioxide emissions nexus Limitations of the study and future research The study analyses how tourism revenue and economic growth contributed to carbon dioxide emissions in selected 47 (SSA) between 2005 and 2020. other studies should look at how tourism revenue and economic growth contribute to carbon dioxide in more (SSA) countries on a wider range. Although the study employed pooled ordinary least square (OLS) and least square dummy variable (LSDV) as the study econometric technique, we observed that the study did not address the long-run relationships of the variables, which would have attracted panel cointegration. The future study may proceed to test for stationary to detect the best econometric method, such as panel autoregressive distributed Lag (Panel ARDL) model, panel vector error correction model, pooled mean group (PMG), and mean group (MG). References Adedoyin, F.F., Agboola, P.O., Ozturk, I., Bekun, F.V., & Agboola, M.O. (2021). Environmental consequences of economic complexities in the EU amidst a booming tourism industry: Accounting for the role of brexit and other crisis events. Journal of Cleaner Production. 305:127117. https://doi.org/10.1016/j.jclepro.2021.127117 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 Pollution Research. 27(31):38881-38900. https://doi.org/10.1007/s11356-020-09869-9 Agyeman, F.O., Zhiqiang, M., Li, M., Sampene, A.K., Dapaah, M.F., Kedjanyi, E.A.G., Buabeng, P., Li, Y., Hakro, S., & Heydari, M. (2022). Probing the Effect of Governance of Tourism Development, Economic Growth, and Foreign Direct Investment on Carbon Dioxide Emissions in Africa: The African Experience. Energies, 15(13), 4530 https://doi.org/10.3390/en15134530 Al-mulali, U., & Binti Che Sab, C.N. (2012). The impact of energy consumption and CO2 emission on the economic growth and financial development in the Sub-Saharan African countries. Energy, 39(1), 180-186. https://doi.org/10.1016/j.energy.2012.01.032 Awokola, B.I., Okello, G., Mortimer, K. J., Jewell, C. P., Erhart, A., & Semple, S. (2020). Measuring air quality for advocacy in Africa (MA3): feasibility and practicality of longitudinal ambient PM2. 5 measurement using low-cost sensors. International Journal of Environmental Research and Public Health, 17(19), 7243. https://doi.org/10.3390/ijerph17197243 Bataka, H. (2020). Globalization and Environmental Pollution in Sub-Saharan Africa. African Journal of Economic Review. 9(1):191-205. Bekun, F.V. (2022). Mitigating emissions in India: accounting for the role of real income, renewable energy consumption and investment in energy. International Journal of Energy Economics and Policy. https://doi.org/10.32479/ijeep.12652 Bekun, F.V., Adedoyin, F.F., Etokakpan, M.U., & Gyamfi, B.A. (2022). Exploring the tourism-CO2 emissions- real income nexus in E7 countries: accounting for the role of institutional quality. Journal of Policy Research in Tourism, Leisure and Events, 14(1), 1-19. https://doi.org/10.1080/19407963 .2021.2017725 Bekun, F.V., Alola, A.A., & Sarkodie, S.A. (2019). Toward a sustainable environment: Nexus between CO2 emissions, resource rent, renewable and non-renewable energy in 16-EU countries. Science of the total Environment, 657, 1023-1029. https://doi.org/10.1016/j.scitotenv.2018.12.104 Chaabouni, S., & Saidi, K. (2017). The dynamic links between carbon dioxide (CO2) emissions, health spending and GDP growth: A case study for 51 countries. Environmental Research, 158, 137-144. https://doi.org/10.1016/j.envres.2017.05.041 