Quality of Institutions, Technological Progress, and Pollution Havens in Latin America. An Analysis of the Environmental Kuznets Curve Hypothesis

sustainability Article Quality of Institutions, Technological Progress, and Pollution Havens in Latin America. An Analysis of the Environmental Kuznets Curve Hypothesis JoséM. Cansino 1,2,* , Rocio Román-Collado 1,2 and Juan C. Molina 1 1Departamento de Análisis Económico y Economía Política, Universidad de Sevilla, Sevilla 410818, Spain 2Universidad Autónoma de Chile, Santiago 7500912, Chile *Correspondence: [email protected]; Tel.: +34 954557528 Received: 16 May 2019; Accepted: 2 July 2019; Published: 6 July 2019   Abstract: A set of 17-year panel data (1996–2013) across a representative sample from eighteen Latin American countries is used to respond four research questions: Did Latin American Greenhouse Gas (GHG) emissions prove the Environmental Kuznets Curve (EKC) hypothesis? Did the quality of institutions play a compensating role for income on environmental stress? Did technological progress help decouple income from environmental stress? Has the Pollution Haven Hypothesis (PHH) been proven? In order to answer the research questions, the paper expands the traditional EKC approach by including an exclusive quality analysis of institutions, technological progress, and PHH as part of the model. This innovation is developed considering the most recent literature about EKC as a starting point. Major findings show that the relationship between income and GHG emissions is adjusted to the traditional EKC hypothesis for the analyzed period. They also show that the quality of institutions and technological progress improve environmental sustainability. However, the variables, Foreign Direct Investment and International Trade, provide a negative answer to the fourth question. The main methodological contribution of this paper is to use a threefold extended classic EKC model to conduct the feasible generalized least squares method. The paper also contributes to the growing body of PHH literature. Keywords: environmental Kuznets curves; quality of institutions; CO 2 emissions; technological progress; pollution haven hypothesis 1. Introduction Since the release of seminal papers by [ 1 ] and [ 2 ], there has been prolific development of the so-called Environmental Kuznets Curve (EKC) by analogy with the income inequality postulated by Kuznets [ 3 ]. In December 1954, at the sixty-seventh annual meeting of the American Economic Association, Simon Kuznets suggested that as per capita income increases, income inequality increases at first, but then after reaching a particular turning point, it begins to decrease [4]. The EKC hypothesis assumes an inverted U shape curve showing a non-linear relationship between incomes or economic growth and environmental stress, which is usually measured as pollutant gas emissions into the atmosphere (i.e., Greenhouse gases). The basic idea upon which this assumption is built states that a transition from an industrialized to a tertiary economy decreases environmental stress. This would allow meeting the needs of the present without compromising the ability of future generations to meet their needs as the sustainability establishes. Together with this, the traditional EKC hypothesis also highlights the role of improving technological progress by replacing high energy intensive technologies with lower ones. An EKC reveals how a specified measurement of environmental quality changes as the welfare of a country changes. The common point of research focused on the EKC hypothesis is that the Sustainability 2019,11, 3708; doi:10.3390/su11133708 www.mdpi.com/journal/sustainability Sustainability 2019,11, 3708 2 of 20 environmental quality deteriorates at the early stages of economic growth but improves at the later ones with higher levels of income [ 5 ]. Since the early 1990s, the EKC concept has led many researchers to assume that each economy should focus on its own growth, and any environmental problems will be solved by the process of economic growth without any environmental protection-oriented measures [ 6 ]. However, some evidence shows that developing countries are addressing environmental issues; sometimes, they address them by adopting developed country standards with a short time lag and sometimes performing better than some high-income countries. Evidence also shows that the EKC results have a very flimsy statistical foundation [7]. From the perspective of developing countries, the EKC hypothesis offers advice on a necessary first income growth stage that damages