Tourism income and ecological footprint reveal an environmental Kuznets curve independent of income level

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We developed a model based on the EKC hypothesis, considering the ecological footprint, energy consumption, urban population, tourism receipt, and real gross domestic product as indicators for 1999–2018 in 78 countries. Our results from the system generalized method of moments indicate an inverted U-shaped relationship between tourism receipt and ecological footprint in 78 countries and another dataset containing 19 countries. The study reveals that income level is not a determinant of the EKC, as the indicator for income level is nonsignificant. The results of our study have significant implications for policymakers and future studies. Environmental Kuznets curve ecological footprint tourism receipt income degree Figures Figure 1 Introduction Since the last century, concerns about greenhouse gases (GHGs) have increased as they are considered a major cause of global warming and disruption of regional ecological balance. Therefore, achieving economic sustainability with minimal environmental cost has become an important issue of this century. In recent years, many studies discussing the relationship between GDP and environmental pollution based on the environmental Kuznets curve (EKC) model, which hypothesizes an inverted U-shaped relationship between economic growth and environmental pollution (Grossman and Krueger, 1991 ), have found that an increase in tourism revenue significantly mitigates environmental pollution. This might indicate that, compared to overall GDP, the growth in the proportion of tourism revenue has a more direct impact on reaching the turning point of the EKC. The tourism industry accounted for 8% of global CO2 emissions in 2013, and it continues to grow. (Ișik, et al. 2020 ) And as Gössling, S. (2002) mentioned, the relationship between the environment and tourism is inextricably linked. Tourism constantly affects the environmental conditions through changes in land cover and land use, energy consumption, biotic exchange and extinction of wild species, the spread and dispersion of diseases, changes in the perception and understanding of the environment, and water resource usage. Therefore, studying the impact of tourism on the EKC allows us to understand the mechanisms of economic activities on the environment in a more multidimensional way. Many past studies have confirmed the existence of the EKC, whether in terms of tourism revenue and environmental pollution or overall GDP and environmental pollution. However, we believe that compared to overall GDP, the impact of tourism revenue is more direct. This is because the development of tourism often brings increased environmental pressures, such as resource consumption and pollution emissions. Additionally, an increase in tourists often leads to overdevelopment and ecological damage to scenic spots. According to the 2020 annual report by the World Tourism Organization, transport-related CO 2 emissions from tourism are expected to account for 5.3% of all emissions by 2030, and emissions from international tourism are predicted to grow 45% during 2016–2030. In addition to transportation, tourism influences carbon emissions during the process of supporting accommodation, producing souvenir, and other tourism behavior, which have a huge demand of consumption for fossil fuels and electricity power, establishing an association between tourism, energy use, and carbon emissions (Dogan and Aslan, 2017 ; Seyfettin Erdoğan et al., 2022 ). However, with the further development of tourism, the country's industrial structure tends to shift towards a service-oriented tertiary industry. This not only signifies the improvement of the country's level of development but is also often accompanied by an increase in environmental awareness among the people. Furthermore, it provides funding and more effective policy support for achieving sustainable development goals. All these factors contribute to reaching the turning point of the EKC. Compared to this, the impact of overall GDP, which is the sum value-added of various industries, For example, agriculture, manufacturing, and tourism, is more complex. For instance, Boluk and Karaman ( 2024 ) confirmed in their study that agriculture exacerbates environmental pollution in Turkey, while Selcuk et al. (2024) found that agriculture reduces environmental pollution in eleven countries, including Bangladesh and Turkey. Stavropoulos, P. & Chryssolouris, G(2022)discussed the impact of the manufacturing industry on carbon emissions across various EU countries, emphasizing the importance of carbon emission factors at different manufacturing levels, and pointed out that both electricity consumption and the use of recyclable materials can effectively affect the carbon footprint. Not to mention, tourism has become one of the most important forces for economic growth in many developing and developed countries (De Vita et al., 2015 ; Katircioglu, 2009 ) and has contributed to economic growth from several perspectives (Alam and Paramati, 2016 ). Therefore, we have reason to believe that studying the impact of specific industries like tourism on the EKC is more effective than studying overall GDP. This approach also helps us better understand the roles various industries play in environmental impact, aiding in the formulation of future development plans. In primary research, environmental pollution is proxied by CO 2 or SO 2 emissions and economic growth is proxied by gross domestic product (GDP). However, this is insufficient because environmental pollution should not be measured using emissions for only one or two gases. From a statistical perspective, an indicator representing environmental pollution more comprehensively and appropriately is required to determine the association between economic growth and environmental pollution. In this study, we selected the ecological footprint (EF) as a measure of environmental degradation, which uses the equivalent requirements of forest, land, and other natural resources rather than waste emissions. The EF has already been characterized as an appropriate representative indicator of the environment by many researchers, such as Destek et al. ( 2018 ) and Altintas and Kassouri (2020), and Destek and Sarkodie ( 2019 ), and involves six components: cropland, grazing land, fishing grounds, forest land, built-up land, and carbon footprint (including CO 2 emissions) (Destek et al. 2018 ). Compared with gas emissions, this can be a full-scale approach to answer the question regarding the existence of the EKC. This paper examines the relationship between tourism and environmental pollution and further discusses the significance of tourism and income levels in relation to environmental pollution, and discovered an EKC relationship between tourism revenue and ecological footprint in 78 countries worldwide, Furthermore, confirming that tourism revenue contributes more significantly to environmental pollution than overall income. Literature Review This section reviews existing studies on the nexus between economic growth, environmental pollution, and energy consumption. Since the conception of the EKC by Grossman and Krueger ( 1991 ), numerous empirical studies have been conducted, especially over the last four decades. Data in different countries and regions were analyzed through different methodologies and models, revealing the existence of an inverted U-shaped relationship between the environmental degradation indicators and economic growth. This means that the correlation between environmental pollution and economic growth will be negative after the economies of those countries developed to a certain level, even though environmental pollution is now gradually increasing with economic growth in some countries. Specifically, economic growth will become environment friendly. Most studies have used CO 2 emissions as environmental degradation indicators as the first step of research on the EKC. However, several arguments remain unresolved. These studies focused on a unique country and assert that an inverted U-shaped relationship exists between CO 2 emissions and economic growth in some developing countries such as Malaysia and China (Ahmad et al., 2017 ; Ahmed and Long, 2012 ; Ali et al., 2017 ; Jalil and Mahmud, 2009 ; Li et al., 2016 ; Mahmood and Alkhateeb, 2017 ; Mrabet and Alsamara, 2017 ; Shahbaz et al., 2012 ; Tiwari et al., 2013 lük and Mert, 2015). Al-Mulali et al. ( 2015a ), Lacheheb et al. ( 2015 ), Ozturk and Al-Mulali ( 2015 ), Saboori and Sulaiman ( 2013 ), and Zambrano-Monserrate et al. ( 2018 ) disagree and prove the existence of a U-shaped or even a linear relationship between CO 2 emissions and economic growth using data from China, Malaysia, Peru, Cambodia, and some other countries. Regarding research on a group of countries/regions, Fakih and Marrouch ( 2019 ) argue that an inverted U-shaped relationship does not exist between environmental indicators and economic growth using data from the 10 Middle East and Northern Africa (MENA) countries, whereas Farhani et al. (2014) find the EKC in the MENA countries. Simultaneously, Shahbaz et al. ( 2019 ) provide mixed results, concluding that the EKC is observed in higher income countries through empirical research using data from 86 countries. As mentioned in Section 1, indicators such as CO 2 emissions represent only a small proportion of the total environmental pollution and damage. To achieve more complete and robust results, the EF is proposed that received substantial attention. Destek and Sinha ( 2020 ), Ozturk et al. ( 2016 ), Wang et al. ( 2013 ), Bagliani et al. ( 2008 ), Mrabet and Alsamara ( 2017 ), Al-Mulali et al. ( 2016 ), and Charfeddine and Marabet (2017) apply the EF data as an environmental degradation indicator and analyze the relationship between environmental pollution and economic growth using data from the MENA or other groups of countries and regions in different periods. The statistical methodologies used in these studies include, but are not limited to, system generalized method of moments (SYS-GMM), FMOLS, DOLS, and ARDL. The indicator of economic growth was not only GDP or GDP per capita but also the annual receipt of tourism. According to World Tourism Organization, carbon emissions from tourism cause environmental damage substantially, and tourist industries have become significant contributors in the GDP of both developed and developing countries. However, the conclusions of these studies are complex. Katircioglu et al. ( 2018 ) estimate that the EKC uses the EF and GDP data from top 10 tourist destination during 1995–2014 and confirm an inverted U-shaped relationship between economic growth and environmental degradation. Additionally, Liu et al. ( 2022 ) verify an inverted U-shaped relationship between travel and tourism and the EF in Pakistan with data for 1980–2017. They also provide evidence to support the “pollution haven hypothesis” by proving that direct foreign investment has a positive influence on environmental degradation in Pakistan. However, some studies have verified a U-shaped relationship between the EF and economic growth. For example, Lee and Chen ( 2021 ) divide the EF into six parts and indicate that a U-shaped relationship generally exists between the EF and either GDP or international tourism receipt when the EF is applied to carbon-absorption land, cropland, and fishing grounds from 123 countries during 1992–2016 using the quantile regression approach. Specifically, the existence of the EKC cannot be confirmed in every study. Some studies still argue whether the existence of inverted U-shaped relationship depends on the level of income or development of such countries or regions (Al-Mulali et al., 2015; Ozturk et al., 2016 ). However, these conclusions are uncertain. Based on the estimation results of Ozturk et al. ( 2016 ), some low-income countries, such as Ethiopia, demonstrate an EKC relationship between their economic growth and environmental pollution indicators. Although the overall discussion indicates the relationship between the Ecological Footprint (EF) and tourism receipts or GDP, the question still persists on how to achieve an EKC relationship between economic growth and environmental degradation within a country. Should the level of income be responsible for facilitating the EKC relationship? For the EKC, is the overall GDP/income level more important, or is the income from a specific industry (e.g., tourism income) more crucial? If so, should governments focus on enhancing the overall income level or promoting a specific industry? What is the actual reason for determining the existence of the EKC, and how can we achieve a path of development similar to the EKC? In this study, we combined data from countries and regions that had an EKC relationship to determine whether the level of income is necessary for the EKC (see Table 1 ). Table 1 Some main reference of this paper Authors Countries Period Variables EKC Result Ozturk et al. ( 2016 ) Tachega et al. ( 2021 ) Azam and Khan ( 2016 ) Handoyo et al. ( 2022 ) Katircioglu et al. ( 2018 ) Ochoa-Moreno et al.