sustainability Article Toxic Income as a Trigger of Climate Change Fander Falconí 1,2, *, Rafael Burbano 3 , Jesus Ramos-Martin 4 and Pedro Cango 1 1 Departamento de Desarrollo, Ambiente y Territorio, Facultad Latinoamericana de Ciencias Sociales (FLACSO), La Pradera E7-174 y Diego de Almagro, Quito 170518, Ecuador; pedro.cango@gmail.com 2 Universidad Nacional de Educación (UNAE), Parroquia Javier Loyola Azogues, Cañar 030154, Ecuador 3 Departamento de Economía Cuantitativa, Escuela Politécnica Nacional, Ladrón de Guevara E11-253, Quito 170517, Ecuador; rafael.burbano@epn.edu.ec 4 Grupo de Población y Ambiente, Universidad Regional Amazónica Ikiam, Vía Muyuna-Atacapi, Km. 7, Tena 150102, Ecuador; jesus.ramos@ikiam.edu.ec * Correspondence: ffalconi@flacso.edu.ec  Received: 1 March 2019; Accepted: 14 April 2019; Published: 25 April 2019  Abstract: The rate of CO2 emissions concentration in the atmosphere increases the likelihood of significant impacts on humankind and ecosystems. The assumption that permissible levels of greenhouse gas emissions cannot exceed the global average temperature increase of 2 ◦ C in relation to pre-industrial levels remains uncertain. Despite this uncertainty, the direct implication is that enormous quantities of fossil fuels have, thus far, wrongly been counted as assets by hydrocarbon firms as they cannot be exploited if we want to keep climate under certain control. These are the so-called “toxic assets”. Due to the relationship among CO2 emissions, GDP, energy consumption, and energy efficiency, the concept of toxic assets can be transferred to toxic income, which is the income level that would generate levels of CO2 emissions incompatible with keeping climate change under control. This research, using a simulation model based on country-based econometric models, estimated a threshold for income per capita above which the temperature limit of 2 ◦ C would be surpassed. Under the business as usual scenario, average per capita income would be $14,208 (in constant 2010 USD) in 2033; and under the intervention scenario, which reflects the commitments of the COP21 meeting held in Paris in December 2015, the toxic revenue would be $13,433 (in constant 2010 USD) in 2036. Keywords: climate change; CO2 emissions; toxic income; contraction and convergence; Paris agreement; intended nationally determined contributions (INDC) JEL Classification: Q43; Q54; Q57 1. Introduction There is a high degree of scientific consensus regarding to the level of global warming that is causing climate changes. Global mean temperature rise, due to CO2 and other greenhouse gases (GHG) concentration levels in the atmosphere, should not exceed 2 ◦ C from pre-industrial times. The agreement on this value was reached at the Conference of the Parties (COP) 16, held in Cancún in 2010 [1]. This political target was established based on several studies [2,3], which had calculated the first estimation of the emission reductions needed. That threshold has been subject to some criticism and revision, even by the very same authors that helped to define it. To keep the temperature below 2 ◦ C [1–4], the emission reduction in 2020 was estimated to be around 25–40% comparing to 1990 baseline levels, and 50–80% for 2050. One of the reasons for this skepticism is the high level of uncertainty involved to reduce emissions [5]. Scientists also estimate the critical threshold for CO2 concentration levels in the atmosphere to be within 450 and 500 ppm of CO2 based on the 2 ◦ C Sustainability 2019, 11, 2448; doi:10.3390/su11082448 www.mdpi.com/journal/sustainability Sustainability 2019, 11, x FOR PEER REVIEW 2 of 25 Sustainability 2019, 11, 2448 2 of 25 (parts per million (ppm) is the ratio between the number of molecules in a gas and the number of molecules in dry air [6] ). Above this level, impacts on global climate regulation would be irreversible. rise These [2]approaches (parts per millionhave also(ppm) is the been ratio between contested by some the authors number [7] of molecules who doubt in the a gasusefulness and the numberof the of molecules in dry air [6] ). Above this level, impacts concept of climate stability. Only a holistic approach (economic, social and environmental) on global climate regulation would be in the irreversible. internationalThese context approaches can avoid havethealso major been contesteddisaster ecological by somehumanity authors [7]haswho doubt been the usefulness facing in the last of the concept of climate stability. Only centuries, originated by global warming and climate change. a holistic approach (economic, social and environmental) in theTheinternational level of CO context can avoid 2 emissions, and thetherefore major ecological disaster humanity the concentration has been facing in the atmosphere, in the last depends on centuries, originated by energy consumption, globalcomposed mostly warming of and climate fossil change. fuels. Since energy consumption is strongly linked The levelgrowth to economic of CO2and emissions, Gross Domesticand thereforeProduct the(GDP), concentration as shown in by theseveral atmosphere, studiesdepends [8–11], theon energy existence consumption, mostly composed of a critical threshold for greenhouseof fossil gases fuels. inSince the energy atmosphere consumption could imply is strongly linked to the existence of economic growth and Gross Domestic Product (GDP), as shown by a critical level for fossil fuels and therefore, for income. Above that level, we would interfere withseveral studies [8–11], the existence of a critical climate threshold for greenhouse gases in the atmosphere could imply the existence of a critical level regulation. for fossil Onefuels couldand arguetherefore, that thefor income. link between Above energy thatconsumption level, we would andinterfere GDP alone with climate is not regulation. enough, as the One could argue that the link between energy consumption world could change its energy mix towards low-carbon options. In fact, there is an ongoing debate and GDP alone is not enough, as the world about couldOn this issue. change the one its hand, energythere mix towards low-carbon are economies options. Inprior decarbonizing fact, there is analthough to 1990, ongoingat debate only about 0.3% perthisyear issue.[12]. On This the one hand, is the case there are economies for China and some decarbonizing other economies. prior toA1990, although structural at only 0.3% decomposition per year for analysis [12]. Thisfound China is thethatcasethe forcountry China and some other had reduced economies. carbon intensityA structural especially duedecomposition to structural analysis changes for in China found that the economy andthe countrythe through hadsubstitution reduced carbon intensity of energy especially sources [13].due to structural However, total changes in the economy and through the substitution of energy sources emissions kept growing in the analyzed period. The same approach was used for the Baltic countries, [13]. However, total emissions kept findinggrowing in the analyzed improvements only in period. The samebut carbon intensity approach was carbon not in total used for the Baltic[14]. emissions countries, finding This optimism improvements is shared by other onlyauthors in carbon who intensity assert that but well-being not in total could carbonbeemissions decoupled [14]. fromThis optimism energy is shared consumption by other authors who and carbon emissions [15]. assert that well-being could be decoupled from energy consumption and carbon emissions On the [15]. other hand, some authors [16] show how carbon emissions have been rising by more than 2% per On year the other since hand, 1990, some and the authors carbon [16] show how intensity carbon emissions reductions, agreed in have been rising by the Copenhagen more pledges, than 2% per year since 1990, and the carbon intensity reductions, would easily be surpassed by GDP growth rates. South Korea is a great example of a fast-growing agreed in the Copenhagen pledges, would countryeasily be surpassed increasing its carbonby GDP growth rates. intensity South Korea [17]. Some other isauthors a great example [18] are ofskeptical a fast-growing about country increasing its carbon intensity [17]. Some other authors decarbonization approaches and question some of the assumptions made in mitigation models, [18] are skeptical about decarbonization approaches which tend and question some to discontinue the of the assumptions relation between energy made in mitigation models, consumption and GDP, which i.e.,tend to discontinue leapfrogging. The the relation authors, betweenpoint however, energy outconsumption that historical andand GDP, i.e., leapfrogging. current trends do not Thesupport authors,those however, point out assumptions, that making historical and current leapfrogging trends do not support those assumptions, making leapfrogging less likely. less likely. Decarbonization Decarbonization isisneeded, needed, butbut it will be costly it will [19] and be costly [19]it should and it go alonggo should demand-side measures along demand-side such measuresas establishing caps for energy such as establishing caps for consumption [20]. However, energy consumption despite thedespite [20]. However, literaturethe discussed literature above and the ongoing discussions in policy and academic discussed above and the ongoing discussions in policy and academic forums, nowadays the world forums, nowadays the world is not is experiencing not experiencing a process a process of decarbonization, of decarbonization, as shown as shownin Figure 1. 