Does the obesity problem increase environmental degradation? Macroeconomic and social evidence from the European countries

Koengkan, Matheus; Fuinhas, José Alberto Article Does the obesity problem increase environmental degradation? Macroeconomic and social evidence from the European countries Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Koengkan, Matheus; Fuinhas, José Alberto (2022) : Does the obesity problem increase environmental degradation? Macroeconomic and social evidence from the European countries, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 10, Iss. 6, pp. 1-17, https://doi.org/10.3390/economies10060131 This Version is available at: https://hdl.handle.net/10419/328431 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Economies 10: 131. https://doi.org/ 10.3390/economies10060131 Academic Editors: Ralf Fendel, Robert Czudaj and Sajid Anwar Received: 28 March 2022 Accepted: 30 May 2022 Published: 6 June 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article Does the Obesity Problem Increase Environmental Degradation? Macroeconomic and Social Evidence from the European Countries Matheus Koengkan 1and JoséAlberto Fuinhas 2,* 1Governance, Competitiveness and Public Policies (GOVCOPP), Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT), University of Aveiro, 3810-193 Aveiro, Portugal; [email protected] 2Centre for Business and Economics Research (CeBER), Faculty of Economics, University of Coimbra, 3004-512 Coimbra, Portugal *Correspondence: [email protected] Abstract: The macroeconomic effect of the obesity epidemic on environmental degradation was examined for panel data from thirty-one European countries from 1991 to 2016. The quantile via moments model (QVM) was used to realize our empirical investigation. The empirical results indicate that the obesity epidemic, electricity consumption, and urbanisation encourage environmental degradation by increasing CO 2 emissions, while economic growth decreases them. Moreover, we identify that the obesity epidemic raises the environmental degradation problem in three ways. First, the obesity epidemic is caused by the increased consumption of processed foods from multinational food corporations. The increase in food production will positively impact energy consumption from non-renewable energy sources. Second, obesity reduces physical and outdoor activities, increasing the intensive use of home appliances and motorized transportation and screen-viewing leisure activities, consequently increasing energy consumption from non-renewable energy sources. A third possible way can be related indirectly to economic growth, globalization, and urbanisation. This empirical investigation will contribute to the literature and for policymakers and governments. Therefore, this investigation will encourage the development of initiatives to mitigate the obesity problem in European countries and accelerate the energy transition process. Finally, this investigation will open a new topic in the literature regarding the correlation between the obesity epidemic and environmental degradation. Keywords: CO 2 emissions; energy consumption; environmental degradation; European region; food production; health problem; macroeconomics; obesity 1. Introduction Climate change caused by the increase in carbon dioxide emissions (CO 2 ) is one of the biggest concerns in the European region (Bianco et al. 2019). CO 2 emissions are the most significant contributor to increased greenhouse gas emissions (GHGs), contributing 77% of GHGs. In contrast, other gases such as methane (CH 4 ), nitrous oxide (N 2 O), and ozone (O 3 ) contribute with 14%, 8%, and 1%, respectively (e.g., Koengkan and Fuinhas 2021a,2021b;Khan et al. 2014). Several initiatives have emerged to mitigate climate change (e.g., the United Nations Framework Convention on Climate Change (UNFCCC), the Earth Summit (1992), the Kyoto Protocol (1997), the 21st Conference of the Parties (COP 21) (2015), and the 26th Conference of the Parties (COP 26) (2021)). These initiatives aim to substantially limit the increase in temperature levels during this century to lower than 2 ◦ C and limit that increase to 1.5 ◦ C. These initiatives will take temperatures to pre-industrial levels. In addition, all countries that align with this agreement will move towards a low-carbon economy. Indeed, as has Economies 2022,10, 131. https://doi.org/10.3390/economies10060131 https://www.mdpi.com/journal/economies Economies 2022,10, 131 2 of 17 long been known, global GHGs, mainly CO 2 emissions, have been increasing since the 1970s (e.g., World Bank Open Data 2022;Koengkan and Alberto Fuinhas 2021b). However, from 1990 to 2016, these emissions grew dramatically, and in 1990, CO 2 emissions were 3.0991 (metric tons per capita) and reached 4.6807 (metric tons per capita) in 