(2022) Sharif et al. ( 2020 ) Akadiri et al. ( 2019 ) Zaman et al. ( 2016 ) Anser et al. ( 2020 ) Ișik et al. ( 2020 ) Aziz et al. ( 2020 ) Al-mulali et al. ( 2016 ) Destek and Sinha ( 2020 ) 144 countries 54 African economies Tanzania, Guatemala, China and the USA, Not-mentioned 10 countries 20 Latin American countries Malaysia 15 countries 34 countries G7 countries G7 countries BRICS countries 58 countries 24 OECD countries 1988–2008 1990–2015 1975–2014 2010–2019 1995–2014 1995–2018 1995Q1-2018Q4 1995–2014 2005–2013 1995–2015 1995–2015 1995–2018 1980–2009 1980–2014 EF TD TD 2 URB TRD ECO CO 2 ARG RECONRECO GDP GDP 2 CO 2 ECO GDP GDP 2 TRD URB CO 2 ECO GDP GDP 2 TD TD 2 FDI EX IM EF GDP GDP 2 URB TD REEX ECO CO 2 TD TD 2 CO 2 GDP GDP 2 TD TRS GLO CO 2 GDP GDP 2 TD ECO GLO CO 2 GDP GDP 2 TD GFCF ECO HEXP CO 2 GDP GDP 2 TD EDU HEXP CO 2 GDP GDP 2 TD RE CO 2 GDP GDP 2 TD RE EF GDP GDP 2 TD RE URB EF GDP GDP 2 RE NRECO TRD Existed, income group specific Existed but not relatedto income Existed, income groupspecific Income group specific for both TD and GDP Existed Existed, income groupspecific Existed Existed Existed Existed Existed except Italy Existed Not existed Existed in 8 countries Note: TD: tourism development proxy; GDP: real GDP; EF: ecological footprint; URB: urbanization proxy; RE: renewable energy; NRECO: non-renewable energy; TRD: trade openness; ECO: energy consumption; REEX: real effective exchange rate index; FDI is Foreign direct investment; EX: export; IM: import; TRS: transportation service; GLO: globalization; GFCF: gross fixed capital formation; HEXP: health expenditure per capita; EDU: education expenditure. [Table 1 about here] Data and Methodology The annual data on the ecological footprint (EF) were obtained from the Global Footprint Network ( https://data.footprintnetwork.org ). Data on tourism receipts, real GDP, and urban population were collected from the World Bank ( https://data.worldbank.org ). Energy consumption data were obtained from the U.S. Energy Information Administration ( https://www.eia.gov ). Given our main objective, we require a model based on the EKC hypothesis. This study’s model is based on Ozturk et al. ( 2016 ), Ahmad et al. ( 2017 ), and Shahbaz et al. (2013): $$\:{lnEF}_{t}={f(lnTGDP}_{t},\:{lnTGDP}_{t}^{2},\:{lnURB}_{t},{lnECO}_{t},{{lnRGDP}_{t},lnEF}_{t-1})$$ , where lnEF is the natural log of the EF, and lnTGDP is the natural log of tourism receipts, measured in constant US dollars. lnURB, lnRGDP, and lnECO are the natural logs of the urban population measured in millions of people, real GDP measured in US dollars, and energy consumption, respectively, which have been confirmed to be closely related to environmental degradation in previous studies and are used to build up a dynamic panel model. Thus, we add the first difference variable, lnEF t−1 , to this model. The correlation between the error term and other variables, such as lnEF t−1 on the right-hand side, is a common problem in most dynamic panel models. Instrument variables are utilized to solve this problem. However, as instrument variables are always random and have arbitrariness, it is extremely difficult to select an instrument variable that does not correlate with the error term; therefore, we proposed the dependent variable, GMM. Thus, as the different instruments in the GMM are usually weak, we applied the SYS-GMM suggested by Arellano and Boverover (1995) and Blundell and Bond ( 1998 ) to estimate our model. The SYS-GMM can be regarded as an evolution of DIF-GMM containing regression and instrumental variables at both levels and differences. In addition, the SYS-GMM contains a two-step Arellano-Bond test for the autocorrelation of the error term, and the validity of the instruments is tested using both the Hansen and Sargan tests. Our GMM model is as follows: $$\:{lnEF}_{it}={{\alpha\:}_{it}＋{\beta\:}_{1}lnTGDP}_{it}＋\:{\beta\:}_{2}{lnTGDP}_{it}^{2}＋\:{{\beta\:}_{3}lnURB}_{it}＋{{\beta\:}_{4}lnECO}_{it}＋{{\beta\:}_{5}lnRGDP}_{it}＋\gamma\:{lnEF}_{t-1}＋{\epsilon\:}_{it},$$ where β 1 , β 2 , β 3 , β 4 and β 5 are the coefficients of each dependent variables, and ε it is an error term of this model. i depends on the cross-sectional number of our data, which refers to the number of countries, and t represents the period from 1999 to 2018. The EKC exists if β 1 > 0 and β 2 < 0. Regarding the selection of endogenous and instrumental variables, during the estimation for all data and subdatasets, we chose lnECO and lnURB as the endogenous variables; however, we selected lnRGDP as an instrumental variable in all datasets and lnTGDP in the subdataset. Additionally, we distinguished between these variables based on previous experience. Furthermore, if β 5 is negative and significant during the estimation utilizing our two datasets, the level of income should be recognized as an important component of the EKC. In contrast, the level of income should be considered a less important part of the EKC compared to tourism receipt. To further substantiate our conclusion, we employed Dominance Analysis(Grömping, U. ( 2007 )) to assess the contribution of each variable in the linear model to both the model itself and the explained variable. Empirical Result and Discussion We estimated the existence of the EKC using all datasets that contained data of 78 countries’ EF, tourism receipts, energy consumption, and urban population from 1999 to 2018. Table 2 provides the results obtained from the SYS-GMM. Table 2 SYS-GMM result for all data from 78 countries Variables Coef All data EF lntgdp 0.0918917**(0.036) -0.0108863***(0.000) -0.081901(0.161) 0.0372897(0.504) 0.7248755***(0.000) 0.1029155(0.375) -0.4164117(0.610) 0.472 0.760 lntgdp2 lnurb lnecp lagEF lnrgdp constant AR(2) Hansen test [Table 2 about here] The estimates in Table 2 suggest that an EKC relationship exists in 78 countries between tourism receipt and the EF. We observe that the coefficient for the squared tourism receipt is negative at the 1% significance level, implying that environmental degradation decreases with the development of tourism after tourism receipt reaches the turning point of the EKC. Furthermore, this result suggests that countries’ policies for promoting the tourism industry achieve an ecofriendly development method. Moreover, our results indicate that the lag of the EF enhances environmental degradation at the 1% significance level; however, the constant term and coefficient for energy consumption and urban population in 78 countries are nonsignificant, which means that such a link is nonexistent between population urbanization and ecological degradation, similar to Xu et al. ( 2018 ). Furthermore, to further determine the importance of the affluence of countries in the EKC model, we narrowed our dataset to 19 countries that have been proven to exhibit the EKC relationship, categorized them based on the results of Ozturk et al. ( 2016 ). Our new dataset contained data from Ethiopia, Madagascar, Guatemala, Pakistan, Argentina, Azerbaijan, Brazil, Mexico, Romania, South Africa, Chile, Greece, Israel, Kuwait, Norway, Singapore, Switzerland, the United Arab Emirates, and Peru. As shown in Table 5 , Madagascar and Ethiopia are defined as low-income countries; Guatemala and Pakistan are defined as lower middle-income countries; Argentina, Brazil, Azerbaijan, Mexico, Peru, Romania, and South Africa are defined as upper middle-income countries; and Chile, Israel, Kuwait, Norway, Greece, Singapore, Switzerland, and the United Arab Emirates are defined as high-income countries. Utilizing the data of 19 countries with the SYS-GMM through the same model of all data, Table 3 reveals that an inverted U-shaped curve exists between tourism receipt and the EF, even if the significance for squared tourism receipt is reduced to the 5% level, while the probability is approximately 1%. Moreover, in this case, the coefficient of the urban population becomes significant and reduces environmental degradation by 5%. This result is similar to that of Farooq and Dar ( 2022 ) but opposite to that of Ozturk et al. ( 2016 ), where agglomeration effects may be devoted to this result. Moreover, Zhou et al. ( 2019 ) assert that social urbanization exerts a negative impact on environmental degradation by increasing public awareness. Simultaneously, higher urbanization may contribute to a higher level of education, which, in turn, promotes education. This result also impels policymakers and governments to pay more attention to their urbanization plans, such as utilizing agglomeration effects to accelerate technology development, popularizing higher education, discontinuing the endless urban expansion, and increasing the forest area, as valid urban policy may contrarily reduce environmental degradation for countries. Moreover, propagating electric vehicle and public transportation to achieve low-carbon travel will effectively reduce environmental degradation. Meanwhile, in the case of the 19 countries, the coefficients for energy consumption and real GDP are nonsignificant. Table 3 SYS-GMM result for the 19 countries Variables Coef 19 countries EF lntgdp 0.1953924**(0.045) -0.0148062**(0.011) -0.1540909**(0.044) 0.1701179(0.147) 0.5471615***(0.001) -0.1172484(0.465) 1.497409 (0.224) 0.111 0.823 lntgdp2 lnurb lnecp lagEF lnrgdp constant AR(2) Hansen test [Table 3 about here] Comparing the results in Table 2 with Table 3 , both the estimates reveal the existence of the EKC. However, the axis of symmetry moved from 4.22 to 6.60 when our data ranged from 78 to 19 countries. This reveals that a higher level of tourism receipt helps countries reach the inflection point of the EKC earlier, to a certain extent, explaining the importance of tourism receipt in the composition of the EKC and suggesting that the government should publish policies, such as redistributing income between poor and affluent people (Anser et al., 2020 ) to promote tourism in these countries. Regarding the coefficient of real GDP, in both estimations, there was no evidence of the effect of real GDP/income on the EKC, as all the results indicated that the coefficient for real GDP was nonsignificant. This implies that in both sets of empirical analyses, income level is considered less important in the composition of the EKC compared to tourism receipt. To corroborate this result, we discussed the contribution of each variable in the model to the Ecological Footprint (EF), with the results presented in Table 4 . Table 4 Dominance analysis result Dominance stat. Ranking lnecop lntgdp lnrgdp lnecp urb lagEF 0.1245 3 4 2 5 1 0.1152 0.1543 0.0019 0.5976 [Table 4 about here] Table 4 presents the results of dominance analysis. The results indicate that the dominance coefficient for `lntgdp` is 0.1245, which is higher than that of `lnrgdp` at 0.1152. This suggests that, compared to income level, tourism income makes a greater contribution