Chen, L., Thapa, B., & Yan, W. (2018). The relationship between tourism, carbon dioxide emissions, and economic growth in the Yangtze River Delta, China. Sustainability, 10(7), 2118. https://doi.org/10.3390/su10072118 Chien, F., Sadiq, M., Nawaz, M.A., Hussain, M. S., Tran, T.D., & Le Thanh, T. (2021). A step toward reducing air pollution in top Asian economies: The role of green energy, eco-innovation, and environmental taxes. Journal of Environmental Management, 297, 113420. https://doi.org/10.1016/ j.jenvman.2021.113420 https://doi.org/10.1016/j.jclepro.2021.127117 https://doi.org/10.1007/s11356-020-09869-9 https://doi.org/10.3390/en15134530 https://doi.org/10.1016/j.energy.2012.01.032 https://doi.org/10.3390/ijerph17197243 https://doi.org/10.32479/ijeep.12652 https://doi.org/10.1080/19407963.2021.2017725 https://doi.org/10.1080/19407963.2021.2017725 https://doi.org/10.1016/j.scitotenv.2018.12.104 https://doi.org/10.1016/j.envres.2017.05.041 https://doi.org/10.3390/su10072118 https://doi.org/10.1016/j.jenvman.2021.113420 https://doi.org/10.1016/j.jenvman.2021.113420 100 Anaba et al. Destek, M.A., & Aydın, S. (2022). An empirical note on tourism and sustainable development nexus. Environmental Science and Pollution Research, 29(23), 34515-34527. https://doi.org/10.1007 /s11356-021-18371-9 Djellouli, N., Abdelli, L., Elheddad, M., Ahmed, R., & Mahmood, H. (2022). The effects of non-renewable energy, renewable energy, economic growth, and foreign direct investment on the sustainability of African countries. Renewable Energy, 183, 676-686. https://doi.org/10.1016/ j.renene.2021.10.066 Dogru, T., & Bulut, U. (2018). Is tourism an engine for economic recovery? Theory and empirical evidence. Tourism Management, 67, 425-434. https://doi.org/10.1016/j.tourman.2017.06.014 Du, D., Lew, A.A., & Ng, P.T. (2016). Tourism and economic growth. Journal of travel research, 55(4), 454- 464. https://doi.org/10.1177/0047287514563167 El Menyari, Y. (2021). The effects of international tourism, electricity consumption, and economic growth on CO2 emissions in North Africa. Environmental Science and Pollution Research, 1-11. https://doi.org/10.21203/rs.3.rs-224555/v1 Fisher, S., Bellinger, D.C., Cropper, M.L., Kumar, P., Binagwaho, A., Koudenoukpo, J.B., Park, Y., Taghian, G., & Landrigan, P.J. (2021). Air pollution and development in Africa: impacts on health, the economy, and human capital. The Lancet Planetary Health, 5(10), 681-688. https://doi.org/10.1016/S2542-5196(21)00201-1 Flanagan, E., Oudin, A., Walles, J., Abera, A., Mattisson, K., Isaxon, C., & Malmqvist, E. (2022). Ambient and indoor air pollution exposure and adverse birth outcomes in Adama, Ethiopia. Environment International, 164, 107251. https://doi.org/10.1016/j.envint.2022.107251 Gao, C., Ge, H., Lu, Y., Wang, W., & Zhang, Y. (2021). Decoupling of provincial energy-related CO2 emissions from economic growth in China and its convergence from 1995 to 2017. Journal of Cleaner Production, 297, 126627. https://doi.org/10.1016/j.jclepro.2021.126627 Gyamfi, B.A, Adebayo, T. S, Bekun, F.V, Agyekum, E.B., Kumar, N.M., Alhelou, H.H., & Al-Hinai, A. (2021). Beyond environmental Kuznets curve and policy implications to promote sustainable development in Mediterranean. Energy Republic. Energy Reports 7, 6119-6129. https://doi.org/10.1016/j.egyr.2021.09.056 Haller, A.P., Ionela Butnaru, G., Tacu Hârșan, G.D., & Ştefănică, M. (2021). The relationship between tourism and economic growth in the EU-28. Is there a tendency towards convergence? Economic research-Ekonomskaistraživanja, 34(1), 1121-1145. https://doi.org/10.1080/1331677X.2020.1819852 Hanif, I. (2018). Impact of economic growth, nonrenewable and renewable energy consumption, and urbanization on carbon emissions in Sub-Saharan Africa. Environmental Science Pollution Research. 