1. in Figure 40 30 20 10 0 1970 1980 1990 2000 2010 2020 CO2 Emissions (Gt) Energy use (Gtoe) Figure Figure1.1.Global Globalcarbon carbonemissions emissionsand andprimary primaryenergy, 1971–2013. Source: energy, 1971–2013. [21]. Source: [21]. In In the the 44 44 years years period period from from 1971 1971 toto 2014, 2014, the the link link between between energy energy use use and and carbon carbon emissions emissions has has remained close. In the period 1971–1980, world energy consumption increased, on average, remained close. In the period 1971–1980, world energy consumption increased, on average, at 2.4% at 2.4% per year, while CO emissions increased at a yearly rate of 2%. Despite of the advances in energy per year, while2 CO2 emissions increased at a yearly rate of 2%. Despite of the advances in energy efficiency and in the and efficiency share inof therenewable sources, CO share of renewable 2 emissions sources, have increased CO2 emissions have in relative in increased terms withterms relative respect to with Sustainability 2019, 11, 2448 3 of 25 energy consumption. This result is in line with the work of some authors [22] who found that a scenario of no climate change or no technology transfer fits better with historical trends for India, the USA and Europe. Therefore, by considering the link between energy and carbon emissions, as well as between energy consumption and GDP as stated above, this research did not enter the discussion of decarbonization directly, but included it in two ways: (i) by considering the improvements in energy efficiency that can come with the transfer of technology; and (ii) by accounting for recent voluntary commitments, the so-called Intended Nationally Determined Contributions (INDC), presented in the Paris Agreement in the COP21 that took place in Paris in December 2015. This research relied also on the approach of contraction and convergence (C&C), introduced in 1996 by Aubrey Meyer at the Global Commons Institute in the UK [23–25]. The main idea of the approach is that we could consider the atmosphere as global commons to which every individual would have the same right of access. That would imply having the same right to emit greenhouse gases. Under this approach, global emissions should be distributed among countries in a per capita egalitarian way, taking as a reference one base year, for instance 2014. The goal would be that emissions per capita would be equal for all countries in a target year, for example 2050. This would imply that countries with emission levels above their target would need to reduce their levels, so that every country would converge to the equal per capita level defined for the final year. Due to the relation between CO2 emissions, GDP, energy consumption and energy efficiency, a certain level of income could be understood as toxic when it is accompanied by a level of CO2 emissions incompatible with keeping climate change under certain control. This new concept is regarded as useful for understanding human-nature relations. Moreover, it helps in understanding income inequality among nations. To test the concept, a simulation model was designed that estimates, under certain assumptions or parameters, the year in which toxic income would be reached. This exercise is not exempt from limitations, the main one being that the level of uncertainty increases as the period simulated increases. However, this limitation is only relative in the sense that the interest does not lie on the particular value for toxic income but on its dynamics over time. Toxic Income Conventional economic theory asserts that one of the main goals of economic policy is to increase the income level [26]. For instance, the axiom of non-satiation preferences assumes that more is better; but this is not necessarily true, as higher consumption levels imply higher environmental impacts, as in the case with CO2 emissions. Under this consideration, one could ask if a particular level of income could be considered “toxic” in the sense that it could induce to CO2 emissions incompatible with climate regulation. According to accounting rules, oil companies usually record among their assets oil and coal reserves valued at market prices. This helps companies to increase their total assets: the more reserves a company has, the wealthier that company is. However, what would happen if all those reserves were extracted and burnt? One of the clearest and most updated answers to that question is found in [27]. According to the authors, maintaining temperature rise below 2 ◦ C implies that we have to keep in the ground one third of current oil reserves, half of natural gas reserves, and 82% of coal reserves until year 2050. The implication of this is that hydrocarbon companies are accounting in their books some toxic assets, and therefore their balances can be questioned [28]. When speaking of income, we are referring to a distribution of per capita income, since not all countries have the same levels of per capita income, and even within each country disparities on income arise. Future income distribution depends on the different growth trajectories of each country. In a simplified way, the fastest-growing countries are the upper-middle income countries, followed by the low-middle income countries, with high-income and low-income countries lagging behind. These different rates of growth are modifying the distribution of (toxic) income. Sustainability 2019, 11, 2448 4 of 25 Generally speaking, increases in economic income might be harmful for the environment, but there are actions of effective decarbonization and distributive improvements. Other factors that affect the determination of toxic income are energy efficiency (GDP/energy consumption) and carbon intensity (CO2 emissions/GDP). Both variables are closely related to the degree of technological development of a country and the economic structure of a country, as shown, for instance, by York et al. [29]. In a very simple way, with more technology we can find less energy consumption and less CO2 emissions in relative terms. Similarly, in a very simplified way, we can say that industrial economies have higher levels of emission of pollutants than agrarian or services economies. Of course, we have to consider the levels of affluence or intensity of consumption. The Jevons’ paradox also works here, i.e., efficiency improvements can lead to a reduction in prices and, therefore, to an increase in consumption that ends up with higher total levels of consumption (and emission of pollutants) per capita [30]. Finally, the uncertainty mentioned above about the critical thresholds of CO2 concentration of greenhouse gases (GHG) (CO2 , CH4 , N2 O, HFCs, PFCs and SF6 ) induces a high degree of uncertainty in the distribution of toxic revenue. Thus, the toxic income would be the per capita income distribution that would generate levels of GHG emissions incompatible with the maintenance of climate change under control. Thus, toxic income depends on the atmospheric concentration of GHG, which depends on population, consumption levels, economic growth trajectories of different countries, energy efficiency, carbon intensity and physical conditions. The toxic income is also a dynamic concept as it depends on the changes in these different variables over time. 2. Data Data sources used to estimate toxic income are the following: “World Development Indicators” (WDI) database of the World Bank [21]; “World Population Prospects” of the United Nations, Department of Economic and Social Affairs, Population Division [31]; and the European Environment Agency (EEA) [32]. We selected the following series: population [21,31], total and per capita energy consumption, total and per capita CO2 and GHG emissions, total energy consumption, total and per capita real GDP (base year 2010) [21] and atmospheric greenhouse gas (GHG) concentrations (EEA) [32]. World Bank’s income groups were used for grouping countries: low income, middle income (lower and upper) and high income. Tables 1–5 describe the global framework for the variables mentioned above: population, GDP, CO2 emissions and energy consumption, grouped by income levels. Table 1. Population. Population Income Group Number of Countries 2014 Annual Growth (Millions) % Share Rate 1992–2014 (%) High income 78 1176 16.2 0.7 Upper middle income 56 2541 35.0 0.9 Lower middle income 52 2927 40.3 1.7 Low income 31 625 8.6 2.8 World 217 7269 100.0 1.3 Source: [21]. Sustainability 2019, 11, 2448 5 of 25 Table 2. GDP. GDP Per Capita GDP Income Group 2014 Annual 2014 Annual (Trillion Growth Rate Growth Rate % Share 1992–2014 (%) (US$2010) 1992–2014 (%) US$2010) High income 48.2 