environmental quality before decoupling them. Reaching the maximum EKC point sooner becomes a crucial point for developing economies. Including technological progress in EKC research has been less frequent than only exploring the relationship between income or economic growth and environmental stress. When technological progress is included in the model, research improves its interest, particularly if it is oriented towards meeting a sustainable economic growth pathway. Designing the right roadmap to reduce the EKC curve, [ 8 , 9 ] proved that a more sustainable economic growth (a softer slope of EKC) is possible, thanks to government measures that are oriented towards protecting the environment. The basic idea upon which this assumption is built states that a transition from an industrialized to a tertiary economy decreases environmental stress. This would allow meeting the needs of the present without compromising the ability of future generations to meet their needs as the sustainability establishes. Here, institutions are defined in the sense of [ 10 ] as the humanly devised constraints that structure political, economic, and social interaction. Institutions—in the sense of North—consist of both informal constraints and formal rules. The author conceived informal constraints as sanctions, taboos, customs, traditions, codes of conduct, while formal rules were constitutions, laws, and property rights. Together with this, the term decoupling is used in environmental studies to characterize the link between economic activity and environmental degradation, or using the terminology of the [ 11 ], the link between “economic goods” and “environmental bads”. Decoupling would be characterized as a disruption in the growth rate of environmental pressure (e.g., CO 2 emissions) and economic activity over a given period [ 12 ]. However, there is some controversy regarding the term. [ 13 ] prefers to move away from the concept of ‘decoupling’ mentioned above, which is often used to refer to the situation where resources impact on the decline relative to GDP growth. The author pointed out that the term decoupling has been used as a key political element to bridge the contentious debate on continuous economic growth and its negative impact on the environment. Finally, the author argued that the term ‘decoupling’ is misleading and that it should be understood as no coupling because it is impossible to have economic growth without affecting the environment. Based on the role played by institutions, recent literature has attempted to include institution quality as a novelty in the EKC approach [14–19]. Finally, in terms of a legal framework as a part of institutions, global governance in the battle against Climate Change has marked many countries (mainly, the so-called Annex I countries in Kyoto Protocol jargon) as commitments oriented towards mitigating their Greenhouse Gas emission levels. Advances in global Climate Change governance coincided in time with intensive offshoring and outsourcing processes. Many scholars explored whether part of the international trade, offshoring, and outsourcing processes was due to movements of high pollutant industries seeking refuge in countries with a low standard of legal environmental protection framework. This is known as the pollution haven hypothesis and needs to be considered as a part of new EKC research. The pollution haven hypothesis (also known as pollution haven effect) posits that jurisdictions with weak environmental regulations—named ‘pollution havens’—would attract polluting industries relocating from more stringent locales. The basic idea is that environmental regulations raise the cost of key inputs to goods with pollution-intensive production and that they reduce jurisdictions’ comparative advantage in manufacturing them. The Pollution Haven hypothesis claims that due to international trade and Sustainability 2019,11, 3708 3 of 20 foreign direct investment (FDI), developing countries appear to be the pollution haven for developed countries [ 20 ]. The Heckscher–Ohlin model [ 21 ] provides the theoretical foundations by showing that regions will export goods that use locally abundant factors as inputs. In the context of a more integrated world, falling trade barriers imply that the role environmental restrictions play in shaping a country’s comparative advantage is greater than before. This has led to fears that pollutant industries will relocate to developing regions where environmental barriers may be less stringent [22]. The pollution haven hypothesis—PHH—establishes that multinational corporations