to the model's fit statistic for ecological footprint, indicating a stronger influence. This finding underscores the higher importance of tourism income over income level in explaining the predictive capability of the model. Furthermore, as shown in Tables 5 and 6 , referring to Ozturk et al. ( 2016 ), a significant proportion of our two datasets consist of countries that are not defined as high-income countries. Low-income and middle-income countries are the major components of our data. However, some countries are not mentioned in the literature: Albania, Armenia, Bahamas, The., Belarus, Bhutan, Bosnia and Herzegovina, Croatia, Dominica, El Salvador, Kazakhstan, Moldova, Philippines, the Slovak Republic, Slovenia, and St. Lucia. Most countries are well known as developing countries with low or middle incomes; therefore, it is reasonable to believe that income level is not particularly crucial in the form of the EKC. Table 5 Composition of all data Countries Low income Benin, Ethiopia, Haiti, Kenya, Madagascar, Malawi, Mali, Nepal, Niger, Sierra Leone, Tanzania, Togo, Zimbabwe Lower middle income Bolivia, Cameroon, Cote d'Ivoire, Ghana, Guatemala, Mongolia, Nigeria, Pakistan, Paraguay, Senegal, Sri Lanka, Upper middle income Angola, Argentina, Azerbaijan, Botswana, Brazil, Colombia, Costa Rica, the Dominican Republic, Fiji, Jordan, Malaysia, Mexico,Panama, Peru, Romania, South Africa, Thailand, Tunisia High income Australia, Bahrain, Chile, Denmark, Finland, France,Germany, Greece, Israel, Japan, Korea, Rep., Kuwait, New Zealand, Norway, Oman, Poland, Portugal, Singapore, Switzerland, the United Arab Emirates, USA Not mentioned Albania, Armenia, Bahamas, The., Belarus, Bhutan, Bosnia and Herzegovina, Croatia, Dominica, El Salvador, Kazakhstan, Moldova, Philippines, the Slovak Republic, Slovenia, St. Lucia Table 6 Composition of sub dataset Countries Low income Madagascar, Ethiopia, Lower middle income Guatemala, Pakistan Upper middle income Argentina, Brazil, Azerbaijan, Mexico, Peru, Romania, South Africa High income Chile, Israel, Kuwait, Norway, Greece, Singapore, Switzerland, the United Arab Emirates [Table 5 about here] [Table 6 about here] After confirming the existence of the EKC, we observed the distribution of each country on the EKC curve, and some of the samples are as follows: These countries can be classified into three types: First, the countries that have already exceeded the inflection such as South Africa, Tunisia, Botswana, Korea, Rep., Dominica, St. Lucia, the Dominican Republic, Costa Rica, Panama, El Salvador, Bahamas, The., Switzerland, France, Poland, Denmark, Portugal, Greece, the Slovak Republic, Norway, Croatia, Albania, Finland, Slovenia, Germany, Mexico, United States, Australia, New Zealand, Fiji, Malaysia, Thailand, Singapore, Chile, Bahrain, Kuwait, Oman, the United Arab Emirates, Israel, and Jordan; these countries are gradually achieving an environment-friendly society. Second, countries located near the inflection point include Kazakhstan, Japan, Mongolia, Armenia, Sri Lanka, Bhutan, Azerbaijan, Guatemala, Belarus, Romania, Moldova, Bosnia and Herzegovina, Philippines, Argentina, Colombia, Peru, and Bolivia; these countries’ efforts are not sufficient to let their environmental degradation gradually disappear with the development of tourism receipt. Finally, countries that have not attached an inflection point include Ghana, Angola, Togo, Sierra Leone, Kenya, Cote d’Ivoire, Cameroon, Ethiopia, Mali, Benin, Zimbabwe, Malawi, Niger, Tanzania, Senegal, Madagascar, Nigeria, Pakistan, Nepal, Haiti, Brazil, and Paraguay; these countries may need other policies to approach the goal of eco-friendly development. Our results indicate that countries exceeding the inflection point does not necessarily comprise developed countries; for example, Jordan, which is a well-known developing country. Furthermore, being a developed country, Japan is “on” the inflection point of the EKC curve. Thus, it is difficult to assert the importance of income level for the EKC. From another perspective, the EKC is absent in many African countries, and geographic factors should be considered in future studies. Conclusion and Future Consideration This study examined the relationship between the EF and tourism receipt using the SYS-GMM. We used real GDP, urban population, energy consumption, and the first difference in the EF as independent variables in our estimation. Two datasets were created to achieve these objectives. One dataset included 78 countries from 1999 to 2018, and the other included data from 19 of these countries over the same period. These datasets were used to determine the existence of the EKC between tourism receipts and EF, and to verify the importance of tourism income and overall income levels in the formation of the EKC. Our results can be summarized as follows. First, for tourism receipt, several studies have confirmed that environmental degradation is strongly related to the tourism receipt. Some studies, such as Akadiri et al. ( 2019 ) and Sharif et al. ( 2020 ), prove that the relationship between environmental degradation and tourism income is negative, which means that the development of tourism reduces pollution to a certain extent. However, others such as Zaman et al. ( 2016 ), Khalid Anser et al. ( 2020 ) and De Vita et al. ( 2015 ) confirm that tourism increases the environmental degradation in some areas/regions. Ișik et al.’s ( 2020 ) result is complex and depends on the target countries. Nevertheless, the tourism industry is closely related to environmental pollution. Moreover, as a sector it may be able to make a significant contribution to economic growth (Katircioglu et al., 2018 ). In this study, following Ozturk et al. ( 2016 ), Ochoa-Moreno et al. ( 2022 ), Handoyo et al. ( 2022 ), and Kyara et al. ( 2022 ), we applied tourism receipt and its square as independent variables using an EKC model and confirmed an inverted U-shaped relationship between tourism receipt and environmental degradation. This means that environmental degradation first increases and then decreases after tourism receipt attaches a specific level with the development of the tourism industry. Second, in previous studies, income level is considered a determinant of the existence of the EKC (Van Alstine and Neumayer, 2010 ). The significance of a polynomial with real GDP in regression usually becomes stronger when moving research object from poor countries to high-income countries (Magnani, 2000 ). As mentioned before, when income grows, more efficient and cleaner technology is adopted by people (Dinda, 2004 ), and people’s demand for a better living environment also induces the structural change, leading economy to be “greener” and more environment friendly with the income growth (Borghesi, 1999 ). When utilizing tourism receipt squared as an independent variable, Ozturk et al. ( 2016 ) also conclude that the EKC frequently appeared in upper middle-income countries and high-income countries. However, to determine whether the level of income affects the existence of the EKC, we used real GDP as an income indicator and created a subdataset by considering countries that had different income levels and an inverted U-shaped link between tourism receipt and environmental degradation. We observed that compared to income levels, tourism income had a more significant impact on environmental pollution and the EKC; the EKC existed in both datasets. Moreover, Azam and Khan ( 2016 ) indicate the presence of the EKC in low- and lower middle-income countries, and Tachega et al. ( 2021 ) note that the EKC phenomenon is not income group specific and can occur in any country or region. This phenomenon may be due to the following reasons: 1. The type of major industry, rather than the income level, determines the existence of the EKC. Qatar, a developing country, obtains income mainly from the petrochemical industry at the cost of severe environmental pollution, whereas Germany, a developed country where the manufacturing industry constitutes over 20% of the GDP, also causes heavy pollution. In the nexus between economic growth and environmental degradation, the concepts of development and income must be distinguished explicitly. Development accompanied by innovation may be helpful for environmental management; for example, Gormus and Aydin ( 2020 ) state that innovation is significantly devoted to reducing environmental degradation in Korea, USA, and Finland. However, no evidence exists to prove that high income directly affects environmental degradation. 2. Compared with the income of countries, policies and other factors may influence procedures such as energy use and carbon emissions, which are related to environmental degradation directly and validly. Onafowora and Owoye ( 2014 ) mention that increasing income should not be considered a solution for environmental degradation, as it can be a statistical artifact; some countries cannot even achieve the turning point in several decades. Our study also provides evidence for this conclusion, as in both datasets, the coefficient of real GDP is nonsignificant. In determining the relationship between the existence of the EKC and income level, some questions should be highlighted in future studies. As previously mentioned, the EF comprises six parts: cropland, grazing land, fishing grounds, forest land, built-up land, and carbon footprint. Past studies have proven the relationship between carbon emissions and GDP when discussing the existence of the EKC using CO 2 emissions as an indicator, which means that a part of the EF is closely related to the income level. However, when all these components are combined, they become dissimilar. Therefore, other factors associated with the EF should be considered in future studies. Moreover, not only tourism but different economic sectors often have varying impacts on the EKC at different stages of economic development, making the related topics extremely complex. Pata ( 2021 ) discusses the relationship between agricultural value added and whether CO 2 emissions or the EF in BRIC countries are caused by bidirectional causality between agriculture and environmental degradation. Moreover, Lee and Chen ( 2021 ) analyze the relationships between tourism and each component of the EF, proving that an inverted U-shaped relationship exists in grazing land and forest land; and U-shaped relationships exists in carbon-absorption land, cropland, and fishing ground. Furthermore, in future research, similar to the findings of this study, there may be cases where the importance of various types of income surpasses income levels. In some instances, the significance of income levels may even outweigh other types of income such as tourism receipt. Many advantages will emerge after refining these problems. For example, by determining whether and how agriculture increases the environmental degradation of cropland and fishing land, targeted policies can be implemented, which will be helpful in solving the deficit of environmental capacity. Using the EF as a consistent variable, resolving problems in any region/country will be effective and referential for other regions/countries. Based on the aforementioned conclusions, other recommendations for future research can also be drawn. First, it is necessary to distinguish between renewable and nonrenewable energy consumption, although there have been many studies, such as Handoyo et al. ( 2022 ), proving that energy use has a significant positive effect on environmental degradation. According to Katircioglu et al. ( 2014 ), the tourism industry always increases environmental pollution by path costing plenty of energy, such as electricity, fossil oil, and natural gas, which are mostly produced from nonrenewable energy. The contradictory effects of renewable and nonrenewable energy on the environment are validated by multiple researchers, such as Sulaiman et al. ( 2013 ) and Jebli and Youssef ( 2015 ). This is the major reason for the coefficient of energy use in our study to be nonsignificant