2016. During this period, these emissions grew from 33 megatons of CO 2 equivalent (MtCO2eq) in 1990 to 47 MtCO2eq in 2016, an annual increase of 1.5% during this period (Bárcena et al. 2019). The energy, industry, transport, and building sector have increased emissions since the 2000s. In 2010 the energy sector contributed 25%, AFOLU (agriculture, forestry and other land use) 24%, industry 21%, transport 14%, other energy sources 10%, and the building sector contributed 6% to this growth (Koengkan et al. 2020). Most of these emissions are caused by the production of electricity and heat, which emanate from the industrial and residential sectors. GHGs come about through direct emissions from fossil fuel combustion for providing power, cooling, heating, and cooking (Khan et al. 2014). As stated above, in 2010, energy consumption was responsible for 25% of global GHGs. The growth in global energy use is accountable for increasing CO 2 emissions. Energy use has been rising since the 1970s when energy use was 1337.00 (kg of oil equivalent per capita) in 1971 and reached 1897.25 in 2016 (e.g., World Bank Open Data 2022;Koengkan and Fuinhas 2021a). Indeed, 94% of this energy use in 1970 came from fossil fuels worldwide, and only 6.45% came from renewable energy. However, in 2016, the contribution of fossil fuels decreased slightly, reaching 85% of the total energy use. Indeed, this reduction is related to the increase in the share of renewable energy sources, which reached 14.35% in 2016 (Our World in Data 2022). In the European region, CO 2 emissions in 1971 were 8.0244 (metric tons per capita) and reached a value of 6.4684 (metric tons per capita) in 2016 (World Bank Open Data 2022). Therefore, between 1990 and 2004, these emissions in the European region remained relatively unchanged. However, due to the 10.8% decrease in primary energy consumption, CO 2 emissions dropped sharply between 2005 and 2016 (IEA 2020). For example, in 1990 the energy consumption was 1641 million tonnes of oil equivalent (Mtoe), while in 2004 it had already reached 1789 Mtoe. However, between 2005 and 2016, this consumption decreased and fell to 1598 Mtoe in the year 2016. The energy efficiency improvements that increased the share of renewable energy sources in the energy matrix and the changes in climate conditions were the causes for this decrease in the primary energy consumption between 2005 to 2016 for most European region countries (e.g., the European Environment Agency 2019;Eurostat 2020). In the European region, 93% of this energy use in 1970 came from fossil fuels, and only 6.90% came from renewable energy. However, in 2016 this value decreased slightly, reaching 75% of total energy use. Indeed, this reduction is related to the increase in the share of renewable energy sources in energy use, where 25% was reached in 2016 (e.g., Our World in Data 2022;Koengkan and Alberto Fuinhas 2021b). As has long been known, various drivers have been influencing the increase of CO 2 emissions. Economic growth, globalisation, trade, financial liberalisation, urbanisation, population growth and energy prices have gained notoriety. However, the literature has given little consideration to a possible relationship between the obesity epidemic problem and the increase in environmental degradation. To the best of our knowledge, the first study to address the link between obesity and climate change was made by Edwards and Roberts (2009). However, this link is very complex and is not exempt from criticism (e.g., Gallar 2010). Nevertheless, the literature remains scarce and primarily focused on the effect of obesity on climate change via CO 2 emissions. Their connections are associated with oxidative metabolic demands, food production, and fossil fuels. The links between obesity and climate change also include processed foods from fast-food and multinational supermarket chains, multinational food corporations, food production on farms, transportation of goods, retail processing and storage of processed food. These approaches also emphasise that the Economies 2022,10, 131 3 of 17 intensive use of motor vehicles and modern household appliances reduces physical effort in the context of a sedentary lifestyle (e.g., Magkos et al. 2019;Furlow 2013;Viscecchia et al. 2012;Breda et al. 2011;Edwards and Roberts 2009). Obesity is defined as abnormal or excessive fat accumulation that may impair health, that is, individuals that have a mean body mass index (BMI) ≥ 30.0, as defined by the World Health Organization (WHO). The organisation also defines ‘overweight’ as BMI ≥ 25.0 (Our World in Data 2022). In 2016, about 39% (2.0 billion) of adults aged 18 years and older, 38% of men and 40% of women worldwide, were overweight or obese (Our World in Data 2022). Indeed, this chronic disease is a significant risk