engaging in highly polluting activities move to the developing countries with weaker environmental standards where the cost of complying with environmental regulations is lower [ 23 ]. Such situations are extremely interesting for developing countries because they usually have weaker legal requirements when it comes to protecting the environment. However, the results regarding PHH remain inconclusive as [ 20 ] remarked. These authors wrote that, despite the fact that the PHH has been studied theoretically and empirically all over the world (being examined with different specifications, functional forms, variables, estimation methods, and datasets), the results are mixed at best. Thus, scientific contributions to this topic are welcome. The main methodological contribution of this paper is to expand the traditional EKC approach by including an exclusive quality analysis of institutions, technological progress, and PHH as part of the model. This innovation is developed by taking recent literature modeling EKC as a starting point [2,5,8,24–28]. This paper also contributes to the growing body of PHH literature. Literature shows that there is a knowledge gap to be filled regarding EKC’s evidence. Expanding the classical approach by including quality of institutions, technological progress, and PHH could help to reduce this gap. In this study, a set of 17-year panel data (1996–2013) across a broad sample of eighteen Latin American countries has been used. The end of this period was due to no longer availability of the quality institutions data. By focusing on this sample, the aim of this paper is to respond to the following research questions: •Did Latin American Greenhouse Gas emissions prove the EKC hypothesis? •Did the quality of institutions play a compensating role for income on environmental stress? • Did technological progress act in line with the quality of institutions to help decouple income from environmental stress? •Has the PHH been proven for the selected sample during the period under consideration? The main utility of the results is related to the fulfillment of mitigation commitments of Greenhouse Gas (GHG) emissions reached at the U.N. Framework Convention on Climate Change (UNFCCC) Conference of the Parties (COP21) in Paris in December 2015, also known as Paris Agreement. Because all the countries in the sample are signatories to this agreement—although they do not appear in Annex 1—they have assumed coded commitments to reduce their GHG emissions in the coming years. These commitments are detailed in the Intended Nationally Determined Contributions (INDCs) sent by each country before the signing of the Agreement and subsequently ratified. Given that the results shed light on the relationships between the analyzed variables and environmental stress, as measured by GHG emissions, it is possible to identify the efforts that must be made in order to improve the quality of institutions, the Research+Develepment policy, the consumption patterns of the population, energy consumption, the energy matrix, international trade, or possible controls on the orientation of the FDI. This paper is organized as follows: after the Introduction, Section 1details the methodology used by emphasizing the new aspects developed. Section 2describes the dataset used. Adjusted models and empirical results are shown in Section 3(empirical models in Section 3.1 and findings in Section 3.2). Finally, Section 4concludes and also offers a number of political recommendations. Sustainability 2019,11, 3708 4 of 20 2. Materials and Methods 2.1. Methods In response to the research questions posed, the EKC classic model has been used as the starting point to test the relationship between environmental degradation or environmental stress and income growth. The main contribution of this paper is the way of expanding the EKC classic model in a sequentially way summarized in a diagram at the end of this sub Section. This paper uses a panel data methodology. Cross-sectional and country-specific data for the period from 1996 to 2013 is pooled for a sample of eighteen Latin American countries. The specification model given by Equation (1) is based on [2,8,24–28]. Yit =β1Xit +β2Xit2+β3Xit3+β4Zit +µi+δt+εit (1) where it refers to country i(i =i, . . . , N) in time t(t =1, . . . , T), Y is a variable representing environmental stress or degradation measured with the pollutant gas emissions into the atmosphere. X measures the income level for each country. Z is a vector that includes control variables usually considered in the literature, while µi measures the unobserved fixed