in all cases. It seems more suitable to use only a part of the energy consumption as an indicator. Second, as the determinants of the EKC remain inconclusive, other factors should be considered. Variables for the validity of policy, similar to our aforementioned indicators measuring the degree of development of countries, should be created instead of income. These indicators should include more factors other than technical innovation, education level, people’s affluence, and so forth. These unanimous variables will be extremely helpful in future studies regarding the “harmony” between economic growth and environmental degradation. Third, as mentioned previously, new technologies adopted by people can significantly affect the EKC; however, in the EKC model, the time point and efficiency of technology are ambiguous. Stern ( 2014 ) mentions that a new technology innovation is usually adopted first in high-income countries and then in low-income countries, but in the model of EKC, it is difficult to determine “how” and “when” the innovation affect our dataset. Thus, the EKC model and other models for testing the link between economic growth and environmental degradation should be more comprehensive. Finally, to determine several reasons affecting the EKC, research on specific industries, such as tourism, should be devoted to future studies to assist policymakers in formulating economic development strategies that are tailored to their country's specific conditions, rather than simply aiming to increase income.. Although there are studies on agriculture and tourism, compared with the research focused on the link between GDP and the environment/income, the existing studies are inadequate. Declarations Data availability The datasets used and analyzed during the current study are publicly available from the following sources: Ecological footprint data are available from the Global Footprint Network ( https://data.footprintnetwork.org ). Tourism receipts, GDP, and urban population data are available from the World Bank ( https://data.worldbank.org ). Energy consumption data are available from the U.S. Energy Information Administration ( https://www.eia.gov ). All data are openly accessible and can be obtained without restriction. The datasets used and analyzed during the current study are publicly available from the World Bank, Global Footprint Network, and other publicly accessible databases. Consent to participate Not applicable. Consent to publish Not applicable. Ethics approval This study does not involve human participants or animals. Therefore, ethics approval is not required. Funding No funding was received for conducting this study. Author Contribution Xiaoning Wang conceived the study, conducted the empirical analysis, and wrote the original manuscript. Shingo Takagi supervised the research, provided guidance on the study design, and revised the manuscript. All authors reviewed and approved the final manuscript. References Ahmad N, Du L, Lu J, et al. Modelling the CO 2 emissions and economic growth in Croatia: Is there any environmental Kuznets curve? Energy. 2017;123:164–72. Ahmed K, Long W. Environmental Kuznets curve and Pakistan: An empirical analysis. Procedia Econ Finance. 2012;1:4–13. Akadiri SS, Lasisi TT, Uzuner G, et al. Examining the impact of globalization in the environmental Kuznets curve hypothesis: The case of tourist destination states. Environ Sci Pollut Res. 2019;26:12605–15. Alam MS, Paramati SR. The impact of tourism on income inequality in developing economies: Does Kuznets curve hypothesis exist?[J]. Annals tourism Res. 2016;61:111–26. Ali W, Abdullah A, Azam M. 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Examining the influences of urbanization on carbon dioxide emissions in the Yangtze River Delta, China: Kuznets curve relationship. Sci Total Environ. 2019;675:472–82. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 29 Apr, 2026 Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Editor invited by journal 16 Apr, 2026 Submission checks completed at journal 15 Apr, 2026 First submitted to journal 15 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9241601","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630072062,"identity":"9c95490e-fabe-45c2-b56d-b81d401d99b4","order_by":0,"name":"XIAONING WANG","email":"data:image/png;base64,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","orcid":"","institution":"Hokkaido University","correspondingAuthor":true,"prefix":"","firstName":"XIAONING","middleName":"","lastName":"WANG","suffix":""}],"badges":[],"createdAt":"2026-03-27 07:38:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9241601/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9241601/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107993889,"identity":"b09163f2-2c9d-405b-b279-046dbc884ff1","added_by":"auto","created_at":"2026-04-28 10:41:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49935,"visible":true,"origin":"","legend":"\u003cp\u003eSome samples of distribution of each country on the EKC curve\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9241601/v1/9780533bfaba292fc3632b77.png"},{"id":107993967,"identity":"e3b287aa-da22-4237-9b93-a7ffd3977859","added_by":"auto","created_at":"2026-04-28 10:41:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":436171,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9241601/v1/d1a27570-8855-4cfa-9467-51d6d4d87bc8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tourism income and ecological footprint reveal an environmental Kuznets curve independent of income level","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the last century, concerns about greenhouse gases (GHGs) have increased as they are considered a major cause of global warming and disruption of regional ecological balance. Therefore, achieving economic sustainability with minimal environmental cost has become an important issue of this century.\u003c/p\u003e \u003cp\u003eIn recent years, many studies discussing the relationship between GDP and environmental pollution based on the environmental Kuznets curve (EKC) model, which hypothesizes an inverted U-shaped relationship between economic growth and environmental pollution (Grossman and Krueger, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), have found that an increase in tourism revenue significantly mitigates environmental pollution. This might indicate that, compared to overall GDP, the growth in the proportion of tourism revenue has a more direct impact on reaching the turning point of the EKC. The tourism industry accounted for 8% of global CO2 emissions in 2013, and it continues to grow. (Ișik, et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) And as G\u0026ouml;ssling, S. (2002) mentioned, the relationship between the environment and tourism is inextricably linked. Tourism constantly affects the environmental conditions through changes in land cover and land use, energy consumption, biotic exchange and extinction of wild species, the spread and dispersion of diseases, changes in the perception and understanding of the environment, and water resource usage. Therefore, studying the impact of tourism on the EKC allows us to understand the mechanisms of economic activities on the environment in a more multidimensional way.\u003c/p\u003e \u003cp\u003eMany past studies have confirmed the existence of the EKC, whether in terms of tourism revenue and environmental pollution or overall GDP and environmental pollution. However, we believe that compared to overall GDP, the impact of tourism revenue is more direct. This is because the development of tourism often brings increased environmental pressures, such as resource consumption and pollution emissions. Additionally, an increase in tourists often leads to overdevelopment and ecological damage to scenic spots. According to the 2020 annual report by the World Tourism Organization, transport-related CO\u003csub\u003e2\u003c/sub\u003e emissions from tourism are expected to account for 5.3% of all emissions by 2030, and emissions from international tourism are predicted to grow 45% during 2016\u0026ndash;2030. In addition to transportation, tourism influences carbon emissions during the process of supporting accommodation, producing souvenir, and other tourism behavior, which have a huge demand of consumption for fossil fuels and electricity power, establishing an association between tourism, energy use, and carbon emissions (Dogan and Aslan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Seyfettin Erdoğan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, with the further development of tourism, the country's industrial structure tends to shift towards a service-oriented tertiary industry. This not only signifies the improvement of the country's level of development but is also often accompanied by an increase in environmental awareness among the people. Furthermore, it provides funding and more effective policy support for achieving sustainable development goals. All these factors contribute to reaching the turning point of the EKC. Compared to this, the impact of overall GDP, which is the sum value-added of various industries, For example, agriculture, manufacturing, and tourism, is more complex. For instance, Boluk and Karaman (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) confirmed in their study that agriculture exacerbates environmental pollution in Turkey, while Selcuk et al. (2024) found that agriculture reduces environmental pollution in eleven countries, including Bangladesh and Turkey. Stavropoulos, P. \u0026amp; Chryssolouris, G(2022)discussed the impact of the manufacturing industry on carbon emissions across various EU countries, emphasizing the importance of carbon emission factors at different manufacturing levels, and pointed out that both electricity consumption and the use of recyclable materials can effectively affect the carbon footprint. Not to mention, tourism has become one of the most important forces for economic growth in many developing and developed countries (De Vita et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Katircioglu, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and has contributed to economic growth from several perspectives (Alam and Paramati, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, we have reason to believe that studying the impact of specific industries like tourism on the EKC is more effective than studying overall GDP. This approach also helps us better understand the roles various industries play in environmental impact, aiding in the formulation of future development plans.\u003c/p\u003e \u003cp\u003eIn primary research, environmental pollution is proxied by CO\u003csub\u003e2\u003c/sub\u003e or SO\u003csub\u003e2\u003c/sub\u003e emissions and economic growth is proxied by gross domestic product (GDP). However, this is insufficient because environmental pollution should not be measured using emissions for only one or two gases. From a statistical perspective, an indicator representing environmental pollution more comprehensively and appropriately is required to determine the association between economic growth and environmental pollution.\u003c/p\u003e \u003cp\u003eIn this study, we selected the ecological footprint (EF) as a measure of environmental degradation, which uses the equivalent requirements of forest, land, and other natural resources rather than waste emissions. The EF has already been characterized as an appropriate representative indicator of the environment by many researchers, such as Destek et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Altintas and Kassouri (2020), and Destek and Sarkodie (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and involves six components: cropland, grazing land, fishing grounds, forest land, built-up land, and carbon footprint (including CO\u003csub\u003e2\u003c/sub\u003e emissions) (Destek et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Compared with gas emissions, this can be a full-scale approach to answer the question regarding the existence of the EKC. This paper examines the relationship between tourism and environmental pollution and further discusses the significance of tourism and income levels in relation to environmental pollution, and discovered an EKC relationship between tourism revenue and ecological footprint in 78 countries worldwide, Furthermore, confirming that tourism revenue contributes more significantly to environmental pollution than overall income.