65.5 2.1 40,983 1.4 Upper middle income 19.3 26.2 4.8 7577 3.9 Lower middle income 5.8 7.8 5.0 1968 3.2 Low income 0.36 0.5 3.9 578 1.1 World 73.6 100.0 2.9 10,119 1.5 Source: [21]. Table 3. CO2 Emissions. CO2 Emissions Per capita CO2 Emissions Income Group 2014 Annual 2014 Annual Growth Rate Growth Rate (Gt) % Share (t) 1992–2014 (%) 1992–2014 (%) High income 12.9 35.7 0.4 11.0 −0.2 Upper middle income 16.8 46.4 3.7 6.6 2.8 Lower middle income 4.3 11.9 3.1 1.5 1.4 Low income 0.2 0.4 2.4 * 0.3 −0.3 * World 36.1 100 2.2 5.0 0.9 Source: [21]. * 1998–2014. Table 4. Energy consumption. Energy Use Per Capita Energy Use Income Group 2014 Annual 2014 Annual Growth Rate Growth Rate (Gtoe) * % Share (Toe) 1992–2014 (%) 1992–2014 (%) High income 5.6 42.2 0.8 4.8 0.1 Upper middle income 5.6 42.4 3.3 2.2 2.4 Lower middle income 1.9 14.3 2.6 0.6 0.9 Low income 0.2 1.2 2.9 ** 0.4 0.3 * World 14.0 100 2.1 1.9 0.8 Source: [21]. * toe: Tons of oil equivalent. ** 2000–2014. Table 5. Energy efficiency (PIB/Energy). Energy Efficiency Income Group 2014 Annual Growth Rate ($/Koe) * 1992–2014 (%) High income 8.62 1.3 Upper middle income 3.42 1.6 Lower middle income 3.04 2.5 Low income 1.28 1.4 ** World 5.27 0.9 Source: [21]. * koe: Kilograms of oil equivalent. ** 1996–2014. Sustainability 2019, 11, 2448 6 of 25 Data in Tables 1–5 present a reality in the world with high levels of inequality in income levels, CO2 emissions, energy consumption, and energy efficiency, with gaps between countries that seem difficult to reduce. For instance, the high-income group of countries represents 16.2% of world population, while is responsible for 65.5% of world GDP, 35.7% of CO2 emissions, and 42.2% of energy consumption, and they have an average energy efficiency of 8.62 $/koe. On the other hand, countries of the lower middle income category represent 40.3% of world population, but they only generate 7.8% of world GDP, 11.9% of CO2 emissions, and 14.3% of world energy consumption, and they do have a much lower energy efficiency at only 3.04 $/koe. In other words, an average citizen of a high income country has an average GDP 20.9 times higher, emits 7.3 times more CO2 , and has an energy consumption eight times higher than an average citizen of a lower middle income country. Moreover, energy efficiency is also 2.8 times higher in high income countries. Methodology Even though projections for CO2 emissions considering a number of future scenarios by the Intergovernmental Panel on Climate Change (IPCC) exist, to estimate toxic income, we need projections that establish a parametric relation between CO2 emissions, GHGs and income (measured by GDP). This is why econometric models were used to establish the relation between the variables. In a country-base way, the following econometric models were applied: Linear model : CO2t = β0 + β1 yt + β2 Et + εt Linear spline model : CO2t = β0 + β1 yt|y0 + β2 y∗t|y + β3 Et + εt 0 Quadratic spline model : CO2t = β0 + β1 yt|y0 + β2 y2t|y 0 + β3 y∗t|y0 + β4 Et + εt where t is the time index; CO2t is per capita CO2 emissions; yt is per capita GDP in year t; y0 is the income threshold between the two sections of the regression; Et is energy efficiency defined as: GDP/energy consumption; and εt is the stochastic error with mean value of zero and constant variance. ( yt i f yt ≤ y0 yt|y0 = min( yt , y0 ) = y0 i f yt > y0 ( 0 i f yt ≤ y0 y∗t|y = max( yt − y0 , 0) = 0 yt − y0 i f yt > y0 Without accounting for energy efficiency, these models allow expressing several functional forms for the relation GDPpc–CO2 pc emissions: positive or negative linear function (or spline), environmental Kuznets curve (EKC) (inverse-U or inverse-V), and weak environmental Kuznets curve (Γ-curve or N-curve, that is, inverse-U or inverse-V shape, followed by a constant, positive or negative trench). These functional forms allow adjusting data in a very acceptable manner as well as creating long-term projections (20–30 years) for the dependent variable (CO2 pc). The traditional EKC presents an inverted U-shaped empiric relation between income and pollution. In theory, higher income causes at first an increase in environmental impact, while this is reduced when income reaches a certain value. The peak of the inverted U is the inflection point of pollution. In the weak version of the EKC, once the inflection point is reached, pollution does not decrease but remains stable for a good review of the EKC, see [33]. To get an econometric model for a specific country, the following procedure was used. Sustainability 2019, 11, 2448 7 of 25 1. First, we verified that emissions per capita and income per capita were co-integrated. For all countries, both time series were integrated of order 1, I (1), with the exception of China, whose time series were integrated of order 2, I (2). Then, the parameters were estimated using ordinary least squares (OLS). All econometric equations generated stationary residual at 5% level except for China and the Democratic Republic of Congo, whose residual were stationary at 10% level of significance. Therefore, the estimated parameters by OLS continued having good properties. In fact, the estimators were super consistent; they converged to the true value at a rate 1/T, √ instead of the habitual convergence ratio 1/ T [34]. 2. For low income countries, we applied only the linear model; for lower-middle income countries, we selected between the linear and the linear spline model. In both cases, the assumption is made of a positive relation between CO2 emissions and income. For upper middle and high income countries, we selected among the three models. The final section of the econometric function may have a positive, negative, or null slope, for those countries. The selection of the final model to be used was linked to the lowest value for the Akaike Information Criterion (AIC). 3. In the case of spline regression models, for each value of income, yt , a regression was estimated, and then the income threshold y0 was computed minimizing the Akaike information criterion, that is, y0 = min AIC. yt 4. Additionally, errors autocorrelation was verified. In the presence of autocorrelation, the correction term AR (1) was applied. 5. Finally, we only included countries with a coefficient of determination R2 larger than 0.6. We ended up with 76 countries, which make up 76.7% of world’s population and 76.9% of world’s CO2 emissions. A summary of the number of countries per income level and the resulting econometric regressions is presented in Table 6. Table 6. Econometric models. Econometric Model Percentage of World’s Income Group Number of Linear Linear Spline Quadratic Spline Countries Population (%) CO2 Emissions (%) High income 8 23 31 12.8 28.6 Upper middle income 3 4 18 25 30.6 38.9 Lower middle income 3 12 15 29.9 9.3 Low income 5 5 3.4 0.1 Total 11 24 41 76 76.7 76.9 Low income countries have a real problem of data availability. Only 10 out of 31 countries included in this group have complete time series for energy consumption in the period 1971–2014. Data on variables such population, GP and CO2 emissions are complete. The outcome was that half of these countries show deficient econometric adjustments. This is also due to their lack of a strong industrial sector, which implies the link between GDP per capita and CO2 emissions per capita is not clear. There may also be problems with the reliability of data. However, the use of 5 out of 31 countries belonging to this group does not generate an important bias to our results. This is explained as follows: (1) the group of low income countries represents a small share in global CO2 emissions (0.4%, see Table 3) and global income (0.5%, see Table 2); and (2) the econometric results are extended to the income group by means of expansion factors. In 2014, these five countries represented 25.4% of population, 38.2% of CO2 emissions and 17.7% of income of their group, therefore the possibility of a bias is reduced. As an example, the results of the parameters estimated for the USA, China, India and Ethiopia are shown in Table 7. Figure 2a–d shows the regression graphs. The complete estimates are provided in Appendix A. Sustainability 2019, 11, 2448 8 of 25 Sustainability 2019, 11, x FOR PEER REVIEW 8 of 25 Per capita CO2 emissions (t) Per capita CO2 emissions (t) Per capita GDP (thousands US$ 2010 ) Per capita GDP (thousands US$ 2010 ) (a) United States (b) China Per capita CO2 emissions (t) Per capita CO2 emissions (t) Per capita GDP (thousands US$ 2010 ) Per capita GDP (thousands US$ 2010 ) (c) India (d) Ethiopia Figure 2. Figure 2. Per Per capita capita GDP-Per GDP-Per capita CO22 emissions capita CO emissions regressions. regressions. Table 7. Table Results of 7. Results of the the estimated estimated parameters. parameters. (a) (a) United States United States China China Model: Model: Quadratic QuadraticSpline Spline Model: Model: Quadratic Quadratic Spline Spline * * 𝑅 2 R2 0.97 0.97 𝑅22 R 0.94 0.94 𝛽0 1.00 (0.47) 𝛽 1.30 (3.2) β0 1.00 (0.47) β00 1.30 (3.2) 𝑦| 0 2.14 (13.4) 𝑦| 0 5.60 (6.5) y 2.14 (13.4) y2 0 5.60 (6.5) 𝑦 2| 0 t|y0 −0.03 (−9.8) 𝑦t|y| 0 −0.91 (−3.5) ∗ y2 y2t|y ∗ 