factor for people with many other diseases. The obesity epidemic has increased significantly over the past three decades. In 2014, over 600 million adults, or 13% of the total adult population, were classified as obese worldwide. Of these 600 million obese adults, 11% are men, and 15% are women (Pineda et al. 2018). It is estimated that 25.6% of the total adult population (18 and over) can be classified as obese in the European region. This disease has almost doubled since the late 1980s. In 1985, the percentage of obese adults that are obese was 12.60%, and this value reached 23.30% in 2016 (Our World in Data 2022). Indeed, it has hit the world’s richest countries, regardless of individuals’ income levels. Indeed, the obesity epidemic is caused by several factors: genetic, social, economic, environmental, political, and physiological, which have interacted to varying degrees over time (Wright and Aronne 2012). Moreover, other factors, such as the globalisation process, urbanisation and technological progress, have caused an increase in the obesity epidemic (e.g., Fox et al. 2019;Toiba et al. 2015;Popkin 1998). The weight increase caused by the factors mentioned earlier contributes to making people physically less active (less physical activity also contributes to increasing obesity). Consequently, it leads to using more motorised vehicles and modern household appliances that reduce physical effort. In addition, it contributes to weight gain due to the lower caloric expenditure of individuals, as well as to increased consumption of processed foods, mainly produced by (i) multinational food companies, (ii) multinational supermarkets, and (iii) fast-food chains. All of these factors contribute to the increase in energy consumption from non-renewable energy sources and negatively impact the environment. In the literature, the impact of the obesity problem on environmental degradation using a macroeconomic approach is not advanced in the literature. For this reason, this investigation opted to use similar studies related to this topic (e.g., Koengkan and Alberto Fuinhas 2021b;Cuschieri and Agius 2020;Magkos et al. 2019;Swinburn 2019;Webb and Egger 2013;Viscecchia et al. 2012;Breda et al. 2011;Davis et al. 2007;Higgins 2005). These investigations pointed out that the obesity problem increases environmental degradation. However, none of these studies realised an analysis using a macroeconomic approach. They used the percentage of adults that are overweight or obese as a proxy for obesity and CO 2 emissions as a proxy for environmental degradation as well as the quantile via moments (QvM) method. Furthermore, these studies do not use the urban population and globalisation as independent variables. Moreover, none of these studies investigated the European countries. That is, there are gaps in the literature that need to be filled. In order to fill the gaps that were mentioned above, this investigation will identify the macroeconomic effect of the obesity epidemic on environmental degradation. Indeed, to identify this effect, this empirical investigation will study a group of thirty-one countries from the European region between 1991 to 2016 that have experienced a rapid increase in the obesity epidemic and social, economic, and environmental transformations. Certainly, to carry out this empirical investigation, the quantile via moments (QvM) approach, which Machado and Silva (2019) developed, will be used. This investigation will introduce a new analysis regarding the macroeconomic impact of the obesity problem on environmental degradation in European countries. This topic of research has never been approached before in the literature. Therefore, this study can open new opportunities for studying the correlation between obesity and environmental Economies 2022,10, 131 4 of 17 degradation through a macroeconomic aspect. Furthermore, this investigation is innovative in that it uses econometric and macroeconomic approaches to identify the possible effect of the obesity problem on ecological degradation. It is the first time this methodology approach has been employed in this kind of investigation. Moreover, this investigation will contribute to the literature for several reasons: (i) it introduces a new analysis regarding the effect of the obesity epidemic on environmental degradation in European countries. This topic of investigation is new and can open new issues of inquiry regarding the relationship between health and the environment using a macroeconomic approach; (ii) this investigation will contribute to introducing the QvM model; and (iii) the results of this study will help governments and policymakers develop more initiatives to reduce the obesity problem in the European countries, in addition to policies to reduce the consumption of non-renewable energy sources and environmental degradation. This study is ordered as follows. Section 2presents the literature review regarding the effect of the obesity epidemic on environmental degradation. Section 3provides the data and the methodology approach. Section 4presents the results and a brief discussion. Finally, Section 5presents the conclusions and limitations of the study. 