effect for each country. δt captures the specific time effect, while ε captures the stochastic error. X coefficients allow the form of the curve and to test the EKC hypothesis to be known. The cubic term in Equation (1) allows proving whether the trend reverses after a decrease in environmental stress. Various forms for EKC are summarized in Table 1[ 27 ]. The EKC hypothesis is proven when represented as an inverted U-shaped curve. The cubic term in Equation (1) tests whether EKC takes an N form when income levels increase. Table 1. Different types of Environmental Kuznets Curve (EKC). β1>0β2=0β3=0 Monotonically increasing β1<0β2=0β3=0 Monotonically decreasing β1>0β2<0β3=0 Inverted U shape. The EKC is valid. β1<0β2>0β3=0 U Shape β1>0β2<0β3>0 N form β1<0β2>0β3<0 Inverted N shape Source: [27]. Environmental degradation or stress for total per capita Greenhouse Gas emissions is measured in kt of CO 2 equivalent (Greenhouse Gases pc). This variable is commonly used in specialized literature, although energy consumption is also used to proxy environmental stress [ 28 – 30 ]. Carbon dioxide emissions from fossil fuel combustion are an important driver of Global Climate Change. Increased energy demand stimulates economic growth, but energy use also drives Greenhouse Gas emissions [ 31 ]. Deforestation is also used to proxy environmental stress but less than Greenhouse Gases and energy consumption [ 15 ]. Income level is measured in per capita GDP 2011 US $ at Purchase Price Parity. To soften outliers and the direct elasticities values obtained, natural logarithms were taken. Equation (1) is re-written as, lnGHGpcit =β1lnGDPpcit +β2lnGDPpcit2+β3lnGDPpcit3+β4Zit +µi+δt+εit (2) Considering that this study is not limited to only proving the EKC hypothesis but also tests the role played by the quality of institutions and technological progress on environmental stress, two variables measuring them (Institutions, INST and technological progress, TECH) were included in the classic EKC model. It is expected that both the quality of institutions and technological progress act as inhibitors of Greenhouse Gas emissions. Additionally, the expanded model includes the crossed product of (GDP per capita × INST) and (GDP per capita × TECH) to capture the interaction effect Sustainability 2019,11, 3708 5 of 20 of these new variables on Greenhouse Gas emissions. This methodological approach goes in line with [15,18,19,32–36]. Equation (3) shows the extended EKC model: lnGHGpcit =β1lnGDPpcit +β2lnGDPpcit2+β3lnGDPpcit3+β4INSTit +β5TECHit +β6(lnGDPpcit ×INSTit) +β7(lnGDPpcit ×INSTit)2+β8(lnGDPpcit ×INSTit)3 +β9(lnGDPpcit ×TECHit)+β10(lnGDPpcit ×TECHit)2 +β11(lnGDPpcit ×TECHit)3+β12Zit +µi+δt+εit (3) To measure the quality of institutions, the World Bank offers useful statistical information for a broad list of countries referenced for almost the past two decades [ 37 ]. This database uses six different indicators to measure the quality of institutions for the countries considered. These indicators are Political Stability and Absence of Violence/Terrorism (PSAVT), Control of Corruption (CC), Government Effectiveness (GE), Regulatory Quality (RQ), Rule of Law (RL), and Voice and Accountability (VA). Recent studies using this database are [ 33 ] and [ 35 ]. An in-depth methodological revision of these indicators may be found at [ 38 ]. This study uses all six indicators in search of better robustness of results. Following definitions given by the World Bank, Political Stability and Absence of Violence/Terrorism (PSAVT) captures perceptions for the likelihood of political instability and/or politically motivated violence. Control of Corruption (CC) indicates the perceptions regarding forms of corruption, as well as the "capture" of the state by elites and private interests. Government Effectiveness (GE) measures perceptions from users of the quality of public goods and services and if civil servants are independent of political pressures or not. GE also captures the quality of policy formulation and implementation and the credibility of government commitment to such policies. Regulatory Quality (RQ) shows the perceptions of government ability to formulate and implement sound policies and regulations that permit and promote private sector development. Rule of Law (RL) puts the focus on the perceptions that agents have about abiding by the rules of society. Special interest is paid in the quality of contracts enforcement, property rights, the police, and the courts’ decisions. RL also provides information on the likelihood of crime and violence. Finally, Voice and Accountability (VA) covers the perceptions of the extent to which a country’s citizens are able to participate in selecting their government, including freedom of expression, association, and free media. An estimate gives the country’s score on the aggregate indicator, in units of a standard normal distribution. Values range from approximately − 2.5 to 2.5. Higher values in indicators mean a higher quality of institutions. It is expected that an increase in the value of any one of the indicators provokes a reduction in Greenhouse Gas emissions. This goes in line with [ 15 ] in the sense that institutions oriented to secure property rights and better environmental policies for moving the economic system towards a right growth path could reduce the maximum point of an environmental Kuznets curve (EKC) relationship between income and environmental stress. Technological progress (TECH) in Equation (3) is measured through high-technology product exports as a percentage of total manufactured product exports. This allows examining the extent to which the EKC inverted U relationship can be explained by trade and the displacement of pollutant industries between the samples of considered countries [ 39 ]. It is also expected that technological progress acts as an inhibitor or a compensating factor of Greenhouse Gas emissions into the atmosphere. Sustainability 2019,11, 3708 6 of 20 Following [ 32 ], the extended EKC model used in this study also includes control variables. The literature shows that these variables have been largely tested. They are based on the IPAT equation and the Kaya identity [ 40 – 44 ]. The first control variable used was the natural logarithm of the total population (lnP). Available literature shows that a population increase acts as a driver of Greenhouse Gas emissions. A second control variable is total energy consumption (EC), measured, in turn, as miles of barrel-oil-equivalent per million of GDP (2010 US $). It is expected that an increase in EC will drive Greenhouse Gas emissions. The third control variable advises the matrix of energy resources (ERM), defined as the ratio between renewable energy sources on the total energy resources used. Increased use of renewable energy sources (which means a higher ERM value) might act as an inhibitor of Greenhouse Gas emissions. A last extension of the classic EKC model allows the PHH to be tested. This extension is deployed by including international trade and direct foreign investment in the classic model. Part of the literature focuses on the impact of international trade on pollutant emissions, mainly in emerging economies [ 45 – 48 ]. To assess the impact of international trade (named as TRADE in the model), the authors of this paper have used commodities trade as a percentage of GDP. Together with the role played by international trade, recent outsourcing and offshoring processes would stress the negative impact of international trade on Greenhouse Gas emissions. The basic idea of the PHH implies that Foreign Direct Investment (FDI) will flow to countries with less stringent environmental protection standards and hence, successfully result in depleting the environment. Following the definition of the OEDC for FDI, this is conceived as a category of cross-border investment made by a resident in one country (the direct investor) to establish a lasting interest in an enterprise (the affiliate enterprise) that is resident in a country other than that of the direct investor. It is expected that FDI flows to countries with low quality of environmental protection for institutions [ 19 , 49 – 55 ]. There have been several debates. Recent literature on the consequences of FDI for less-developed countries states that it may contribute to different forms of environmental degradation in these countries (e.g., [ 56 – 60 ]). From a panel dataset of 187 countries, [ 61 ] found that embodied CO 2 emissions in the international trade had been on the rise in developed and developing countries during the period 1970 to 2011. Using a Multiregional Input-Output Model (MRIO), they claimed that 72% of embodied flows of CO 2 were being generated from the so-called non-Annex I countries signatory of Kyoto’s protocol. This indicated the existence of PHH type trade flows. To test PHH, FDI is measured as direct foreign investment and net inflows (% of GDP). Based on the previously stated arguments, it is possible to rewrite Equation (3) to formulate a more extended version of the classic EKC model as Equation (4) does: lnGHGpcit =β1lnGDPpcit +β2lnGDPpcit2+β3lnGDPpcit3+β4INSTit +β5TECHit +β6(lnGDPpcit ×INSTit) +β7(lnGDPpcit ×INSTit)2+β8(lnGDPpcit ×INSTit)3 +β9(lnGDPpcit ×TECHit)+β10(lnGDPpcit ×TECHit)2 +β11(lnGDPpcit ×TECHit)3+β12lnPit +β13ECit +β14ERMit +β15TRADEit +β16FDI +µi+δt+εit (4) Thefollowingdiagram(Figure 1) summarizes the main sequence of methodological steps described in Equations (1)–(4). Sustainability 2019,11, 3708 7 of 20 Sustainability 2019, 11, x FOR PEER REVIEW 8 of 21 Figure 1. The Main sequence of methodological steps. Starting point: EKC classic model Including quality of institutions and technological progress Starting point: EKC classic model Including control variables Including international trade and direct foreign investment Indicators to capture quality of institutions: i.- Political Stability and Absence of Violence/Terrorism ii.- Control of Corruption iii.- Government Effectiveness iv.- Regulatory Quality v.- Rule of Law vi.- Voice and Accountability Control variables: i.- Population ii.- Energy consumption iii.- Matrix of energy resources Choosing from among the pooled regression, the fixed-effects, and the random-effects Results Figure 1. The Main sequence of methodological steps. Sustainability 2019,11, 3708 8 of 20 2.2. Data Paneldatacontainingcross-sectionalcountry-specificannual dataovera 17-yearperiod(1996–2013) for eighteen Latin American countries has been used. Argentina, Bolivia, Brazil, Chile, Colombia, Dominican Republic, Ecuador, Guatemala, Honduras, Haiti, Mexico, Nicaragua, Panama, Peru, Paraguay, El Salvador, Uruguay, and Venezuela made up the sample group. The sample of countries was based on the availability of data, as was the period to be considered. Data for Greenhouse Gas emissions, GDPpc, TECH, P, TRADE, and FDI variables were taken from the World Development Indicator database (WDI). Data for quality of institution indicators were taken from The Worldwide Governance Indicators (WGI) project. Data for EC and energy sources were taken from the database named CEPALSTATS. Indicators measuring the quality of institutions were built by WGI upon the specific local/national characteristics and regulations. Besides, the control variables (mainly population and energy resource mix) also take into account specific local/national characteristics of countries included in the sample. The supplementary materials include an excel file with full data used in this research. Table 2offers a detailed description of the variables included in the extended EKC model with this same Table showing the expected sign of coefficients. It is expected that the sign of the variables ln (GDPpc), ln( GDPpc2) , and ln ( GDPpc3) fulfill some of the standard models of EKC shown in Table 1. In correspondence to what has been explained in Section 2, it is expected that an increase in the variables ln ( GDPpc2) , PSAVT, CC, GE, RQ, RL, VA, TECH, ln( GDPpc2) xPSAVT/CC/GE/RQ/RL/VA, ln( GDPpc2)x TECH, EE (Renewable energy sources on total energy use) reduce deeply the level of emissions, while an increase in the variables ln (GDPpc), ln(GDPpc)xPSAVT/CC/GE/RQ/RL/VA, ln(GDPpc)xTECH, Ln (P), EC, TRADE, FDI increase considerably the emissions level. Table 2. Details of variables. Variable Explanation Unit Source Expected Sign ln (GHGpc) Total greenhouse gas emission ln (kt of CO2 equivalent) WDI ln (GDPpc) GDP per capita, PPP (current international $) ln (1000 US$ 2011) WDI Positive ln GDPpc2)GDP per capita square WDI Negative ln GDPpc3)GDP per capita cube WDI ± PSAVT Political Stability and Absence of Violence/Terrorism (PSAVT) Values between −2.5 and 2.5 WGI Negative CC Control of Corruption (CC) Values between −2.5 and 2.5 WGI Negative GE Government Effectiveness (GE) Values between −2.5 and 2.5 WGI Negative RQ Regulatory Quality (RQ) Values between −2.5 and 2.5 WGI Negative RL Rule of Law (RL) Values between −2.5 and 2.5 WGI Negative VA Voice and Accountability (VA) Values between −2.5 and 2.5 WGI Negative TECH High-technology exports Percentage of manufactured exports WGI Negative ln(GDPpc) × PSAVT/CC/GE/RQ/RL/VA Terms of interaction Positive Sustainability 2019,11, 3708 9 of 20 Table 2. Cont. Variable Explanation Unit Source Expected Sign lnGDPpc2)× PSAVT/CC/GE/RQ/RL/VA Terms of interaction Negative lnGDPpc3)× PSAVT/CC/GE/RQ/RL/VA Terms of interaction ± ln(GDPpc) ×TECH Terms of interaction Positive lnGDPpc2)×ECH Terms of interaction Negative lnGDPpc3)×TECH Terms of interaction ± Ln (P) Total population ln (units) WDI Positive EC Energy consumption (EC) per million dollars of GDP at constant 2010 prices Thousands of barrels of oil equivalent CEPAL Positive. EE Renewable energy sources on total energy use Proportion CEPAL Negative TRADE