𝑦 | 0 t|y0 −0.03 0.53 (−9.8) (18.1) 𝑦 | 00 −0.91 1.57 (−3.5) (16.3) 𝐸 yt|y∗ −4.16 0.53 (−21.6) (18.1) y𝐸∗ t|y0 −2.81 1.57 (−10.0) (16.3) 0 𝑦0 E 31.27 −4.16 (−21.6) 𝐴𝑅(1) Et 0.79 −2.81 (−10.0) (4.1) t 𝑑 1.80 𝑦0 2.26 y0 31.27 AR(1) 0.79 (4.1) AIC 0.63 𝑑 1.46 d 1.80 y0 2.26 ndat 55 AIC −1.06 AIC 0.63 d ndat 1.4644 ndat 55 (b) AIC −1.06 India ndat Ethiopia 44 Model: Linear Spline * Model: Linear 𝑅2 0.99 𝑅2 0.91 𝛽0 0.27 (3.5) 𝛽0 0.067 (7.4) 𝑦| 0 2.48 (17.2) 𝑦 4.248 (7.2) 𝑦 ∗| 0 1.36 (17.1) 𝐸 −2.05 (−6.8) 𝐸 −0.58 (−7.5) 𝑑 2.211 Sustainability 2019, 11, 2448 9 of 25 Table 7. Cont. (b) India Ethiopia Model: Linear Spline * Model: Linear R2 0.99 R2 0.91 β0 0.27 (3.5) β0 0.067 (7.4) yt|y0 2.48 (17.2) yt 4.248 (7.2) y∗t|y 1.36 (17.1) Et −2.05 (−6.8) 0 EtPEER REVIEW Sustainability 2019, 11, x FOR −0.58 (−7.5) d 2.211 9 of 25 AR(1) 0.24 (2.4) AIC −7.434 𝐴𝑅(1)y0 0.24 0.66 (2.4) AIC ndat 34−7.434 𝑦0 0.66 ndat 34 d 1.83 𝑑 1.83 AIC −4.77 AIC −4.77 ndat 44 ndat 44 t values between parenthesis. * Autocorrelation error model. t values between parenthesis. * Autocorrelation error model. The Theestimate estimateforfor toxic income toxic incomewaswas carried out through carried a simulation out through modelmodel a simulation that estimates the values that estimates the of the variables values for the period of the variables for the 2014–2040. GDP per period 2014–2040. capita GDP per and energy capita efficiency and energy were projected efficiency using were projected the values using in the in the values basetheyear baseandyearapplying growth and applying rates.rates. growth Those ratesrates Those werewere established according established to according the simulation scenario. On the other hand, population projections were taken from to the simulation scenario. On the other hand, population projections were taken from United United Nations Population Division. Division. Nations Population The The estimation of estimation of CO CO22 emissions, emissions, greenhouse greenhouse gases gases (GHG) (GHG) andand toxic toxic income income isis described described schematically schematicallyin inFigure Figure3.3. Figure3.3.Toxic Figure Toxicincome incomeestimation. estimation. The Thepositive positiveside sideof ofthe thex-axis x-axisshows showstime; time;the thenegative negativeoneoneshows showstotal totaland andper percapita capitaCO CO2 2 emissions. The positive side of the y-axis plots GDP per capita and the negative, total CO 2 emissions. The positive side of the y-axis plots GDP per capita and the negative, total CO2 emissions emissions expressed expressedininppm. ppm. As Astime timegoes fromt0𝑡 onwards, goesfrom onwards,income incomeper percapita capitaincreases, increases,determining determiningthe theper percapita capitaincome income Y𝑌pc locus. The econometric equation, represented graphically by the curve CO locus. The econometric equation, represented graphically by the curve 𝐶𝑂 , establishes its 2pc , establishes its value valuedepending depending onon the the per capita income per capita income and and energy energyefficiency efficiency(this (thislatter lattervariable variableisisnotnotshown shownin Figure 3). Multiplying this value by population, total CO2 is obtained (CO locus). A percentage of the stream emissions are captured by the biosphere (soil, oceans and vegetation) and other, the residual, remains in the atmosphere (it is transformed into a stock). This lasting CO2 flow is expressed in ppm units by applying the corresponding conversion factor (cf locus). 𝑃𝑃𝑀(𝑡) is the flow of CO2 retained in the atmosphere and accumulated year by year to the initial stock (PPM0)—the shaded area Sustainability 2019, 11, 2448 10 of 25 in Figure 3). Multiplying this value by population, total CO2 is obtained (CO2 locus). A percentage of the stream emissions are captured by the biosphere (soil, oceans and vegetation) and other, the residual, remains in the atmosphere (it is transformed into a stock). This lasting CO2 flow is expressed in ppm units by applying the corresponding conversion factor (cf locus). PPM(t) is the flow of CO2 retained in the atmosphere and accumulated year by year to the initial stock (PPM0 )—the shaded area in the fourth quadrant of Figure 3. When the accumulated CO2 emissions reach the critical threshold determined as PPM∗ , that is when the shaded area is equal to PPM∗ , the critical period t∗ is reached. Then, the critical time t∗ is defined by: Z t∗ PPM0 + PPM(t)dt = PPM∗ (1) to where PPM0 is the atmospheric concentration of CO2 in the base year for the simulation (year 2014). ∗ where: The toxic income is then defined as Ypc ∗ Ypc = Ypc (t∗ ) (2) Income per capita in each year t, Ypc (t), is a vector containing the GDP per capita values of ∗ is really a set with m the m = 76 countries included in the simulation model, thus the toxic income Ypc n o ∗ = Y (t∗ ) , i = 1, 2, . . . , m ). elements (Ypc pc i A more refined definition of toxic income is to define it as the probability density of the income distribution over the critical period t∗ . These 76 countries can be viewed as a sample of all countries in the world. To account for global emissions, we used the corresponding expansion factors for each income group, which were simply calculated by the quotient between total CO2 emissions and CO2 emission added up for the countries in the simulation model. For simplicity, the model in Figure 3 only shows the flow for a single country. On the other hand, the graph presents the factors that affect the result for the toxic income: (1) the world’s income distribution (levels of per capita GDP for the countries); (2) the diverse paths of economic growth; (3) the relation between real per capita income and per capita CO2 emissions (which depend on economic structure, energy sources, technology and consumption levels); (4) population and population growth; and (5) CO2 sinks (“bad” sinks: oceans and soil; and “good” ones: forests). Thus, the estimates for the toxic income Ypc∗ and for the critical time t∗ change with the parameters and assumptions of the simulation. Equations (1) and (2) can also be used to define the toxic input from the GHG concentration instead of CO2 concentration. We assumed that there is a stable relation between global CO2 emissions and global GHG emissions. For 2008–2012, the ratio of global CO2 and GHG emissions was 0.6608. Following (IPCC, 2014), we assumed that 40% of CO2 emissions remain in the atmosphere. For greenhouse gases (GHG), this percentage is 42% (this parameter was estimated from the equation: GHGt (ppm) = GHGt−1 (ppm) + α ∗ GHG(t)/7.8, thus the values of GHGt are equal for the extreme years of the period 1990–2013). On the other hand, the technical coefficient to transform the flow of CO2 emissions in ppm is 7.8 (7.8 Gigatons of CO2 ≈ 1 ppm) [35]. As indicated in the Introduction, there is a great deal of uncertainty about the critical level of concentrations that can lead to an increase in global average temperature and the effects that could be caused by climate change. To simplify this analysis, we took as critical threshold a concentration of 530 ppm of GHG. Mitigation scenarios reaching concentration levels of about 530 ppm of GHG are more likely than not to limit temperature change to less than 2 ◦ C [36]. Simulation scenarios As is common in simulation studies, we considered two scenarios. The business as usual scenario considered that the future replicates the past, and the intervention scenario reflected the political will Sustainability 2019, 11, 2448 11 of 25 to change current trends for a desired future. In this second scenario, an attempt was made to reflect the results of the commitments of the COP21 meeting held in Paris in December 2015. By 15 October 2015, 147 countries presented 119 Intended Nationally Determined Contributions (INDC). To calculate the critical time and the toxic income, data for 26 years were projected, from the base year 2014 to 2040. In both scenarios, estimated population corresponded to projections from the UN Population Division [31]. Additionally, since the USA, China and India combined represent 52% of global emissions and the USA alone accounts for 40.7% of emissions in its income group, China accounts for 61.4% of emissions of its group and India accounts for 52% of emissions of its group, each of these countries was analyzed as if it were a group itself. Business as usual scenario For the Business as Usual (BaU) scenario, in the case of per capita GDP and energy efficiency (EE) growth, we used the average growth rate for the period 1992–2014. In the period 2014–2016, we used actual per capita GDP data. As USA, China and India share the greatest percentage (52%) of global CO2 emissions, we used their own growth rates; except for China’s per capita GDP growth, which was assumed to be equal to India’s growth as the economic growth of China has lowered in the last years. From 2031 onwards, we assumed these countries behave the same as their income group (see Table 8). Table 8. Business as usual (BaU) scenario. GDP Growth (%) EE Growth (%) 2014–2016 2017–2030 2031–2040 2013–2030 2031–2040 High income—USA 1.3 1.0 Upper middle—China 1.8 