25(15):15057-15067. https://doi.org/10.1007/s11356-018-1753-4 Işik, C., Kasımatı, E., & Ongan, S. (2017). Analyzing the causalities between economic growth, financial development, international trade, tourism expenditure and/on the CO2 emissions in Greece. Energy Sources, Part B: Economics, Planning, and Policy, 12(7), 665-673. https://doi.org/10.1080/15567249.2016.1263251 Jiang, S., Tan, X., Hu, P., Wang, Y., Shi, L., Ma, Z., & Lu, G. (2022). Air pollution and economic growth under local government competition: Evidence from China, 2007-2016. Journal of Cleaner Production, 334, 130231. https://doi.org/10.1016/j.jclepro.2021.130231 Jong, M. C., Soh, A.N., & Puah, C.H. (2022). Tourism sustainability: Climate change and carbon dioxide emissions in South Africa. International Journal of Energy Economics and Policy, 12(6), 412-417. https://doi.org/10.32479/ijeep.13662 Khan, M.M., Zaman, K., Irfan, D., Awan, U., Ali, G., Kyophilavong, P., Shahbaz, M., & Naseem, I. (2016). Triangular relationship among energy consumption, air pollution and water resources in Pakistan. Journal of Cleaner Production, 112, 1375-1385. https://doi.org/10.1016/ j.jclepro.2015.01.094 Kiracı, K. & Bakır, M. (2019). Causal Relationship Between Air Transport and Economic Growth: Evidence from Panel Data for High, Upper-Middle, Lower-Middle and Low-Income Countries. https://doi.org/10.5782/2223-2621.2019.22.3.24 Kukla-Gryz, A. (2009). Economic growth, international trade and air pollution: A decomposition analysis. Ecological Economics, 68(5), 1329-1339. https://doi.org/10.1016/j.ecolecon.2008.09.005 Ku, G., Da Silveira, V., Kegels, G., Develtere, P., Nemery, B., Doussou, J., Bossyns, P., & Gyselinck, K. (2021). Tackling bad air and promoting prevention and care for chronic lung diseases in sub-Saharan Africa. European Journal of Public Health, 31(Supplement_3), ckab165. 189. https://doi.org/ 10.1093/eurpub/ckab165.189 Kyara, V.C., Rahman, M.M., & Khanam, R. (2022). Investigating the environmental externalities of tourism development: evidence from Tanzania. https://doi.org/10.1016/j.heliyon.2022.e09617 https://doi.org/10.1007/s11356-021-18371-9 https://doi.org/10.1007/s11356-021-18371-9 https://doi.org/10.1016/j.renene.2021.10.066 https://doi.org/10.1016/j.renene.2021.10.066 https://doi.org/10.1177/0047287514563167 https://doi.org/10.21203/rs.3.rs-224555/v1 https://doi.org/10.1016/S2542-5196(21)00201-1 https://doi.org/10.1016/j.envint.2022.107251 https://doi.org/10.1016/j.jclepro.2021.126627 https://doi.org/10.1016/j.egyr.2021.09.056 https://doi.org/10.1080/1331677X.2020.1819852 https://doi.org/10.1007/s11356-018-1753-4 https://doi.org/10.1080/15567249.2016.1263251 https://doi.org/10.1016/j.jclepro.2021.130231 https://doi.org/10.32479/ijeep.13662 https://doi.org/10.1016/j.jclepro.2015.01.094 https://doi.org/10.1016/j.jclepro.2015.01.094 https://doi.org/10.5782/2223-2621.2019.22.3.24 https://doi.org/10.1016/j.ecolecon.2008.09.005 https://doi.org/10.1093/eurpub/ckab165.189 https://doi.org/10.1093/eurpub/ckab165.189 https://doi.org/10.1016/j.heliyon.2022.e09617 Tourism revenue, economic growth