Merchandise trade (% of GDP) Percentage WDI Positive FDI Foreign direct investment, net inflows Percentage WDI Positive The dataset is complete for almost all variables considered in the analysis until 2013 with the exception of The Worldwide Governance Indicators and the dependent variable GHGpc. However, the missing values of these variables are limited. The WGI database, consisting of the variables PSAVT, CC, GE, RQ, RL, and VA, is 82% complete. Missing values appear for some countries in 1997, 1999, and 2001. The dataset for the dependent variable GHGpc is 94% complete with only missing data for some countries in 2013. The linear interpolation method was used to estimate the missing values. Table 3 provides descriptive statistics, providing information about the nature and distribution of the data. Descriptive statistics help to avoid bias in the econometric estimation by identifying potential outliers through the indicators mean, standard deviance (Std. Dev.), minimum (min), and maximum (max). The overall values refer to the whole sample. The between values refer to the standard deviation of the averages of each individual country as well as to the minimum and maximum of the averages of each individual country. The within values are referred to the standard deviation, minimum, and maximum of the variation of a country’s values from its average over time. It can be observed that there is a greater variety among the ‘between’ values than the ‘within’ values. Table 3. Descriptive Statistics. Variable Mean Std. Dev. Min Max Obs ln (GHGpc) Overall −5.330286 0.7015173 −6.432259 −2.786832 N =324 Between 0.6852661 −6.265535 −3.959671 n =18 Within 0.2173756 −6.287268 −4.157447 T =18 ln (GDPpc) Overall 8.972472 0.5152072 7.738997 10.02322 N =324 Between 0.4667575 8.093602 9.559566 n =18 Within 0.2429863 8.480332 9.562592 T =18 PSAVT Overall − 0.3426055 0.6530893 −2.3857 0.9973063 N =324 Between 0.6386092 −1.749541 0.7633462 n =18 Within 0.2004201 −1.062043 0.1544409 T =18 CC Overall −0.303594 0.6779242 −1.444359 1.572951 N =324 Between 0.6782205 −1.124076 1.444894 n =18 Within 0.1542978 − 0.7346453 0.2069996 T =18 Sustainability 2019,11, 3708 16 of 20 Table 6. Cont. (1) PSAVT (2) CC (3) GE (4) RQ (5) RL (6) VA ∆LN (GDP) *TECH 0.010 *** −0.002 *** 0.005 *** −0.001 0.002 −0.002 (0.001) (0.003) (0.002) (0.002) (0.002) (0.003) ∆LN (GDP2) *TECH 0.007 *** 0.007 *** 0.008 *** 0.008 *** 0.009 *** 0.009 *** (0.001) (0.002) (0.001) (0.001) (0.001) (0.002) ∆LN (GDP3) *TECH −0.019 *** −0.002 *** −0.013 *** −0.004 −0.010 *** −0.006 (0.002) (0.004) (0.003) (0.003) (0.003) (0.004) ∆EC 0.037** 0.060 *** 0.092 *** 0.055 *** 0.072 *** 0.056 *** (0.018) (0.026) (0.013) (0.014) (0.014) (0.020) ∆EE −0.005 *** −0.005 *** −0.005 *** −0.004 *** −0.004 *** −0.004 *** (0.000) (0.001) (0.000) (0.000) (0.000) (0.001) ∆LN (P) −0.449 *** −0.404 *** −0.668 *** −1.088 *** −0.725 * −0.658 (0.137) (0.223) (0.149) (0.320) (0.391) (0.558) ∆FDI −0.009 *** −0.009 *** −0.010 *** −0.010 *** −0.010 *** −0.007 *** (0.000) (0.001) (0.000) (0.001) (0.001) (0.001) ∆TRADE −0.002 *** −0.001 *** −0.001 *** −0.001 *** −0.001 *** −0.001 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) WALD CHI2(33) 5,526,160 169,000,000 1,210,087 6,954,258 4,969,125 9,365,484 PROB >CHI2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 OBS 306 306 306 306 306 306 NOTE: STANDARD ERRORS ARE SHOWN IN PARENTHESIS. THE ESTIMATES CONTAIN DUMMIES FOR EACH YEAR. * INDICATES SIGNIFICANCE AT 10%. ** INDICATES SIGNIFICANCE AT 5%. *** INDICATES SIGNIFICANCE AT 1%. 4. Conclusions By using a panel of eighteen countries from Latin America for the period 1996–2013, based on the Environmental Kuznets Curve hypothesis, this article mainly re-examines not only the Pollution Haven Hypothesis and the relationship between environmental degradation and economic growth but also the role played by the quality of institutions and technological progress on environmental stress. The results show that the relationship between income and Greenhouse Gas emissions adjusts to the traditional Environmental Kuznets Curve EKC hypothesis, and the quality of institutions and technological progress has a significant negative effect on the relationship between quality of institutions and environmental stress. For the eighteen Latin American countries selected, the results obtained allow us to conclude that the relationship between income and Greenhouse Gas emissions adjusts to the traditional EKC hypothesis for the period 1996–2013. This is an affirmative answer to the first research question: Did Latin American Greenhouse Gas emissions prove