1.6 Lower middle—India 2.4 2.8 Low income Actual data 1.1 1.4 USA 1.5 1.3 1.9 1.0 China 5.1 1.8 4.2 1.6 India 5.1 2.4 2.5 2.8 COP21 scenario. When the estimates for per capita CO2 emissions trajectory decreased, according to the C&C approach, we supposed the trajectory converged to the 2012 world’s mean, 5 tons per capita. In the so-called COP21 scenario, we assumed that CO2 emissions follow a consistent path with the proposals presented by nations at the COP21. Following the synthesis report for these INDC [37], aggregate greenhouse gas emissions are expected to grow, when compared to 2010 levels, by 13% in year 2025 and 17% in 2030. In this second scenario, we assumed that the abatement of CO2 emissions was fulfilled by lessening the income growth rate and increasing energy efficiency. From 2018 to 2030, we established an ad-hoc growth rate for per capita GDP and adjust the energy efficiency growth rate to satisfy the restrictions on COP21 scenario. Specifically, we assumed that economic growth in rich countries (medium and medium-high income) is halved, India reduces its growth 1% and China grows at the same rate as India. Taking into consideration that, for lower-medium and low income level countries, the lack of growth is problematic, poor countries do not reduce their economic growth rate. From year 2031 onwards, we assumed that the changes in economic growth and energy efficiency return to their historical values (see Table 9a,b). Sustainability 2019, 11, 2448 12 of 25 Table 9. (a) COP 21 scenario. GDP growth. (b) COP 21 scenario. Energy efficiency growth. (a) GDP Growth (g) 2014–2016 2017–2030 (%) 2031–2040 (%) High income—USA 0.7 1.3 Upper middle—China 0.9 1.8 Lower middle—India Actual 2.4 2.4 Low income Data 1.1 1.1 USA 0.8 1.3 China 4.1 1.8 India 4.1 2.4 (b) EE Growth (%) 2014–2017 2018–2025 2025–2030 2030–2040 High income—USA 1.0 2.4 2.5 1.0 Upper middle—China 1.6 3.1 3.1 1.6 Lower middle—India 2.8 4.2 4.3 2.8 Low income 1.4 2.9 3.0 1.4 USA 1.9 3.4 3.5 1.9 China 4.2 5.7 5.8 4.2 India 2.5 3.9 4.0 2.5 3. Results and Discussion The critical period t∗ is reached when GHG concentration in the atmosphere reaches the threshold of 530 ppm (Figure 4). Under the assumptions of the BaU scenario, the critical time is t∗ = 2033 that is, in 14 years from the current year 2019. For the scenario COP21, the critical period is extended only by three years, until t∗ = 2036. The main explanation for this limited difference between the scenarios is that the level of GHG accumulated in the atmosphere until 2014 (PPM0 , Figure 3), as a consequence of CO2 emissions by the current high-income countries since the industrial revolution, is quite high (GHG = 441 ppm, equivalent to 83% of the threshold of 530 ppm). Therefore, the room for change is rather limited, an example that responsibilities are common but (very) differentiated. The main differences between these two scenarios are in energy and environmental indicators. The combined effort to reduce the rate of economic growth and increase the rate of growth of energy efficiency generates a significant reduction in global CO2 and per capita emissions (Figures 5 and 6). In fact, in 2036, CO2 emissions per capita under the COP21 scenario are 27.8% lower than the per capita emissions of the BaU scenario, while total CO2 emissions are 27.3% lower. extended only by three years, until 𝑡 ∗ = 2036. The main explanation for this limited difference between the scenarios is that the level of GHG accumulated in the atmosphere until 2014 (PPM0, Figure 3), as a consequence of CO2 emissions by the current high-income countries since the industrial revolution, is quite high (GHG = 441 ppm, equivalent to 83% of the threshold of 530 ppm). Therefore, the room for Sustainability 2019,change 11, 2448 is rather limited, an example that responsibilities are common but (very) 13 of 25 differentiated. 600 550 530 ppm threshold 500 450 Sustainability 2019, 11,400 x FOR PEER REVIEW 13 of 25 2010 2015 2020 2025 2030 2035 2040 efficiency generates a significant reduction in Bau global CO 22 and per capita emissions (Figures 5 and 6). COP21 In fact, in 2036, CO22 emissions per capita under the COP21 scenario are 27.8% lower than the per Figure capita emissions of the BaU scenario, 4. 4. GHG while Figure totalconcentration GHG (ppm). CO22 emissions concentration are 27.3% lower. (ppm). The main differences between these two scenarios are in energy and environmental indicators. The combined effort 8 to reduce the rate of economic growth and increase the rate of growth of energy 6 4 2 0 2010 2015 2020 2025 2030 2035 2040 Bau COP21 Figure 5. Figure 5. CO222 per capita emissions (tons per capita). 70 60 50 40 30 20 10 0 2010 2015 2020 2025 2030 2035 2040 Bau COP21 Figure 6. 6. CO CO22 emissions (Gt). Figure 2 emissions (Gt). Table 9a,b On the other hand, Table 9a,b shows shows that, to meet the commitments of COP21, efforts to increase energy efficiency efficiency are areofofgreat greatmagnitude. magnitude.ForFor example, according example, to our according to econometric estimates, our econometric were estimates, countries to reduce their economic growth to what is assumed for this scenario, energy were countries to reduce their economic growth to what is assumed for this scenario, energy efficiency growth should increase 1.5% in all countries to fulfil the emissions reductions commitment. That is, high-income countries should increase their energy efficiency growth 2.5 times, medium-high and low income countries should double their energy efficiency, and medium-low income countries should increase it by 53%. On the other hand, the last two graphs show that the effort to slow down economic growth and increase energy efficiency must be permanent, i.e. it should go beyond 2030. Sustainability 2019, 11, 2448 14 of 25 growth should increase 1.5% in all countries to fulfil the emissions reductions commitment. That is, high-income countries should increase their energy efficiency growth 2.5 times, medium-high and low income countries should double their energy efficiency, and medium-low income countries should increase it by 53%. On the other hand, the last two graphs show that the effort to slow down economic growth and increase energy efficiency must be permanent, i.e., it should go beyond 2030. Otherwise, CO2 and GHG emissions will immediately resume their growing trends. Even assuming that global CO2 emissions stabilize at a certain value, for instance the value of 38.0 Gigatons from 2025, thermodynamics would imply that the flow would be absorbed by the good sinks (forests) or bad ones (oceans and soil, increasing the oceans acidity) or would be accumulated in the atmosphere (accentuating the greenhouse effect and climate change). The planet would not reach a stationary state in terms of emissions (when total emissions are absorbed by sinks), but rather would see CO2 concentration in the atmosphere rising over time. 3.1. Results2019, Sustainability by Income Group 11, x FOR PEER REVIEW 14 of 25 Figure 7a,b Figure 7a,bpresents presentsCO CO2 emissions per capita and total CO emissions for the four income groups 2 emissions per capita and total2 CO2 emissions for the four income under the Business as Usual scenario. groups under the Business as Usual scenario. There is There is aa big big difference difference inin CO emissions in CO22 emissions in per per capita capita terms terms among among the the four four groups. groups. The The relation relation of higher income resulting in higher emissions is maintained, however the gap between higher of higher income resulting in higher emissions is maintained, however the gap between higher income and income andupperupper middle income middle countries income is closed countries from 2030 is closed fromonwards, accordingaccording 2030 onwards, to our simulation. to our On the other simulation. On hand, the gaps the other hand, among upper the gaps middle among upperincome middlecountries, lower middle income countries, lowerincome middleand low income income not only remain, but even grow over time. Low income countries, however, and low income not only remain, but even grow over time. Low income countries, however, are are characterized by very low levels characterized by veryof CO emissions low2 levels of COper capita. The final outcome is that the growth in per capita 2 emissions per capita. The final outcome is that the growth in emissions per capita at world level emissions is 1.3% at world perisyear. level 1.3% per year. 14.0 70.0 12.0 60.0 10.0 50.0 8.0 40.0 6.0 30.0 4.0 20.0 2.0 10.0 0.0 0.0 2010 2015 2020 2025 2030 2035 2040 2010 2015 2020 2025 2030 2035 2040 High Upper Middle Lower Middle Low World High Upper Middle Lower Middle Low World (a) CO2 emissions per capita (b) CO2 emissions (Gt) Figure 7. Figure 7. CO CO22 emissions emissions per per income income group. group. BaU BaU scenario. scenario. Global CO CO22emissions emissions byby income group income show group the effect show of population the effect upon emissions of population per capita, upon emissions per as the different capita, groups groups as the different have different levels of have different population. levels Upper middle of population. income income Upper middle group, group, which includes which China,China, includes showsshows the combined the combinedeffect of emissions effect of emissions perper capita, capita,population populationand and population growth, with withthe theresult resultthat thatthis thisgroup group hashas 49% 49% of total of total emissions. emissions. HighHigh income income countries countries represent represent 24% 24% of global of global emissions emissions and lower and lower middle middle income income countries countries accountaccount for Despite for 21%. 