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eThis section reviews existing studies on the nexus between economic growth, environmental pollution, and energy consumption. Since the conception of the EKC by Grossman and Krueger (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), numerous empirical studies have been conducted, especially over the last four decades. Data in different countries and regions were analyzed through different methodologies and models, revealing the existence of an inverted U-shaped relationship between the environmental degradation indicators and economic growth. This means that the correlation between environmental pollution and economic growth will be negative after the economies of those countries developed to a certain level, even though environmental pollution is now gradually increasing with economic growth in some countries. Specifically, economic growth will become environment friendly.\u003c/p\u003e\n\u003cp\u003eMost studies have used CO\u003csub\u003e2\u003c/sub\u003e emissions as environmental degradation indicators as the first step of research on the EKC. However, several arguments remain unresolved. These studies focused on a unique country and assert that an inverted U-shaped relationship exists between CO\u003csub\u003e2\u003c/sub\u003e emissions and economic growth in some developing countries such as Malaysia and China (Ahmad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ahmed and Long, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ali et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jalil and Mahmud, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mahmood and Alkhateeb, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mrabet and Alsamara, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shahbaz et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tiwari et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003el\u0026uuml;k and Mert, 2015). Al-Mulali et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e), Lacheheb et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Ozturk and Al-Mulali (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Saboori and Sulaiman (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and Zambrano-Monserrate et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) disagree and prove the existence of a U-shaped or even a linear relationship between CO\u003csub\u003e2\u003c/sub\u003e emissions and economic growth using data from China, Malaysia, Peru, Cambodia, and some other countries. Regarding research on a group of countries/regions, Fakih and Marrouch (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) argue that an inverted U-shaped relationship does not exist between environmental indicators and economic growth using data from the 10 Middle East and Northern Africa (MENA) countries, whereas Farhani et al. (2014) find the EKC in the MENA countries. Simultaneously, Shahbaz et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) provide mixed results, concluding that the EKC is observed in higher income countries through empirical research using data from 86 countries.\u003c/p\u003e\n\u003cp\u003eAs mentioned in Section 1, indicators such as CO\u003csub\u003e2\u003c/sub\u003e emissions represent only a small proportion of the total environmental pollution and damage. To achieve more complete and robust results, the EF is proposed that received substantial attention. Destek and Sinha (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Ozturk et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Wang et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Bagliani et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), Mrabet and Alsamara (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Al-Mulali et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and Charfeddine and Marabet (2017) apply the EF data as an environmental degradation indicator and analyze the relationship between environmental pollution and economic growth using data from the MENA or other groups of countries and regions in different periods. The statistical methodologies used in these studies include, but are not limited to, system generalized method of moments (SYS-GMM), FMOLS, DOLS, and ARDL. The indicator of economic growth was not only GDP or GDP per capita but also the annual receipt of tourism.\u003c/p\u003e\n\u003cp\u003eAccording to World Tourism Organization, carbon emissions from tourism cause environmental damage substantially, and tourist industries have become significant contributors in the GDP of both developed and developing countries. However, the conclusions of these studies are complex. Katircioglu et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) estimate that the EKC uses the EF and GDP data from top 10 tourist destination during 1995\u0026ndash;2014 and confirm an inverted U-shaped relationship between economic growth and environmental degradation. Additionally, Liu et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) verify an inverted U-shaped relationship between travel and tourism and the EF in Pakistan with data for 1980\u0026ndash;2017. They also provide evidence to support the \u0026ldquo;pollution haven hypothesis\u0026rdquo; by proving that direct foreign investment has a positive influence on environmental degradation in Pakistan. However, some studies have verified a U-shaped relationship between the EF and economic growth. For example, Lee and Chen (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) divide the EF into six parts and indicate that a U-shaped relationship generally exists between the EF and either GDP or international tourism receipt when the EF is applied to carbon-absorption land, cropland, and fishing grounds from 123 countries during 1992\u0026ndash;2016 using the quantile regression approach. Specifically, the existence of the EKC cannot be confirmed in every study. Some studies still argue whether the existence of inverted U-shaped relationship depends on the level of income or development of such countries or regions (Al-Mulali et al., 2015; Ozturk et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, these conclusions are uncertain. Based on the estimation results of Ozturk et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), some low-income countries, such as Ethiopia, demonstrate an EKC relationship between their economic growth and environmental pollution indicators.\u003c/p\u003e\n\u003cp\u003eAlthough the overall discussion indicates the relationship between the Ecological Footprint (EF) and tourism receipts or GDP, the question still persists on how to achieve an EKC relationship between economic growth and environmental degradation within a country. Should the level of income be responsible for facilitating the EKC relationship? For the EKC, is the overall GDP/income level more important, or is the income from a specific industry (e.g., tourism income) more crucial? If so, should governments focus on enhancing the overall income level or promoting a specific industry? What is the actual reason for determining the existence of the EKC, and how can we achieve a path of development similar to the EKC? In this study, we combined data from countries and regions that had an EKC relationship to determine whether the level of income is necessary for the EKC (see Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\" width=\"100%\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSome main reference of this paper\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAuthors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eEKC Result\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOzturk et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eTachega et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eAzam and Khan (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eHandoyo et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eKatircioglu et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eOchoa-Moreno et al.(2022)\u003c/p\u003e\n \u003cp\u003eSharif et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eAkadiri et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eZaman et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eAnser et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eIșik et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eAziz et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eAl-mulali et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eDestek and Sinha (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e144 countries\u003c/p\u003e\n \u003cp\u003e54 African economies\u003c/p\u003e\n \u003cp\u003eTanzania, Guatemala, China and the USA,\u003c/p\u003e\n \u003cp\u003eNot-mentioned\u003c/p\u003e\n \u003cp\u003e10 countries\u003c/p\u003e\n \u003cp\u003e20 Latin American countries\u003c/p\u003e\n \u003cp\u003eMalaysia\u003c/p\u003e\n \u003cp\u003e15 countries\u003c/p\u003e\n \u003cp\u003e34 countries\u003c/p\u003e\n \u003cp\u003eG7 countries\u003c/p\u003e\n \u003cp\u003eG7 countries\u003c/p\u003e\n \u003cp\u003eBRICS countries\u003c/p\u003e\n \u003cp\u003e58 countries\u003c/p\u003e\n \u003cp\u003e24 OECD countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1988\u0026ndash;2008\u003c/p\u003e\n \u003cp\u003e1990\u0026ndash;2015\u003c/p\u003e\n \u003cp\u003e1975\u0026ndash;2014\u003c/p\u003e\n \u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e\n \u003cp\u003e1995\u0026ndash;2014\u003c/p\u003e\n \u003cp\u003e1995\u0026ndash;2018\u003c/p\u003e\n \u003cp\u003e1995Q1-2018Q4\u003c/p\u003e\n \u003cp\u003e1995\u0026ndash;2014\u003c/p\u003e\n \u003cp\u003e2005\u0026ndash;2013\u003c/p\u003e\n \u003cp\u003e1995\u0026ndash;2015\u003c/p\u003e\n \u003cp\u003e1995\u0026ndash;2015\u003c/p\u003e\n \u003cp\u003e1995\u0026ndash;2018\u003c/p\u003e\n \u003cp\u003e1980\u0026ndash;2009\u003c/p\u003e\n \u003cp\u003e1980\u0026ndash;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eEF TD TD\u003csup\u003e2\u003c/sup\u003e URB TRD ECO\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e ARG RECONRECO GDP GDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e ECO GDP\u003c/p\u003e\n \u003cp\u003eGDP\u003csup\u003e2\u003c/sup\u003eTRD URB\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e ECO GDP\u003c/p\u003e\n \u003cp\u003eGDP\u003csup\u003e2\u003c/sup\u003e TD TD\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eFDI EX IM\u003c/p\u003e\n \u003cp\u003eEF GDP GDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eURB TD REEX\u003c/p\u003e\n \u003cp\u003eECO\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e TD TD\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e GDP GDP\u003csup\u003e2\u003c/sup\u003e TD TRS GLO\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e GDP GDP\u003csup\u003e2\u003c/sup\u003e TD ECO GLO\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e GDP GDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eTD GFCF ECO\u003c/p\u003e\n \u003cp\u003eHEXP\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e GDP GDP\u003csup\u003e2\u003c/sup\u003e TD EDU HEXP\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e GDP GDP\u003csup\u003e2\u003c/sup\u003e TD RE\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e GDP GDP\u003csup\u003e2\u003c/sup\u003e TD RE\u003c/p\u003e\n \u003cp\u003eEF GDP GDP\u003csup\u003e2\u003c/sup\u003e TD RE URB\u003c/p\u003e\n \u003cp\u003eEF GDP GDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eRE NRECO TRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eExisted, income group specific\u003c/p\u003e\n \u003cp\u003eExisted but not relatedto income\u003c/p\u003e\n \u003cp\u003eExisted, income groupspecific\u003c/p\u003e\n \u003cp\u003eIncome group specific for both TD and GDP\u003c/p\u003e\n \u003cp\u003eExisted\u003c/p\u003e\n \u003cp\u003eExisted, income groupspecific\u003c/p\u003e\n \u003cp\u003eExisted\u003c/p\u003e\n \u003cp\u003eExisted\u003c/p\u003e\n \u003cp\u003eExisted\u003c/p\u003e\n \u003cp\u003eExisted\u003c/p\u003e\n \u003cp\u003eExisted except Italy\u003c/p\u003e\n \u003cp\u003eExisted\u003c/p\u003e\n \u003cp\u003eNot existed\u003c/p\u003e\n \u003cp\u003eExisted in 8 countries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: TD: tourism development proxy; GDP: real GDP; EF: ecological footprint; URB: urbanization proxy; RE: renewable energy; NRECO: non-renewable energy; TRD: trade openness; ECO: energy consumption; REEX: real effective exchange rate index; FDI is Foreign direct investment; EX: export; IM: import; TRS: transportation service; GLO: globalization; GFCF: gross fixed capital formation; HEXP: health expenditure per capita; EDU: education expenditure.