2. Literature Review As mentioned before in the introduction, the literature has given little attention to a possible connection between the obesity epidemic problem and the increase in environmental degradation. Due to this, our investigation opted to use the few existing pieces of literature that approached this topic of investigation and which are similar (e.g., Koengkan and Alberto Fuinhas 2021b;Cuschieri and Agius 2020;Magkos et al. 2019;Swinburn 2019;Webb and Egger 2013;Viscecchia et al. 2012;Breda et al. 2011;Davis et al. 2007; Higgins 2005). Koengkan and Alberto Fuinhas (2021b) investigated the impact of the overweight epidemic on energy consumption in thirty-one countries in the European region from 1990 to 2016. The authors find that being overweight increases the consumption of energy from fossil fuels and consequently increases the emissions of CO 2 . Moreover, according to the authors, the increase in energy consumption and CO 2 emissions by the overweight epidemic is related to the increased consumption of processed foods from fast-food and multinational supermarket chains and multinational food corporations. Indeed, this process positively affects farm production, fast-food and multinational supermarket chains, and multinational food corporations to attend to the demand for processed foods. This increase affects the consumption of energy from non-renewable energy sources. Magkos et al. (2019) explored the effect of obesity on climate change. The authors point out that this health problem can aggravate climate change with increased CO 2 emissions in three ways: (i) oxidative metabolic demands; (ii) food production; and (iii) fossil fuels use. The increase of oxidative metabolic demands caused by the higher body mass associated with obesity is responsible for 7% of total GHGs. Indeed, the rise in production driven by the need to provide higher energy caloric intake is responsible for 52% of total GHGs. In contrast, the increase in fossil fuel consumption caused by transport and food production is responsible for 41% of these emissions. Thus, the authors estimated that the obesity epidemic adds the equivalent of 700 megatons of extra carbon dioxide to emissions per year or about 1.6% of the total global emissions. This idea is also shared by Swinburn (2019), who investigated the same topic. Other authors also share similar ideas, such as Breda et al. (2011), who studied the relationship between climate change and obesity in four regions of Karakalpakstan in Uzbekistan. According to the authors, there is strong evidence that being overweight contributes more to climate change, where overweight influences food consumption and production. Those categories contribute more to climate change by consuming processed foods from fast-food and multinational supermarket chains, multinational food corporations, and farms. The authors add that the food sector accounts for 7% of CO 2 emissions, Economies 2022,10, 131 5 of 17 43% of CH 4 emissions, and 50% of N 2 O emissions produced across the entire economy. Viscecchia et al. (2012) investigated the relationship between obesity and climate change in Italy. The authors opted to use the ordinary least squares method to undertake this investigation. The authors found that the increase in food consumption with low energy content has a twofold effect on reducing obesity and climate change mitigation. Moreover, the increase in food consumption with low energy caloric content reduces the obesity rate from 9.68 to 7.04% and avoids 5,406,000 tons of CO2emissions per year. Cuschieri and Agius (2020) investigated the link between diabetes caused by obesity and climate change. The increase in the demand for processed food caused by obesity also has an adverse effect on the climate. The authors highlight that this effect is caused by the increased transportation of goods, retail processing, and processed food storage. Webb and Egger (2013) also studied the link between obesity and climate change and point out that some behaviours connected with obesity also affect emissions of GHGs associated with climate change. The authors show that consuming processed food and non-renewable energy sources results from intensive motor vehicles and modern household appliances that reduce physical effort. Davis et al. (2007) investigated the interactions between cars, obesity, and climate change in the United Kingdom from 1974 to 2004. Their results precede the idea