21%. Despite the similar the similar share share in emissions, in emissions, high income high income countries countries only haveonlyabout have40% about of 40% of the population the population of lowerofmiddle lower income middle income countries.countries. Low income Low income countries countries accountaccount for onlyfor only 0.7% of 0.7% globalofemissions. global emissions. Figure 8a,b presents CO22 emissions emissions per capita and total CO22 for for the four income groups under the COP21 scenario. Figure 8a shows the effects of the Intended Nationally Determined Contributions. The main 14.0 50.0 efforts fall upon high income countries, which should almost halve their emissions, although they 12.0 are still above the world’s average emissions. Upper40.0 middle income countries, despite the reduction 10.0 in the growth path for emissions, would become30.0the group with higher emissions per capita. 8.0 Lower middle income and low income countries, despite maintaining their trend in the growth 6.0 20.0 4.0 10.0 2.0 0.0 0.0 2010 2015 2020 2025 2030 2035 2040 2010 2015 2020 2025 2030 2035 2040 High Upper Middle Lower Middle Low World High Upper Middle Lower Middle Low World Global CO2 emissions by income group show the effect of population upon emissions per capita, as the different groups have different levels of population. Upper middle income group, which includes China, shows the combined effect of emissions per capita, population and population growth, with the result that this group has 49% of total emissions. High income countries represent 24% of global Sustainability 2019, emissions 11, 2448 and lower middle income countries account for 21%. Despite the similar 15 of 25 share in emissions, high income countries only have about 40% of the population of lower middle income countries. Low income countries account for only 0.7% of global emissions. of emissions, Figure 8a,b would have CO presents lower than the average emissions per capita. The global average remains 2 emissions per capita and total CO2 for the four income groups under almost stable, the COP21 scenario.keeping the average for the period 2010–2015, that is, 5.0 tons per capita. 14.0 50.0 12.0 40.0 10.0 30.0 8.0 6.0 20.0 4.0 10.0 2.0 0.0 0.0 2010 2015 2020 2025 2030 2035 2040 2010 2015 2020 2025 2030 2035 2040 High Upper Middle Lower Middle Low World High Upper Middle Lower Middle Low World (a) CO2 emissions per capita (t) (b) CO2 emissions (Gt) Sustainability 2019, 11, x FOR PEER REVIEW 15 of 25 Figure8. Figure 8. CO CO22 emissions emissions by by income income group. group. COP21 COP21 scenario. scenario. Including population into the analysis shows the net effect on high income countries, which would see a 8a Figure Including reduction shows the population in total into emissions. effects the Upper showsmiddle of the Intended analysis income Nationally the net effect and highlower Determined on income middle income Contributions. countries, whichcountries, Thewould main however, efforts see would fall upon a reduction increase in high their emissions income countries, total emissions. 1% and which income Upper middle 4.3%, should and respectively. almost lowerhalve The impact theirincome middle emissions, of low although countries, income they however, countries would would are stillincrease above the beworld’s their even more emissions reduced average 1% than emissions. and 4.3%, inUpper the BaU respectively. scenario, middle The income impact accounting ofcountries, for low income only despite 0.4% of global the reduction countries would be emissions, in themore even growthon average. path than reduced for emissions, in the BaUwould become scenario, the group accounting with0.4% for only higher emissions of global per capita. emissions, Lower on average. middle income and low income countries, despite maintaining their trend in the growth of emissions, 3.2. would Estimated Toxic than have lower Income Incomethe average emissions per capita. The global average remains almost stable, keeping Mean thetoxic average for the income. income. period Under Under 2010–2015, the the assumptions assumptions thatofis, 5.0 ofBaU BaU tons per capita. scenario, scenario, at at thethe critical critical time 𝑡∗ = time t∗ 2033, = 2033, the the mean mean value value for toxic for toxic incomeincome be 𝑌 ∗ be wouldwould =Y ∗ 14,208 pc = (in14,208 (in constant constant 2010 USD).2010For USD). For thescenario, the COP21 COP21 ∗ ∗ ∗ scenario, the criticaltheperiod is 𝑡period critical = 2036is tand=the2036 mean and meanincome of the toxic of the decreases toxic income to a 𝑌 slightly decreases slightly = 13,433 to ∗ = 13,433 (in constant 2010 USD). That is, a reduction of only 5.4% (complete results can be a(inYpc constant 2010 USD). That is, a reduction of only 5.4% (complete results can be seen in Appendix seen in Appendix B). Figure 9 shows B).that Figure GDP 9 shows that GDP per capita duringperthe capita wholeduring the of period whole period of the the simulation is simulation is lower in the lower COP21inscenario the COP21 thanscenario than scenario. in the BaU in the BaU scenario. This This result result comes fromcomes from the assumptions the assumptions made made regarding regarding economic economic growth. The growth. The economic economic growth growth rate rate for for high high countries income income countries is reducedis reduced becausebecause global global GDP per GDP per capita capita is also is also reduced. reduced. This reduction, This reduction, however, however, is small, is small, at 5.4%,at due 5.4%,todue to inequality inequality in the in the distribution distribution of income. of income. 20.0 16.0 12.0 8.0 4.0 0.0 2010 2015 2020 2025 2030 2035 2040 Bau COP21 Figure 9. Toxic Toxic income (World per per capita capita GDP) GDP) (thousand (thousand constant constant 2010 2010 USD). USD). Toxic Toxic income distribution. distribution. The Thewhole wholeper percapita capitatoxic toxicincome income (GDP) (GDP) distribution distribution is presented is presented as as a relative a relative frequency frequency graph, graph, and and as as a probability a probability density density function function in in Figures Figures 1010 andand 11.11. 60,0% 50,0% 40,0% 30,0% Bau COP21 Figure 9. Toxic income (World per capita GDP) (thousand constant 2010 USD). Toxic 2019, Sustainability income distribution. The whole per capita toxic income (GDP) distribution is presented 11, 2448 16 of as 25 a relative frequency graph, and as a probability density function in Figures 10 and 11. 60,0% 50,0% 40,0% 30,0% 20,0% 10,0% 0,0% 3,7 11,0 22,3 37,5 53,8 68,8 83,5 99,0 113,0 127,0 141,0 Per capita GDP BaU COP21 Sustainability 2019, 11, x FOR Figure PEER REVIEW 10. Toxic Toxic income (World per capita GDP) frequency. frequency. 16 of 25 Kernel density estimate .03 .02 Density .01 0 0 50 100 150 Per capita GDP Business as usual scenario COP 21 scenario kernel = epanechnikov, bandwidth = 10.0000 Figure 11. Toxic Figure 11. Toxicincome income(World (Worldper percapita GDP) capita GDP)density function density (the(the function probability density probability function density was function estimated by using was estimated the “twoway by using kdensity” the “twoway StataStata kdensity” command). command). In relation to In relation totoxic toxicincome incomedistribution, distribution, there there areare small small differences differences between between the the BaUBaU scenario scenario and and the COP21 scenario in the relative frequency and the probability density the COP21 scenario in the relative frequency and the probability density function. function. It It is is worth worth mentioning mentioning that, that, in in the the period period between between 20142014 and and 2033 2033 oror 2036, 2036, income income inequality inequality is is reduced. The explanation is the deceleration observed in both high income countries reduced. The explanation is the deceleration observed in both high income countries and upper and upper middle income middle countries combined income countries with thewith combined increase in the growth the increase in therate for lower growth middle rate for lower income middlecountries income and countries and low income countries. The Gini coefficient goes from 0.6225 in 2014 to 0.5613scenario) low income countries. The Gini coefficient goes from 0.6225 in 2014 to 0.5613 in 2033 (BaU in 2033 and 0.5602 in 2036 (COP21 scenario). (BaU scenario) and 0.5602 in 2036 (COP21 scenario). 