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e[Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/h2\u003e\n \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\n \u003ch2\u003eData and Methodology\u003c/h2\u003e\n \u003cp\u003eThe annual data on the ecological footprint (EF) were obtained from the Global Footprint Network (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.footprintnetwork.org\u003c/span\u003e\u003c/span\u003e). Data on tourism receipts, real GDP, and urban population were collected from the World Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.worldbank.org\u003c/span\u003e\u003c/span\u003e). Energy consumption data were obtained from the U.S. Energy Information Administration (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eia.gov\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eGiven our main objective, we require a model based on the EKC hypothesis. This study\u0026rsquo;s model is based on Ozturk et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Ahmad et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Shahbaz et al. (2013):\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:{lnEF}_{t}={f(lnTGDP}_{t},\\:{lnTGDP}_{t}^{2},\\:{lnURB}_{t},{lnECO}_{t},{{lnRGDP}_{t},lnEF}_{t-1})$$\u003c/div\u003e\u003c/div\u003e,\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003ewhere lnEF is the natural log of the EF, and lnTGDP is the natural log of tourism receipts, measured in constant US dollars. lnURB, lnRGDP, and lnECO are the natural logs of the urban population measured in millions of people, real GDP measured in US dollars, and energy consumption, respectively, which have been confirmed to be closely related to environmental degradation in previous studies and are used to build up a dynamic panel model. Thus, we add the first difference variable, lnEF\u003csub\u003et\u0026minus;1\u003c/sub\u003e, to this model.\u003c/p\u003e\u003cp\u003eThe correlation between the error term and other variables, such as lnEF\u003csub\u003et\u0026minus;1\u003c/sub\u003e on the right-hand side, is a common problem in most dynamic panel models. Instrument variables are utilized to solve this problem. However, as instrument variables are always random and have arbitrariness, it is extremely difficult to select an instrument variable that does not correlate with the error term; therefore, we proposed the dependent variable, GMM. Thus, as the different instruments in the GMM are usually weak, we applied the SYS-GMM suggested by Arellano and Boverover (1995) and Blundell and Bond (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) to estimate our model. The SYS-GMM can be regarded as an evolution of DIF-GMM containing regression and instrumental variables at both levels and differences. In addition, the SYS-GMM contains a two-step Arellano-Bond test for the autocorrelation of the error term, and the validity of the instruments is tested using both the Hansen and Sargan tests. Our GMM model is as follows:\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:{lnEF}_{it}={{\\alpha\\:}_{it}＋{\\beta\\:}_{1}lnTGDP}_{it}＋\\:{\\beta\\:}_{2}{lnTGDP}_{it}^{2}＋\\:{{\\beta\\:}_{3}lnURB}_{it}＋{{\\beta\\:}_{4}lnECO}_{it}＋{{\\beta\\:}_{5}lnRGDP}_{it}＋\\gamma\\:{lnEF}_{t-1}＋{\\epsilon\\:}_{it},$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003ewhere \u0026beta;\u003csub\u003e1\u003c/sub\u003e, \u0026beta;\u003csub\u003e2\u003c/sub\u003e, \u0026beta;\u003csub\u003e3\u003c/sub\u003e, \u0026beta;\u003csub\u003e4\u003c/sub\u003e and \u0026beta;\u003csub\u003e5\u003c/sub\u003e are the coefficients of each dependent variables, and \u0026epsilon;\u003csub\u003eit\u003c/sub\u003e is an error term of this model. i depends on the cross-sectional number of our data, which refers to the number of countries, and t represents the period from 1999 to 2018. The EKC exists if \u0026beta;\u003csub\u003e1\u003c/sub\u003e \u0026gt; 0 and \u0026beta;\u003csub\u003e2\u003c/sub\u003e \u0026lt; 0. Regarding the selection of endogenous and instrumental variables, during the estimation for all data and subdatasets, we chose lnECO and lnURB as the endogenous variables; however, we selected lnRGDP as an instrumental variable in all datasets and lnTGDP in the subdataset. Additionally, we distinguished between these variables based on previous experience. Furthermore, if \u0026beta;\u003csub\u003e5\u003c/sub\u003e is negative and significant during the estimation utilizing our two datasets, the level of income should be recognized as an important component of the EKC. In contrast, the level of income should be considered a less important part of the EKC compared to tourism receipt.\u003c/p\u003e\u003cp\u003eTo further substantiate our conclusion, we employed Dominance Analysis(Gr\u0026ouml;mping, U. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)) to assess the contribution of each variable in the linear model to both the model itself and the explained variable.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Empirical Result and Discussion","content":"\u003cp\u003eWe estimated the existence of the EKC using all datasets that contained data of 78 countries\u0026rsquo; EF, tourism receipts, energy consumption, and urban population from 1999 to 2018. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides the results obtained from the SYS-GMM.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSYS-GMM result for all data from 78 countries\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCoef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\n \u003cp\u003eAll data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003elntgdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\n \u003cp\u003e0.0918917**(0.036)\u003c/p\u003e\n \u003cp\u003e-0.0108863***(0.000)\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e-0.081901(0.161)\u003c/p\u003e\n \u003cp\u003e0.0372897(0.504)\u003c/p\u003e\n \u003cp\u003e0.7248755***(0.000)\u003c/p\u003e\n \u003cp\u003e0.1029155(0.375)\u003c/p\u003e\n \u003cp\u003e-0.4164117(0.610)\u003c/p\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003elntgdp2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003elnurb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003elnecp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003elagEF\u003c/p\u003e\n \u003cp\u003elnrgdp\u003c/p\u003e\n \u003cp\u003econstant\u003c/p\u003e\n \u003cp\u003eAR(2)\u003c/p\u003e\n \u003cp\u003eHansen test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e[Table 2 about here]\u003c/h3\u003e\n\u003cp\u003eThe estimates in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e suggest that an EKC relationship exists in 78 countries between tourism receipt and the EF. We observe that the coefficient for the squared tourism receipt is negative at the 1% significance level, implying that environmental degradation decreases with the development of tourism after tourism receipt reaches the turning point of the EKC. Furthermore, this result suggests that countries\u0026rsquo; policies for promoting the tourism industry achieve an ecofriendly development method. Moreover, our results indicate that the lag of the EF enhances environmental degradation at the 1% significance level; however, the constant term and coefficient for energy consumption and urban population in 78 countries are nonsignificant, which means that such a link is nonexistent between population urbanization and ecological degradation, similar to Xu et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFurthermore, to further determine the importance of the affluence of countries in the EKC model, we narrowed our dataset to 19 countries that have been proven to exhibit the EKC relationship, categorized them based on the results of Ozturk et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our new dataset contained data from Ethiopia, Madagascar, Guatemala, Pakistan, Argentina, Azerbaijan, Brazil, Mexico, Romania, South Africa, Chile, Greece, Israel, Kuwait, Norway, Singapore, Switzerland, the United Arab Emirates, and Peru. As shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Madagascar and Ethiopia are defined as low-income countries; Guatemala and Pakistan are defined as lower middle-income countries; Argentina, Brazil, Azerbaijan, Mexico, Peru, Romania, and South Africa are defined as upper middle-income countries; and Chile, Israel, Kuwait, Norway, Greece, Singapore, Switzerland, and the United Arab Emirates are defined as high-income countries.\u003c/p\u003e\n\u003cp\u003eUtilizing the data of 19 countries with the SYS-GMM through the same model of all data, Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e reveals that an inverted U-shaped curve exists between tourism receipt and the EF, even if the significance for squared tourism receipt is reduced to the 5% level, while the probability is approximately 1%. Moreover, in this case, the coefficient of the urban population becomes significant and reduces environmental degradation by 5%. This result is similar to that of Farooq and Dar (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) but opposite to that of Ozturk et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), where agglomeration effects may be devoted to this result. Moreover, Zhou et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) assert that social urbanization exerts a negative impact on environmental degradation by increasing public awareness. Simultaneously, higher urbanization may contribute to a higher level of education, which, in turn, promotes education. This result also impels policymakers and governments to pay more attention to their urbanization plans, such as utilizing agglomeration effects to accelerate technology development, popularizing higher education, discontinuing the endless urban expansion, and increasing the forest area, as valid urban policy may contrarily reduce environmental degradation for countries. Moreover, propagating electric vehicle and public transportation to achieve low-carbon travel will effectively reduce environmental degradation. Meanwhile, in the case of the 19 countries, the coefficients for energy consumption and real GDP are nonsignificant.