and carbon dioxide emissions nexus Li, X., Wang, Y., Zhou, H., & Shi, L. (2020). Has China's war on pollution reduced employment? Quasi- experimental evidence from the Clean Air Action. Journal of Environmental Management, 260, 109851. https://doi.org/10.1016/j.jenvman.2019.109851 Lin, J., Pan, D., Davis, S. J., Zhang, Q., He, K., Wang, C., Streets, D. G., Wuebbles, D. J., & Guan, D. (2014). China's international trade and air pollution in the United States. Proceedings of the National Academy of Sciences, 111(5), 1736-1741. https://doi.org/10.1073/pnas.1312860111 Liu, H., Cui, W., & Zhang, M. (2022). Exploring the causal relationship between urbanization and air pollution: Evidence from China. Sustainable Cities and Society, 80, 103783. https://doi.org/ 10.1016/j.scs.2022.103783 Nosheen, M., Iqbal, J., & Khan, H.U. (2021). Analyzing the linkage among CO 2 emissions, economic growth, tourism, and energy consumption in the Asian economies. Environmental Science and Pollution Research, 28, 16707-16719. https://doi.org/10.1007/s11356-020-11759-z Nyasha, S., Odhiambo, N.M., & Asongu, S.A. (2021). The impact of tourism development on economic growth in Sub-Saharan Africa. The European Journal of Development Research, 33, 1514-1535. https://doi.org/10.1057/s41287-020-00298-5 Ohajionu, U.C., Gyamfi, B.A., Haseki, M.I., & Bekun, F.V. (2022). Assessing the linkage between energy consumption, financial development, tourism and environment: evidence from method of moments quantile regression. Environmental Science and Pollution Research, 29(20), 30004- 30018. https://doi.org/10.1007/s11356-021-17920-6 Ongan, S., Işik, C., & Özdemir, D. (2017). The effects of real exchange rates and income on international tourism demand for the USA from some European Union countries. Economies, 5(4), 51. https://doi.org/10.3390/economies5040051 Ouyang, X., Shao, Q., Zhu, X., He, Q., Xiang, C., & Wei, G. (2019). Environmental regulation, economic growth and air pollution: Panel threshold analysis for OECD countries. Science of The Total Environment, 657, 234-241. https://doi.org/10.1016/j.scitotenv.2018.12.056 Ozpolat, A., Ozsoy, F.N., & Destek, M.A. (2021). Investigating the Tourism Originating CO 2 Emissions in Top 10 Tourism-Induced Countries: Evidence from Tourism Index. In Strategies in Sustainable Tourism, Economic Growth and Clean Energy (pp. 155-175). Springer, Cham. https://doi.org/10.1007/978-3-030-59675-0_9 Pal, D., & Mitra, S.K. (2017). The environmental Kuznets curve for carbon dioxide in India and China: Growth and pollution at crossroad. Journal of Policy Modeling, 39(2), 371-385. https://doi.org/10.1016/j.jpolmod.2017.03.005 Qin, M., Zhang, X., Li, Y., & Badarcea, R.M. (2023). Blockchain market and green finance: The enablers of carbon neutrality in China. Energy Economics, 118, 106501. https://doi.org/ 10.1016/j.eneco.2022.106501 Shaheen, K., Zaman, K., Batool, R., Khurshid, M.A., Aamir, A., Shoukry, A.M., & Gani, S. (2019) Dynamic linkages between tourism, energy, environment, and economic growth: evidence from top 10 tourism-induced countries. Environmental Science Pollution Research 26(30), 31273-31283. https://doi.org/10.1007/s11356-019-06252-1 Su, C.-W., Pang, L.-D., Tao, R., Shao, X., & Umar, M. (2022). Renewable energy and technological innovation: Which one is the winner in promoting net-zero emissions? Technological Forecasting and Social Change, 182, 121798. https://doi.org/10.1016/j.techfore.2022.121798 Su, C.