3.3. Historical Responsibilities Related to Toxic Income 3.3. Historical Responsibilities Related to Toxic Income We defined toxic income as the level of per capita income that would generate levels of GHG We defined toxic income as the level of per capita income that would generate levels of GHG emissions incompatible with the maintenance of climate change under control. Under this framework, emissions incompatible with the maintenance of climate change under control. Under this which would be the common but differentiated responsibilities that have driven income to become toxic? framework, which would be the common but differentiated responsibilities that have driven income The initial concentration level for GHG (PPM0 in Figure 3) is one the factors that determines to become toxic? the critical level of concentration in this analysis. According to the literature [38], between 1750 The initial concentration level for GHG (PPM0 in Figure 3) is one the factors that determines the and 2014 the world emitted 1.5 million Gt of CO2 . According to our simulation, in the BaU scenario critical level of concentration in this analysis. According to the literature [38], between 1750 and 2014 (2015–2033), cumulated CO2 emissions would total 885 thousand Gt, whereas, under the COP21 the world emitted 1.5 million Gt of CO2. According to our simulation, in the BaU scenario (2015– scenario (2015–2036), CO2 emissions would be 871 thousand Gt. Under the assumption that GHG 2033), cumulated CO2 emissions would total 885 thousand Gt, whereas, under the COP21 scenario (2015–2036), CO2 emissions would be 871 thousand Gt. Under the assumption that GHG emissions are proportional to CO2 emissions, emissions in the critical period t* would be distributed as indicated in Table 10. Sustainability 2019, 11, 2448 17 of 25 emissions are proportional to CO2 emissions, emissions in the critical period t* would be distributed as indicated in Table 10. Table 10. (a) Cumulated CO2 emissions, historical period 1751–2014. (b) Cumulated CO2 emissions. Simulation period 1751–2014. (a) Income Group (103 Gt) High Upper Middle Lower Middle Low World Cumulated emissions 872.0 423.2 112.8 7.4 1475.6 % share 59.1% 28.7% 7.6% 0.5% (b) BaU Scenario COP21 Scenario Income Group 2015–2033 1751–2033 2015–2036 1751–2036 103 Gt % Share 103 Gt % Share 103 Gt % Share 103 Gt % Share High 214.9 24.3% 1086.9 46.0% 187.6 21.5% 1059.6 45.2% Upper middle 436.2 49.3% 859.4 36.4% 431.1 49.5% 854.3 36.4% Lower middle 179.0 20.2% 291.8 12.4% 196.9 22.6% 309.7 13.2% Low 6.0 0.7% 13.4 0.6% 7.1 0.8% 14.5 0.6% World 884.8 2360.4 870.6 2346.2 Source: (a) [37]; (b) Simulation model. Historical responsibilities for CO2 emissions are absolutely differentiated. High income countries are responsible for 59.1% of cumulated CO2 emissions, whereas the upper middle income group of countries is responsible for 28.3% of cumulated emissions. Both lower middle income group and low income group combined would represent only 8.1% of cumulated emissions. The inequality is even more noticeable if we consider that high income countries only represented 16.2% of world population in 2014. The results for both simulation scenarios do not differ much. If we add historical data on emissions, the differences are even smaller. For instance, adding historical emissions in the case of the COP21 scenario, the share of cumulated GHG emissions by income group would be the following: 45.2% for high income countries; 36.4% for upper middle income countries; 13.2% for lower middle income countries; and 0.6% for low income countries. This very same inequality in the distribution of emissions among income groups can be seen when we compute the average per capita emissions per year (historical plus simulated) for both scenarios, as presented in Table 11. Table 11. Average historical and simulated CO2 emissions per capita per year. Income Group (t) High Upper Middle Lower Middle Low World BaU scenario1751–2033 10.6 5.2 1.7 0.2 4.8 COP21 scenario1751–2036 9.8 4.9 1.7 0.3 4.5 Source: [21], Simulation model. Under the COP21 scenario, which is the one with less inequality, between years 1751 and 2036, a citizen of a high income country would have emitted twice the amount of CO2 as a citizen of an upper middle income country; 5.8 times more emissions than a citizen of a lower middle income country; and 38.5 times more emissions than a citizen from a low income country. Table 12 shows the inequality in income among the different groups. Sustainability 2019, 11, 2448 18 of 25 Table 12. Income inequality among groups of countries. Income Group (constant 2010 USD) High Upper Middle Lower Middle Low World BaU scenario (2033) 54.980 14.186 4.031 731 14.208 COP21 scenario (2036) 52.607 13.372 4.045 754 13.433 Source: Simulation model. As in the previous case, a citizen of a high income country, under the COP21 scenario, would have an average income in the critical period t* (2036) 4.1 times higher than a citizen from a upper middle income country; 13.6 times higher than a citizen from a lower middle income country; and 73 times higher than a citizen from a low income country. The dynamics of current economic growth generates pollution and social inequality. This is the essence of toxic income at the world level. 4. Conclusions and Policy Implications If nothing changes in the mid-term, in about 14 years, the threshold for critical concentration in the atmosphere will be reached. This result makes us believe that INDC need to become mandatory commitments for the different countries. The impact of INDC on global CO2 emissions is noticeable. INDC are not mandatory, but voluntary measures. If implemented, the environmental consequences would be very positive. There is a need for combining energy efficiency measures in productive processes with moderation in consumption and in population growth, as well as reducing economic growth. That decrease in growth is what Tim Jackson calls “prosperity without growth” for rich countries [39]: productive restructuring of developed economies towards economic sectors with lower environmental impact. Low-middle income and especially low income countries would still be able to grow, in per capita terms, to guarantee decent living standards for their citizens. Global responsibilities of developed economies need not only be directed to avoid their own CO2 emissions and mitigating environmental impacts, but also to help to reduce CO2 emissions of the rest of the countries, especially in developing countries, and to improve living conditions in these countries. This can only be possible through scientific and technological cooperation and transfer of technology from the North to the South. This is the least that can be demanded in light of differentiated responsibilities, which are based on the historical contribution of rich countries to current concentration levels of greenhouse gases in the atmosphere. The analysis shows that, if current trends of population growth, income, emissions per capita and energy efficiency will be maintained, at some point, by 2033, the critical threshold for mean toxic income per capita of Ypc ∗ = 14,208 (in constant 2010 USD) would be reached. This means that any income level above that value could be considered as toxic income, as it would go hand in hand with CO2 emissions and greenhouse gases concentration levels above 530 ppm, which inevitably would increase global mean temperature threshold above 2 ◦ C. The distribution of toxic income additionally shows the high level of inequality that characterizes the world (Gini = 0.5602). Combating climate change needs to go hand in hand with fighting income inequality at a world level, giving room for economic development of poorer and more vulnerable countries. The essence of toxic income is pollution and inequality. INDCs presented at the last COP21 in Paris in December 2015 [37], need to become mandatory commitments for the different countries. After 2030, the world will need to enter a path of decreasing global and per capita CO2 emissions that will require a transition of our energy and transport sectors towards the massive use of renewable energies. In this sense, international agreements on climate change, additional to INDC, should include the proposals under C&C and apply them, not just to CO2 emissions as proposed in the past, but also Sustainability 2019, 11, 2448 19 of 25 to income levels of countries. Countries with income levels above the critical threshold should contract, allowing countries with income levels below the threshold to converge towards that critical value that would avoid temperature rise above 2 ◦ C. The verification of the existence of toxic income opens the discussion about the different strategies to confront climate change. Technology plays a key role in combating climate change. High income countries have both the technology and the economic means to abate GHG emissions, as well as to introduce adaptation and mitigation measures. On the other hand, poor countries lack the technology or the economic resources to confront this pressing problem. World Bank data [21] shows that R&D per capita investment in low income and middle income countries was 16.4 times lower than in high income countries. This translates in low levels of knowledge production. Low income and middle income countries had 251 patent applications per million inhabitants, compared to 1118 applications for high income countries in 2016 [21]. Additionally, another explaining factor is the current asymmetry in the international regime for intellectual property rights, that penalizes poor countries that do not have the resources for protecting their inventions, and that do not have access to new technology due to the high costs of the royalties involved [40]. In any case, it is the opinion of the authors that future strategies oriented to reduce emissions need to consider the drop in renewable energy costs and therefore the likelihood that these technologies may represent an ever growing share of the energy mix. As one can see, the consequences would not fall just on energy or climate policy, but also in pursuing certain levels of equality in the world distribution of income, which would be highly desirable. Future research will focus on the depiction of the convergence scenarios for the different groups of countries. Author Contributions: F.F. conceived the concept of toxic income; F.F. and R.B. conceived and designed the econometric model and the simulation; R.B. conducted the simulation; F.F., R.B., J.R.