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSYS-GMM result for the 19 countries\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCoef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\n \u003cp\u003e19 countries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003elntgdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\n \u003cp\u003e0.1953924**(0.045)\u003c/p\u003e\n \u003cp\u003e-0.0148062**(0.011)\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e-0.1540909**(0.044)\u003c/p\u003e\n \u003cp\u003e0.1701179(0.147)\u003c/p\u003e\n \u003cp\u003e0.5471615***(0.001)\u003c/p\u003e\n \u003cp\u003e-0.1172484(0.465)\u003c/p\u003e\n \u003cp\u003e1.497409 (0.224)\u003c/p\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003elntgdp2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003elnurb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003elnecp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003elagEF\u003c/p\u003e\n \u003cp\u003elnrgdp\u003c/p\u003e\n \u003cp\u003econstant\u003c/p\u003e\n \u003cp\u003eAR(2)\u003c/p\u003e\n \u003cp\u003eHansen test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e[Table 3 about here]\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eComparing the results in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e with Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, both the estimates reveal the existence of the EKC. However, the axis of symmetry moved from 4.22 to 6.60 when our data ranged from 78 to 19 countries. This reveals that a higher level of tourism receipt helps countries reach the inflection point of the EKC earlier, to a certain extent, explaining the importance of tourism receipt in the composition of the EKC and suggesting that the government should publish policies, such as redistributing income between poor and affluent people (Anser et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to promote tourism in these countries. Regarding the coefficient of real GDP, in both estimations, there was no evidence of the effect of real GDP/income on the EKC, as all the results indicated that the coefficient for real GDP was nonsignificant. This implies that in both sets of empirical analyses, income level is considered less important in the composition of the EKC compared to tourism receipt.\u003c/p\u003e\n\u003cp\u003eTo corroborate this result, we discussed the contribution of each variable in the model to the Ecological Footprint (EF), with the results presented in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDominance analysis result\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDominance stat.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eRanking\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\n \u003cp\u003elnecop\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003elntgdp\u003c/p\u003e\n \u003cp\u003elnrgdp\u003c/p\u003e\n \u003cp\u003elnecp\u003c/p\u003e\n \u003cp\u003eurb\u003c/p\u003e\n \u003cp\u003elagEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.1245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.1152\u003c/p\u003e\n \u003cp\u003e0.1543\u003c/p\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003cp\u003e0.5976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e[Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e about here]\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of dominance analysis. The results indicate that the dominance coefficient for `lntgdp` is 0.1245, which is higher than that of `lnrgdp` at 0.1152. This suggests that, compared to income level, tourism income makes a greater contribution to the model\u0026apos;s fit statistic for ecological footprint, indicating a stronger influence. This finding underscores the higher importance of tourism income over income level in explaining the predictive capability of the model.\u003c/p\u003e\n \u003cp\u003eFurthermore, as shown in Tables \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, referring to Ozturk et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), a significant proportion of our two datasets consist of countries that are not defined as high-income countries. Low-income and middle-income countries are the major components of our data. However, some countries are not mentioned in the literature: Albania, Armenia, Bahamas, The., Belarus, Bhutan, Bosnia and Herzegovina, Croatia, Dominica, El Salvador, Kazakhstan, Moldova, Philippines, the Slovak Republic, Slovenia, and St. Lucia. Most countries are well known as developing countries with low or middle incomes; therefore, it is reasonable to believe that income level is not particularly crucial in the form of the EKC.\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComposition of all data\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLow income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eBenin, Ethiopia, Haiti, Kenya, Madagascar, Malawi, Mali, Nepal,\u003c/p\u003e\n \u003cp\u003eNiger, Sierra Leone, Tanzania, Togo, Zimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLower middle\u003c/p\u003e\n \u003cp\u003eincome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eBolivia, Cameroon, Cote d\u0026apos;Ivoire, Ghana, Guatemala, Mongolia,\u003c/p\u003e\n \u003cp\u003eNigeria, Pakistan, Paraguay, Senegal, Sri Lanka,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUpper middle\u003c/p\u003e\n \u003cp\u003eincome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eAngola, Argentina, Azerbaijan, Botswana, Brazil, Colombia,\u003c/p\u003e\n \u003cp\u003eCosta Rica, the Dominican Republic, Fiji, Jordan, Malaysia, Mexico,Panama, Peru, Romania, South Africa, Thailand, Tunisia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eAustralia, Bahrain, Chile, Denmark, Finland, France,Germany,\u003c/p\u003e\n \u003cp\u003eGreece, Israel, Japan, Korea, Rep., Kuwait, New Zealand, Norway, Oman, Poland, Portugal, Singapore, Switzerland,\u003c/p\u003e\n \u003cp\u003ethe United Arab Emirates, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNot mentioned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eAlbania, Armenia, Bahamas, The., Belarus, Bhutan, Bosnia and Herzegovina, Croatia, Dominica, El Salvador, Kazakhstan, Moldova, Philippines, the Slovak Republic, Slovenia, St. Lucia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComposition of sub dataset\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLow income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003eMadagascar, Ethiopia,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLower middle income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003eGuatemala, Pakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUpper middle\u003c/p\u003e\n \u003cp\u003eincome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003eArgentina, Brazil, Azerbaijan, Mexico, Peru, Romania, South Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003eChile, Israel, Kuwait, Norway, Greece, Singapore,\u003c/p\u003e\n \u003cp\u003eSwitzerland, the United Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e[Table 5 about here]\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch3\u003e[Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e about here]\u003c/h3\u003e\n \u003cp\u003eAfter confirming the existence of the EKC, we observed the distribution of each country on the EKC curve, and some of the samples are as follows:\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003cp\u003eThese countries can be classified into three types: First, the countries that have already exceeded the inflection such as South Africa, Tunisia, Botswana, Korea, Rep., Dominica, St. Lucia, the Dominican Republic, Costa Rica, Panama, El Salvador, Bahamas, The., Switzerland, France, Poland, Denmark, Portugal, Greece, the Slovak Republic, Norway, Croatia, Albania, Finland, Slovenia, Germany, Mexico, United States, Australia, New Zealand, Fiji, Malaysia, Thailand, Singapore, Chile, Bahrain, Kuwait, Oman, the United Arab Emirates, Israel, and Jordan; these countries are gradually achieving an environment-friendly society. Second, countries located near the inflection point include Kazakhstan, Japan, Mongolia, Armenia, Sri Lanka, Bhutan, Azerbaijan, Guatemala, Belarus, Romania, Moldova, Bosnia and Herzegovina, Philippines, Argentina, Colombia, Peru, and Bolivia; these countries\u0026rsquo; efforts are not sufficient to let their environmental degradation gradually disappear with the development of tourism receipt. Finally, countries that have not attached an inflection point include Ghana, Angola, Togo, Sierra Leone, Kenya, Cote d\u0026rsquo;Ivoire, Cameroon, Ethiopia, Mali, Benin, Zimbabwe, Malawi, Niger, Tanzania, Senegal, Madagascar, Nigeria, Pakistan, Nepal, Haiti, Brazil, and Paraguay; these countries may need other policies to approach the goal of eco-friendly development. Our results indicate that countries exceeding the inflection point does not necessarily comprise developed countries; for example, Jordan, which is a well-known developing country. Furthermore, being a developed country, Japan is \u0026ldquo;on\u0026rdquo; the inflection point of the EKC curve. Thus, it is difficult to assert the importance of income level for the EKC. From another perspective, the EKC is absent in many African countries, and geographic factors should be considered in future studies.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion and Future Consideration","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003cp\u003eThis study examined the relationship between the EF and tourism receipt using the SYS-GMM. We used real GDP, urban population, energy consumption, and the first difference in the EF as independent variables in our estimation. Two datasets were created to achieve these objectives. One dataset included 78 countries from 1999 to 2018, and the other included data from 19 of these countries over the same period. These datasets were used to determine the existence of the EKC between tourism receipts and EF, and to verify the importance of tourism income and overall income levels in the formation of the EKC. Our results can be summarized as follows. First, for tourism receipt, several studies have confirmed that environmental degradation is strongly related to the tourism receipt. Some studies, such as Akadiri et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Sharif et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), prove that the relationship between environmental degradation and tourism income is negative, which means that the development of tourism reduces pollution to a certain extent. However, others such as Zaman et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Khalid Anser et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and De Vita et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) confirm that tourism increases the environmental degradation in some areas/regions. Ișik et al.\u0026rsquo;s (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) result is complex and depends on the target countries. Nevertheless, the tourism industry is closely related to environmental pollution. Moreover, as a sector it may be able to make a significant contribution to economic growth (Katircioglu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this study, following Ozturk et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Ochoa-Moreno et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Handoyo et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and Kyara et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), we applied tourism receipt and its square as independent variables using an EKC model and confirmed an inverted U-shaped relationship between tourism receipt and environmental degradation. This means that environmental degradation first increases and then decreases after tourism receipt attaches a specific level with the development of the tourism industry.