-W., Umar, M., Kirikkaleli, D., Awosusi, A.A., &Altuntaş, M. (2023). Testing the asymmetric effect of financial stability towards carbon neutrality target: The case of Iceland and global comparison. Gondwana Research. https://doi.org/10.1016/j.gr.2022.12.014 Sun, Y.-Y., Gossling, S., & Zhou, W. (2022). Does tourism increase or decrease carbon emissions? A systematic review. Annals of Tourism Research, 97, 103502. https://doi.org/10.1016/ j.annals.2022.103502 Tshehla, C. E., & Wright, C.Y. (2019). Spatial and temporal variation of PM10 from industrial point sources in a rural area in Limpopo, South Africa. International journal of environmental research and public health, 16(18), 3455. https://doi.org/10.3390/ijerph16183455 Wijesekara, C., Tittagalla, C., Jayathilaka, A., Ilukpotha, U., Jayathilaka, R., & Jayasinghe, P. (2022). Tourism and economic growth: A global study on Granger causality and wavelet coherence. Plos one, 17(9), e0274386. https://doi.org/10.1371/journal.pone.0274386 Wipfli, H., Kumie, A., Atuyambe, L., Oguge, O., Rugigana, E., Zacharias, K., Simane, B., Samet, J., & Berhane, K. (2021). The GEOHealth Hub for eastern Africa: Contributions and lessons learned. GeoHealth, 5(6), e2021GH000406. https://doi.org/10.1029/2021GH000406 World Health Organization. (2019). World health statistics overview 2019: monitoring health for the SDGs, sustainable development goals (No. WHO/DAD/2019.1). World Health Organization. Yusuf, M.S., Musibau, H.O., Dirie, K.A., & Shittu, W.O. (2023). Role of trade liberalization, industrialisation and energy use on carbon dioxide emissions in Australia: 1990 to 2018. Environmental Science and Pollution Research, 1-16. https://doi.org/10.1007/s11356-023-27825-1 https://doi.org/10.1016/j.jenvman.2019.109851 https://doi.org/10.1073/pnas.1312860111 https://doi.org/10.1016/j.scs.2022.103783 https://doi.org/10.1016/j.scs.2022.103783 https://doi.org/10.1007/s11356-020-11759-z https://doi.org/10.1057/s41287-020-00298-5 https://doi.org/10.1007/s11356-021-17920-6 https://doi.org/10.3390/economies5040051 https://doi.org/10.1016/j.scitotenv.2018.12.056 https://doi.org/10.1007/978-3-030-59675-0_9 https://doi.org/10.1016/j.jpolmod.2017.03.005 https://doi.org/10.1016/j.eneco.2022.106501 https://doi.org/10.1016/j.eneco.2022.106501 https://doi.org/10.1007/s11356-019-06252-1 https://doi.org/10.1016/j.techfore.2022.121798 https://doi.org/10.1016/j.gr.2022.12.014 https://doi.org/10.1016/j.annals.2022.103502 https://doi.org/10.1016/j.annals.2022.103502 https://doi.org/10.3390/ijerph16183455 https://doi.org/10.1371/journal.pone.0274386 https://doi.org/10.1029/2021GH000406 https://doi.org/10.1007/s11356-023-27825-1 102 Anaba et al. Appendix I. List of countries in the panel data S/N Country S/N Country 1 Angola 25 Malawi 2 Benin 26 Mali 3 Botswana 27 Mauritania 4 Burkina Faso 28 Mauritius 5 Burundi 29 Mozambique 6 Cameroon 30 Namibia 7 Cape Verde 31 Niger 8 Central African Republic 32 Nigeria 9 Chad 33 Republic of the Congo 10 Comoros 34 Rwanda 11 Democratic Republic of the Congo 35 Sao Tome and Principe 12 Equatorial Guinea 36 Senegal 13 Eritrea, 37 Seychelles 14 Ethiopia 38 Sierra Leone 15 Gabon 39 Somalia 16 Gambia 40 South Africa 17 Ghana 41 Sudan 18 Guinea 42 Swaziland 19 Guinea-Bissau 43 Tanzania 20 Ivory Coast 44 Togo 21 Kenya 45 Uganda 22 Lesotho 46 Zambia 23 Liberia 47 Zimbabwe 24 Madagascar 1. Introduction 2. Literature review 3. Methodology 4. Results and discussions 5. Conclusions and policy implications References