developed later by Webb and Egger (2013). According to the authors, the intensive use of motor vehicles in the United Kingdom has reduced physical activity and increased obesity and CO 2 emissions by increasing non-renewable energy sources. Higgins (2005), in an investigation that investigated whether “exercise-based transportation reduces oil dependence, carbon emissions and obesity”, points out that the use of the automobile as a means of transport also contributes to a sedentary lifestyle and the obesity epidemic and poor health. The author adds that these problems consume 27% of global oil production and produce 25% of global carbon emissions. The summary of the literature presented in this section has discussed some of the most consequential investigations that directly approached the impact of obesity on environmental degradation and similar investigations. However, none of these studies realised an analysis using a macroeconomic approach. Instead, they used the percentage of adults that are overweight or obese as a proxy for obesity and CO 2 emissions as a proxy for environmental degradation, as well as the quantile via moments (QvM) method. Furthermore, these studies do not use the urban population and globalisation as independent variables. Moreover, none of these studies investigated the European countries. Therefore, there are gaps in the literature that need to be filled. The following section will show the data and methods used in this investigation. 3. Data and Methodology This section is organised into two parts. The data, including the variables, is presented first, and the second part describes the methodology used in this study. 3.1. Data As mentioned before, this section will present the data used in this empirical investigation. Thirty-one countries from the European region were used, namely Austria (AT), Belgium (BE), Bulgaria (BG), Croatia (HR), Czech Republic (CZ), Denmark (DK), Estonia (EE), Finland (FI), France (FR), Germany (DE), Greece (GR), Hungary (HU), Iceland (IS), Ireland (IE), Italy (IT), Latvia (LV), Lithuania (LT), Luxembourg (LU), Malta (MT), the Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Republic of Cyprus (CY), Romania (RO), Slovakia (SK), Slovenia (SI), Spain (ES), Sweden (SE), Turkey (TR), and the United Kingdom (UK). Moreover, as mentioned earlier, this group of countries was selected because they have experienced a rapid increase in the obesity epidemic and social, economic, and environmental transformations that have facilitated this problem in the last three decades. Economies 2022,10, 131 6 of 17 Data for the period between 1991 and 2016 was utilised in this investigation. Please note that data for the variable Y only begins in 1991. Moreover, the time series of this investigation goes until 2016 due to data availability for the variable OBESE (see, Our World in Data 2022). The variables used in this empirical investigation are shown in Table 1 below. The variables EC, Y, and CO 2 , were first transformed into per capita values. Per capita values allow disparities to be controlled for population growth over time and within countries (e.g., Fuinhas et al. 2022;Koengkan et al. 2020). After this, all variables were transformed into natural logarithms (“Log”). Table 1. Description of variables and summary statistics. Description of Variables Summary Statistics Variable Definition Source Obs Mean Std Dev Min Max Dependent variable LogCO2 CO2emissions (kg per capita 2011 PPP $ of GDP). World Bank Open Data (2022) 806 −1.3226 0.4362 −2.6267 0.2197 Independent variables LogOBESE Percentage of adults that are obese. Obese is defined as having a body-mass index (BMI) equal to or greater than 30. BMI is a person’s weight in kilograms divided by their height in metres squared. Our World in Data (2022)806 4.027 0.0902 3.7612 4.2535 LogEC Electric power consumption (kWh per capita) from fossil fuels. World Bank Open Data (2022) 806 8.6668 0.6540 6.8724 10.9433 LogY_PC GDP per capita, PPP (constant 2011 international $). World Bank Open Data (2022) 806 10.2095 0.5013 8.5276 11.421 LogUP Urban population (% of the total population). This variable is a proxy for urbanisation. World Bank Open Data (2022) 806 4.2624 0.1727 3.8809 4.5841 LogGLOBA Globalisation index. This index is compounded by de facto economic, social and political components of globalisation and is scaled from 1 to 100. Thus, this variable encompasses three main factors of globalisation. KOF Globalization Index (2022) 802 4.9901 0.1616 4.2648 5.2038 Notes: Obs., Std.