\u003c/p\u003e\n \u003cp\u003eSecond, in previous studies, income level is considered a determinant of the existence of the EKC (Van Alstine and Neumayer, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The significance of a polynomial with real GDP in regression usually becomes stronger when moving research object from poor countries to high-income countries (Magnani, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). As mentioned before, when income grows, more efficient and cleaner technology is adopted by people (Dinda, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and people\u0026rsquo;s demand for a better living environment also induces the structural change, leading economy to be \u0026ldquo;greener\u0026rdquo; and more environment friendly with the income growth (Borghesi, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). When utilizing tourism receipt squared as an independent variable, Ozturk et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) also conclude that the EKC frequently appeared in upper middle-income countries and high-income countries. However, to determine whether the level of income affects the existence of the EKC, we used real GDP as an income indicator and created a subdataset by considering countries that had different income levels and an inverted U-shaped link between tourism receipt and environmental degradation. We observed that compared to income levels, tourism income had a more significant impact on environmental pollution and the EKC; the EKC existed in both datasets. Moreover, Azam and Khan (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) indicate the presence of the EKC in low- and lower middle-income countries, and Tachega et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) note that the EKC phenomenon is not income group specific and can occur in any country or region. This phenomenon may be due to the following reasons:\u003c/p\u003e\n \u003cp\u003e1. The type of major industry, rather than the income level, determines the existence of the EKC. Qatar, a developing country, obtains income mainly from the petrochemical industry at the cost of severe environmental pollution, whereas Germany, a developed country where the manufacturing industry constitutes over 20% of the GDP, also causes heavy pollution. In the nexus between economic growth and environmental degradation, the concepts of development and income must be distinguished explicitly. Development accompanied by innovation may be helpful for environmental management; for example, Gormus and Aydin (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) state that innovation is significantly devoted to reducing environmental degradation in Korea, USA, and Finland. However, no evidence exists to prove that high income directly affects environmental degradation.\u003c/p\u003e\n \u003cp\u003e2. Compared with the income of countries, policies and other factors may influence procedures such as energy use and carbon emissions, which are related to environmental degradation directly and validly. Onafowora and Owoye (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) mention that increasing income should not be considered a solution for environmental degradation, as it can be a statistical artifact; some countries cannot even achieve the turning point in several decades. Our study also provides evidence for this conclusion, as in both datasets, the coefficient of real GDP is nonsignificant.\u003c/p\u003e\n \u003cp\u003eIn determining the relationship between the existence of the EKC and income level, some questions should be highlighted in future studies. As previously mentioned, the EF comprises six parts: cropland, grazing land, fishing grounds, forest land, built-up land, and carbon footprint. Past studies have proven the relationship between carbon emissions and GDP when discussing the existence of the EKC using CO\u003csub\u003e2\u003c/sub\u003e emissions as an indicator, which means that a part of the EF is closely related to the income level. However, when all these components are combined, they become dissimilar. Therefore, other factors associated with the EF should be considered in future studies. Moreover, not only tourism but different economic sectors often have varying impacts on the EKC at different stages of economic development, making the related topics extremely complex. Pata (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) discusses the relationship between agricultural value added and whether CO\u003csub\u003e2\u003c/sub\u003e emissions or the EF in BRIC countries are caused by bidirectional causality between agriculture and environmental degradation. Moreover, Lee and Chen (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) analyze the relationships between tourism and each component of the EF, proving that an inverted U-shaped relationship exists in grazing land and forest land; and U-shaped relationships exists in carbon-absorption land, cropland, and fishing ground. Furthermore, in future research, similar to the findings of this study, there may be cases where the importance of various types of income surpasses income levels. In some instances, the significance of income levels may even outweigh other types of income such as tourism receipt. Many advantages will emerge after refining these problems. For example, by determining whether and how agriculture increases the environmental degradation of cropland and fishing land, targeted policies can be implemented, which will be helpful in solving the deficit of environmental capacity. Using the EF as a consistent variable, resolving problems in any region/country will be effective and referential for other regions/countries.\u003c/p\u003e\n \u003cp\u003eBased on the aforementioned conclusions, other recommendations for future research can also be drawn. First, it is necessary to distinguish between renewable and nonrenewable energy consumption, although there have been many studies, such as Handoyo et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), proving that energy use has a significant positive effect on environmental degradation. According to Katircioglu et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), the tourism industry always increases environmental pollution by path costing plenty of energy, such as electricity, fossil oil, and natural gas, which are mostly produced from nonrenewable energy. The contradictory effects of renewable and nonrenewable energy on the environment are validated by multiple researchers, such as Sulaiman et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Jebli and Youssef (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This is the major reason for the coefficient of energy use in our study to be nonsignificant in all cases. It seems more suitable to use only a part of the energy consumption as an indicator. Second, as the determinants of the EKC remain inconclusive, other factors should be considered. Variables for the validity of policy, similar to our aforementioned indicators measuring the degree of development of countries, should be created instead of income. These indicators should include more factors other than technical innovation, education level, people\u0026rsquo;s affluence, and so forth. These unanimous variables will be extremely helpful in future studies regarding the \u0026ldquo;harmony\u0026rdquo; between economic growth and environmental degradation. Third, as mentioned previously, new technologies adopted by people can significantly affect the EKC; however, in the EKC model, the time point and efficiency of technology are ambiguous. Stern (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) mentions that a new technology innovation is usually adopted first in high-income countries and then in low-income countries, but in the model of EKC, it is difficult to determine \u0026ldquo;how\u0026rdquo; and \u0026ldquo;when\u0026rdquo; the innovation affect our dataset. Thus, the EKC model and other models for testing the link between economic growth and environmental degradation should be more comprehensive. Finally, to determine several reasons affecting the EKC, research on specific industries, such as tourism, should be devoted to future studies to assist policymakers in formulating economic development strategies that are tailored to their country\u0026apos;s specific conditions, rather than simply aiming to increase income.. Although there are studies on agriculture and tourism, compared with the research focused on the link between GDP and the environment/income, the existing studies are inadequate.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eData availability\u003c/h2\u003e\n \u003cp\u003eThe datasets used and analyzed during the current study are publicly available from the following sources: Ecological footprint data are available from the Global Footprint Network (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.footprintnetwork.org\u003c/span\u003e\u003c/span\u003e). Tourism receipts, GDP, and urban population data are available from the World Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.worldbank.org\u003c/span\u003e\u003c/span\u003e). Energy consumption data are available from the U.S. Energy Information Administration (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eia.gov\u003c/span\u003e\u003c/span\u003e). All data are openly accessible and can be obtained without restriction.\u003c/p\u003e\u003cp\u003eThe datasets used and analyzed during the current study are publicly available from the World Bank, Global Footprint Network, and other publicly accessible databases.\u003c/p\u003e\n\u003c/div\u003e\u003cp\u003e \u003ch2\u003eConsent to participate\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003eThis study does not involve human participants or animals. Therefore, ethics approval is not required.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXiaoning Wang conceived the study, conducted the empirical analysis, and wrote the original manuscript. Shingo Takagi supervised the research, provided guidance on the study design, and revised the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad N, Du L, Lu J, et al. Modelling the CO\u003csub\u003e2\u003c/sub\u003e emissions and economic growth in Croatia: Is there any environmental Kuznets curve? Energy. 2017;123:164\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed K, Long W. Environmental Kuznets curve and Pakistan: An empirical analysis. 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Sci Total Environ. 2019;675:472\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Environmental Kuznets curve, ecological footprint, tourism receipt, income degree","lastPublishedDoi":"10.21203/rs.3.rs-9241601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9241601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the existence of the hypothesis of the environmental Kuznets curve (EKC) between tourism receipt and ecological footprint and determines the effect of income level on the existence of the EKC. We developed a model based on the EKC hypothesis, considering the ecological footprint, energy consumption, urban population, tourism receipt, and real gross domestic product as indicators for 1999\u0026ndash;2018 in 78 countries. Our results from the system generalized method of moments indicate an inverted U-shaped relationship between tourism receipt and ecological footprint in 78 countries and another dataset containing 19 countries. The study reveals that income level is not a determinant of the EKC, as the indicator for income level is nonsignificant. The results of our study have significant implications for policymakers and future studies.\u003c/p\u003e","manuscriptTitle":"Tourism income and ecological footprint reveal an environmental Kuznets curve independent of income level","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 10:39:55","doi":"10.21203/rs.3.rs-9241601/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"138359284608391133817401927773239539136","date":"2026-04-29T06:31:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-26T16:18:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80151894807137282041862416037013632121","date":"2026-04-21T05:35:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T06:18:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T06:13:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-16T08:06:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-15T17:11:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-04-15T17:06:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9bfffd22-c042-46f2-aff2-2aaf0b12a28e","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"138359284608391133817401927773239539136","date":"2026-04-29T06:31:26+00:00","index":32,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T10:39:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 10:39:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9241601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9241601","identity":"rs-9241601","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}