the EKC hypothesis? This tells us that from the sustainability perspective, reaching the maximum EKC point sooner becomes a crucial point. The results also show negative and significant signs for cubic terms in all of the conducted estimations. Consequently, it does not prove that the trend reverses after a decrease in environmental stress. These results support the theory that any increase in Political Stability and Absence of Violence/Terrorism (PSAVT), Control of Corruption (CC), Government Effectiveness (GE), Regulatory Sustainability 2019,11, 3708 17 of 20 Quality (RQ), Rule of Law (RL), and Voice and Accountability (VA) causes a decrease in Greenhouse Gas emissions. Such results give a positive answer to the research question: Did the quality of institutions play a compensating role for income on environmental stress? This study concludes that the quality of institutions helps to move towards sustainable production models. Political measures oriented towards enhancing the quality of institutions would soften EKC, thus making it easier to reach the maximum. The quality of institutions’ improvement, mainly those of a legal nature, should preferably be based on an international perspective since environmental degradation is a clear example of a negative externality. The results for technological progress also satisfy theoretical expectations, so a positive answer is given to the third research question: Did technological progress act in line with the quality of institutions to help decouple income from environmental stress? As a conclusion and in line with the arguments given above, technological progress facilitates reaching EKC’s maximum faster and, therefore, contributes to sustainability. Political measures oriented towards increasing Research and Development must be promoted. Regarding the control variables for energy consumption (EC) and energy matrix (ERM), their behavior was as expected. The energy consumption variable coefficients were positive and significant. This result shows that the energy consumption acted as an important driver of Greenhouse Gas emissions opposite to a transition to better levels of sustainability. Signs of variable ERM coefficients were negative and statistically significant, thus verifying that an increase in the share of renewable energy sources used reduces Greenhouse Gas emissions. In terms of the results for the variables, Foreign Direct Investment (FDI) and International Trade (TRADE), these provided a negative answer to our last question: Has the Pollution Haven Hypothesis been proven for the selected sample during the period under consideration? During the period under analysis, important international agreements were put in place to fight against Climate Change. The most important agreements were the Montreal Protocol (1989), the Kyoto Protocol (1998), and the Copenhagen Accord (2009) on global warming. Although the countries in the sample have the status of non-Annex I countries, that is, they did not assume mandatory commitments to mitigate Greenhouse Gas emissions, this did not prevent them from strengthening their legal framework to protect the environment by diverting the most polluting investments to other countries with less demanding legislation. In this sense, the legislative changes moved in the right direction. Author Contributions: J.M.C. conceivedtheidea, discussedtheobtainedresults, and co-authorizedthemanuscript. R.R.-C. co-authorized the manuscript and wrote conclusions. J.C.M. expanded the classic EKC model, ran models, obtained results, and co-authorized the manuscript. Funding: This research received no external funding. Acknowledgments: The authors wish to acknowledge the funding provided by the following institutions: the Andalusian Regional Government (project SEJ-132), the “C á tedra de Econom í a de la Energ í a y del Medio Ambiente” sponsored by Red El é ctrica de España at the University of Seville,” and from Departamento de An á lisis Econ ó mico y Econom í a Pol í tica (Department of Economic Analysis and Political Economy (Universidad de Sevilla) at the University of Seville. The first and second authors also acknowledge the funding provided by the Universidad Autónoma de Chile (Chile). Conflicts of Interest: The authors declare no conflict of interest. References 1. Kuznets, S. Economic Growth and Income Inequality. Am. Econ. Assoc. 1955,45, 1–28. 2. Grossman, G.M.; Krueger, A.B. Economic Growth and the Environment. Q. J. Econ. 1995 ,110, 353–377. [CrossRef] 3. Panayotou, T. Economic growth and the environment. 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