-Dev., Min. and Max denote the number of observations, the standard deviation, the minimum and the maximum, respectively. To capture the effect of obesity on environmental degradation, the econometric model has to include other variables that also explain pieces of the explained variable, the socalled control variables. Thus, the model uses variables that are in line with economic theory. Furthermore, the variables have support from the literature. For instance, variables Y_PC, EC, and UP have been used to justify the increase in CO 2 emissions, and GLOBA has been used as a proxy for environmental degradation (e.g., Koengkan and Fuinhas 2021a;Hdom and Fuinhas 2020;Wang et al. 2018). On the other hand, the variable OBESE, taken from the Our World in Data (2022), has not been used in literature to capture the rise in environmental degradation. Therefore, our study, by including this variable, is pioneering. The panel of countries and the variables used in this investigation are presented in this subsection. The methodology pursued in this investigation will follow the strategy presented in Figure 1below. Economies 2022,10, 131 7 of 17 Economies 2022, 10, x FOR PEER REVIEW 7 of 18 Figure 1. Methodology strategy. The authors created this figure. Therefore, after presenting the variables and the methodology strategy that this investigation will follow, it is also necessary to present the methodological approach for our empirical analysis. 3.2. Methodology As mentioned before, our empirical analysis will use the QvM model approach. This method was developed by Machado and Silva (2019) as an alternative for quantile regression. Consistent with Kazemzadeh et al. (2022) and Koengkan et al. (2020), this method can differentiate individual effects in panel data models. Moreover, Machado and Silva (2019) also add that this method can explain how the regressor affects the entire conditional distribution. Therefore, this method can be adapted to provide estimates in crosssectional models with endogenous variables. Koengkan et al. (2020) reveal that his method is based on moment conditions, not on conditional means, to identify the conditional means under exogeneity and that it allows the identification of the exact structural quantile function. Thus, given these advantages indicated by Koengkan et al. (2020) and Machado and Silva (2019), this empirical investigation opted to use this methodological approach. After briefly presenting the methodological approach and its advantages, it is time to show the equation where the QvM is constructed. For this, Equation (1) is presented: 𝑌  𝑎 𝑋  󰆒𝛽󰇛𝛿𝑍  󰆒𝛾󰇜𝑈 , (1) where 󰇝 󰇛𝑌 , 𝑋  󰆒󰇜󰆒󰇞 comes from a panel of n individuals i = 1, …, n over T periods, with 𝑃󰇝𝛿𝑍  󰆒𝛾0 󰇞1. The parameters 󰇛𝛼,𝛿 󰇜,𝑖 1,…,𝑛, catch the individual i fixed-ef- fects, and Z is a k-vector of known differentiable (with probability 1) transformations of the components of X, with the element l given by 𝑍𝑍 󰇛𝑋󰇜, 𝑙 1,…,𝑘. The sequence 󰇝 𝑋󰇞 is i.i.d., for any fixed I, and independent across t. 𝑈 are i.i.d., across i and t, statistically independent of 𝑋, and normalised to satisfy the moment condition 𝐸󰇛𝑈󰇜0 ∧ 𝐸󰇛|𝑈|󰇜 1 (e.g., Koengkan et al. 2020). Figure 1. Methodology strategy. The authors created this figure. Therefore, after presenting the variables and the methodology strategy that this investigation will follow, it is also necessary to present the methodological approach for our empirical analysis. 3.2. Methodology As mentioned before, our empirical analysis will use the QvM model approach. This method was developed by Machado and Silva (2019) as an alternative for quantile regression. Consistent with Kazemzadeh et al. (2022) and Koengkan et al. (2020), this method can differentiate individual effects in panel data models. Moreover, Machado and Silva (2019) also add that this method can explain how the regressor affects the entire conditional distribution. Therefore, this method can be adapted to provide estimates in cross-sectional models with endogenous variables. Koengkan et al. (2020) reveal that his method is based on moment conditions, not on conditional means, to identify the conditional means under exogeneity and that it allows the identification of the exact structural quantile function. Thus, given these advantages indicated by Koengkan et al. (2020) and Machado and Silva (2019), this empirical investigation opted to use this methodological approach. After briefly presenting the methodological approach and its advantages, it is time to show the equation where the QvM is constructed. For this, Equation (1) is presented: Yit =ai+X0 itβ+δi+Z0 itγUit , (1) where nYit,X0 it0o comes from a panel of n individuals i= 1, . . . ,nover Tperiods, with Pδi+Z0 itγ>0= 1. The parameters (α1,δi) , i= 1, . . . , n , catch the individual ifixedeffects, and Zis a k-vector of known differentiable (with probability 1) transformations of the components of X, with the element lgiven by Zl=Zl(X) , l= 1, . . . , k . The sequence {Xit} is i.i.d., for any fixed I, and independent across t. Uit are i.i.d., across i and t, statistically independent of Xit , and normalised to satisfy the moment condition E(U) = 0∧E(|U|)=1 (e.g., Koengkan et al. 2020). Economies 2022,10, 131 8 of 17 Before estimating the model regression, it is advised to assess the statistical proprieties of variables. Therefore, a battery of preliminary tests is applied (see Table 2below). Table 2. Preliminary tests. Test Objective Variance inflation factor (VIF) (Belsley et al. 1980)This test verifies the presence of multicollinearity between the variables of the model. Cross-section dependence (CSD) (Pesaran 2004)This test verifies the presence of cross-sectional dependence (CSD) in model variables. Panel unit root test (CIPS) (Pesaran 2007)This test verifies the presence of unit roots in the model’s variables. Hausman test This test verifies the presence of heterogeneity, i.e., whether the panel has random effects (RE) or fixed-effects (FE) in the model regression. Indeed, after the regression of the QvM model, it is necessary to apply the postestimation tests to identify if the models are adequate. Table 3below shows the postestimation tests that will be used in this empirical investigation. Table 3. Post-estimation tests for the QvM model. Post-Estimation Tests for the QvM Model Test Objective Wald test (Agresti 1990) This test verifies the global significance of the estimated models. Notes: The authors created this table. Stata Commands After presenting the preliminary tests, the QvM model and the post-estimation test, we must show the Stata commands we used in this empirical investigation. Table 4below shows the Stata commands used. Table 4. Stata commands. Preliminary Tests Test Stata Command Descriptive statistics of variables sum Variance inflation factor (VIF) test vif Cross-sectional dependence (CSD) test xtcd Panel unit root test (CIPS) multipurt Hausman test hausman (with the option, sigmamore) QvM model QvM xtqreg, quantile (0.25 0.50 0.75) Post-estimation test Wald test testparm Indeed, the preliminary tests, model regression, and post-estimation tests will be accomplished using Stata 17.0. The following section will show the results and discussions. 4. Results and Discussions As previously explained, this section will present the results and the possible explanations for the macroeconomic impact of the obesity epidemic on environmental degradation. The preliminary tests indicated that the variables used have characteristics such as (i) lowmulticollinearity among independent variables (as shown in Table A1 in Appendix A) ; (ii) cross-sectional dependence in the logs of variables (as shown in Table A2 in Appendix A); (iii) variables with orders of integration borderline I(0) and I(1) (as shown Economies 2022,10, 131 15 of 17 Table A5. QvM estimation without dummy variable. Independent Variables Dependent Variable (LogCO2) Quantiles 25th 50th 75th LogOBESE 1.4425 *** 1.3335 *** 1.2524 *** LogEC 0.2597 *** 0.2929 *** 0.3176 *** LogY_PC −0.5563 *** −0.5899 *** −0.6150 *** LogUP 0.5592 * 0.5304 *** 0.5090 *** TREND −0.0371 *** −0.0354 *** −0.0341 *** Obs 806 806 806 F/Wald test chi2(3) = 91.30 *** chi2(3) = 249.03 *** chi2(3) = 206.52 *** Notes: *** and * denote statistically significant at the 1% and 10% levels, respectively. Table A6. QvM estimations (complementary analysis). Independent Variables Dependent Variable (LogOBESE) Quantiles 25th 50th 75th LogGLOBA 0.2397 *** 0.2439 *** 0.2494 *** LogY_PC 0.0905 *** 0.0855 *** 0.0792 *** LogUP 0.9900 *** 0.9978 *** 1.0076 *** LogEC 0.0641 *** 0.0637 *** 0.0631 *** Obs 829 829 802 F/Wald test chi2(3) = 1182.55 *** chi2(4) = 7155.18 *** chi2(4) = 3313.36 *** Dependent variable (LogEC) LogOBESE 0.4711 ** 0.8690 *** 1.2596 *** LogGLOBA 0.3514 *** 0.0742 −0.1979 LogY_PC 0.1997 *** 0.1481 *** 0.0975 * LogUP 0.8091 ** 0.6018 ** 0.3983 * Obs 829 829 F/Wald test chi2(4) = 361.24 *** chi2(4) = 204.63 *** chi2(4) = 61.24 *** Dependent variable (LogY_PC) LogOBESE 0.9484 ** 1.0261 *** 1.0847 *** LogGLOBA 1.3978 *** 1.1282 *** 0.9248 *** LogEC 0.0590 0.1387 0.1988 *** LogUP −1.3186 *** −1.3223 *** −1.3251 *** Obs 829 829 829 F/Wald test chi2(4) = 156.71 *** chi2(4) = 524.13 *** chi2(4) = 762.55 *** Dependent variable (LogUP) LogOBESE 0.4266 *** 0.4318 *** 0.4365 *** LogGLOBA −0.0500 *** −0.0663 *** −0.0809 *** LogY_PC −0.0495 *** −0.0475 *** −0.0456 *** LogEC 0.0185 ** 0.0197 *** 0.0208 *** Obs 829 829 829 F/Wald test chi2(4) = 339.41 *** chi2(4) = 606.57 *** chi2(4) = 318.66 *** Dependent variable (LogGLOBA) LogOBESE 0.7550 *** 0.6667 *** 0.5971 *** LogY_PC 0.2737 *** 0.2609 *** 0.2507 *** LogEC 0.0047 0.0188 0.0298 LogUP −0.4515 ** −0.4161 *** −0.3882 *** Obs 829 829 829 F/Wald test chi2(4) = 559.54 *** chi2(4) = 1257.30 *** chi2(4) = 827.28 *** Notes: ***, ** and * denote statistically significant at the 1%, 5%, and 10% levels, respectively. 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