-M. and P.C. analyzed the data and wrote the paper. Funding: This research and the APC were funded by the Spanish Ministry of Science and Innovation under the Grant HAR2016-76814-C2-1-P (AEI/ FEDER UE). Acknowledgments: The authors would like to thank the editor and the anonymous referees for providing relevant comments that improved the paper. Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. Appendix A Econometric Regressions Low income countries Table A1. Linear model. Country R2 β0 yt Et AR(1) d AIC ndat 0.913 0.067 4.248 −2.050 2.211 −7.434 34 Ethiopia 7.421 7.191 −6.817 0.921 −1.000 2.445 −0.191 0.304 2.009 −3.181 44 Benin −8.684 14.721 −3.239 2.175 0.949 −0.014 0.283 −0.038 0.703 2.133 −5.983 44 Congo, Dem. Rep. −1.053 5.117 −2.214 5.333 0.751 −0.007 3.340 −1.224 0.423 1.723 −4.116 35 Mozambique −0.221 7.395 −7.391 1.647 0.909 −0.081 0.905 −0.162 0.740 1.634 −4.923 44 Nepal −1.412 5.579 −1.971 6.781 Lower middle income countries Sustainability 2019, 11, 2448 20 of 25 Table A2. Linear model. Country R2 β0 yt Et AR(1) d AIC ndat 0.760 0.095 0.554 −0.197 2.656 −1.005 44 El Salvador 0.499 11.091 −4.041 0.789 −0.357 0.552 −0.070 0.284 2.114 −1.921 44 Guatemala −0.830 5.905 −1.304 1.907 0.931 0.191 0.699 −0.138 0.312 1.627 −0.898 44 Indonesia 0.422 6.015 −0.709 4.062 Table A3. Linear spline model. Country R2 β0 yt|y y∗t|y Et AR(1) d AIC ndat 0 0 0.997 0.103 0.887 0.707 −0.093 0.551 1.651 −7.073 43 Bangladesh 2.766 55.419 33.485 −8.541 0.868 −1.444 2.042 0.928 −0.158 1.466 2.121 −1.042 44 Bolivia −2.297 4.537 8.135 −7.702 0.651 −0.823 1.069 0.040 −0.049 1.527 1.573 −2.106 44 Cote d’Ivoire −3.856 4.893 0.569 −1.485 0.792 0.521 0.069 1.202 −0.037 1.498 2.354 −1.520 44 Honduras 0.815 0.107 10.440 −0.243 0.796 4.920 1.647 4.705 −2.713 2.229 2.186 3.055 30 Mongolia 3.418 1.792 8.090 −3.673 0.993 0.728 0.696 0.515 −0.173 2.508 1.892 −3.798 44 Morocco 6.363 51.437 12.178 −8.261 0.801 0.078 0.928 0.572 −0.158 0.347 1.667 −4.331 44 Myanmar 3.987 6.368 6.081 −5.037 0.994 0.226 1.380 0.654 −0.340 1.014 2.017 −5.075 44 Pakistan 2.865 35.227 2.700 −5.973 0.988 0.988 0.919 0.540 −0.346 2.110 2.136 −2.840 44 Tunisia 2.432 11.115 23.957 −5.186 Egypt, Arab 0.975 0.954 0.760 1.021 −0.246 0.388 1.513 1.922 −1.803 44 Rep. 1.741 2.635 14.121 −2.669 3.442 0.997 0.269 2.478 1.362 −0.584 0.242 0.657 1.833 −4.773 44 India 3.500 17.217 17.105 −7.541 2.353 0.964 0.140 0.455 0.345 −0.107 0.618 1.947 2.485 −3.567 44 Sri Lanka 1.894 6.736 7.876 −2.925 3.938 Upper middle income countries Table A4. Linear model. Country R2 β0 yt Et d AIC ndat 0.872 2.470 0.333 −0.253 2.449 −0.952 44 Argentina 6.893 14.607 −2.880 0.753 0.857 0.645 −0.247 1.743 0.314 44 Ecuador 1.460 8.749 −3.610 0.934 4.185 0.368 −0.589 1.742 −1.040 44 Mexico 12.263 19.784 −11.287 Sustainability 2019, 11, 2448 21 of 25 Table A5. Linear spline model. Country R2 β0 yt|y y∗t|y Et AR(1) y0 d AIC ndat 0 0 0.936 0.446 0.274 0.046 −0.078 6.606 2.348 −1.954 44 Costa Rica 1.303 11.236 1.913 −2.841 0.848 0.896 0.545 0.292 −0.304 6.523 2.308 −0.538 44 Panama 5.389 10.185 7.270 −8.757 0.896 4.491 1.996 0.828 −3.181 6.217 2.045 0.492 44 South Africa 3.174 7.541 7.983 −17.026 0.997 1.845 0.459 0.324 −0.327 0.513 8.332 1.967 −2.544 55 Turkey 6.061 29.324 17.580 −9.248 3.240 Table A6. Quadratic spline model. Country R2 β0 yt|y y2t|y y∗t|y Et AR(1) y0 d AIC ndat 0 0 0 0.895 0.181 2.784 −0.446 1.149 −0.749 3.001 2.064 0.203 35 Albania 0.182 3.186 −2.203 7.376 −13.06 0.644 −5.021 4.907 −0.696 1.249 −0.134 4.091 2.065 0.816 44 Algeria −1.466 2.516 −2.446 2.567 −3.752 0.910 4.127 −1.503 0.389 0.544 −0.567 4.011 1.835 −0.412 34 Botswana 3.183 −1.608 2.729 5.605 −3.890 0.938 0.734 0.806 −0.037 0.266 −0.395 9.762 1.854 −1.865 44 Brazil 1.317 3.918 −2.836 8.088 −5.648 0.931 −121.8 77.278 −10.73 2.716 −6.991 3.782 2.351 1.502 35 Bulgaria −2.318 2.610 −2.582 13.685 −16.77 0.951 3.399 −2.130 0.630 0.204 −0.189 3.194 1.776 −1.351 44 Dominican Republic 3.804 −2.737 4.329 2.632 −3.330 0.714 35.089 −7.053 0.413 0.292 −0.352 12.131 1.985 3.506 44 Gabon 1.937 −1.985 2.461 1.688 −2.482 0.880 9.644 −2.986 0.471 0.450 −0.498 5.964 2.525 1.938 44 Iran, Islamic Rep. 1.950 −1.471 2.306 2.050 −9.868 0.878 844.7 −470.6 66.019 0.297 −0.947 3.674 1.652 0.086 44 Jamaica 2.320 −2.311 2.320 3.487 −13.354 0.969 4.225 −0.198 0.181 0.261 −0.709 3.349 2.540 −1.434 40 Jordan 5.463 −0.367 1.816 1.416 −21.15 0.988 2.654 −0.934 0.241 0.747 −0.047 5.500 2.380 0.228 44 Malaysia 2.367 −2.584 5.429 19.695 −0.264 0.998 0.596 0.739 −0.013 0.362 −0.310 6.747 2.025 −3.025 39 Mauritius 3.934 13.067 −2.185 11.702 −8.939 0.891 0.720 −0.173 0.142 0.332 −0.172 2.912 1.652 −2.486 44 Paraguay 2.808 −0.538 2.121 3.203 −2.851 0.922 2.506 −0.935 0.203 0.322 −0.080 4.070 2.677 −2.013 44 Peru 2.717 −1.723 2.496 7.972 −5.417 0.996 0.746 0.481 0.105 0.585 −0.279 3.969 1.828 −1.911 44 Thailand 5.344 2.760 3.029 14.106 −3.696 0.944 1.296 5.599 −0.906 1.569 −2.807 0.788 2.259 1.456 −1.058 44 China 3.205 6.536 −3.451 16.266 −10.03 4.094 0.773 −0.662 1.550 −0.152 0.440 −0.245 0.299 4.862 1.986 −2.377 44 Colombia −0.685 3.304 −2.686 10.182 −9.132 1.805 0.810 −10.857 9.691 −1.537 0.963 −0.621 0.263 3.473 1.909 −0.306 44 Cuba −1.282 1.756 −1.730 7.750 −8.657 1.720 Sustainability 2019, 11, 2448 22 of 25 High income countries Table A7. Linear spline model. Country R2 β0 yt|y y∗t|y Et AR(1) y0 d AIC ndat 0 0 0.958 1.714 0.450 0.009 −0.424 25.509 1.835 0.798 44 Israel 2.027 25.738 0.234 −5.058 0.666 9.153 −0.150 2.163 −0.066 15.473 2.275 4.825 44 Oman 2.265 −0.537 6.245 −2.058 0.975 17.319 0.013 2.085 −2.459 7.198 2.515 3.622 44 Trinidad and Tobago 2.274 0.012 24.748 −6.239 0.962 14.051 0.076 0.310 −0.846 34.442 1.748 0.245 55 United Kingdom 82.263 4.944 7.320 −13.792 0.928 2.838 0.329 0.053 −0.403 9.439 2.068 −1.548 44 Uruguay 23.158 15.428 3.085 −19.476 0.970 1.992 0.411 0.173 −0.278 0.513 13.191 1.914 0.149 44 Hong Kong SAR, China 1.521 4.085 11.037 −9.293 3.570 0.929 16.579 0.156 0.254 −1.476 0.207 32.148 1.979 1.114 55 Netherlands 17.995 8.061 10.476 −16.966 2.032 0.923 11.825 −0.002 0.068 −0.320 0.267 67.385 1.973 −0.790 35 Switzerland 13.043 −0.207 2.037 −6.161 1.446 Table A8. Quadratic spline model. Country R2 β0 yt|y y2t|y y∗t|y Et AR(1) y0 d AIC ndat 0 0 0 0.958 10.542 0.350 −0.002 0.081 −1.214 44.029 1.921 0.036 55 Austria 15.634 14.285 −4.544 2.191 −15.016 0.963 11.256 1.290 −0.016 0.508 −4.495 35.648 1.738 0.926 55 Canada 3.627 8.292 −5.467 12.441 −12.682 0.958 1.943 1.705 −0.134 0.385 −0.831 6.309 1.655 −0.205 44 Chile 0.847 1.762 −1.472 16.860 −7.380 0.994 3.452 0.626 −0.011 0.241 −0.665 22.560 2.403 −1.545 40 Cyprus 11.679 15.223 −8.821 29.469 −28.514 0.970 15.441 0.122 0.002 0.156 −1.112 50.262 2.098 0.618 55 Denmark 12.761 2.060 2.232 6.115 −25.735 0.920 −1.365 1.376 −0.029 0.094 −0.794 31.853 0.663 1.034 55 France −0.814 13.615 −13.286 2.704 −5.810 0.983 20.967 −0.891 0.021 0.243 −0.629 15.738 2.562 0.726 55 Greece 11.643 −3.632 2.040 17.648 −15.459 0.984 8.238 0.328 −0.002 0.946 −0.850 44.394 2.137 0.259 55 Japan 13.181 14.006 −4.428 9.250 −16.436 0.996 8.486 0.440 0.067 0.450 −2.072 5.405 1.938 0.026 44 Korea, Rep. 9.252 1.261 1.496 51.835 −12.215 0.938 −43.958 5.207 −0.074 0.321 −3.170 43.986 2.112 4.168 55 Luxembourg −2.227 4.665 −4.721 7.387 −9.767 0.983 2.711 0.615 −0.008 0.230 −0.486 13.333 1.983 −0.165 44 Malta 7.976 6.207 −1.435 9.519 −15.029 0.886 10.302 −0.206 0.010 −0.074 −0.672 30.363 1.734 0.859 38 New Zealand 1.431 −0.390 1.020 −1.127 −5.208 0.967 5.622 0.768 −0.038 0.155 −0.555 12.667 1.794 0.664 55 Portugal 5.856 3.508 −2.952 7.536 −8.153 0.664 6.093 1.129 −0.026 0.329 −0.668 38.117 1.905 4.048 44 Singapore 2.407 6.636 −6.857 2.237 −2.060 0.966 1.001 2.143 −0.032 0.531 −4.161 31.269 1.800 0.628 55 United States 0.469 13.448 −9.796 18.080 −21.608 Sustainability 2019, 11, 2448 23 of 25 Table A8. Cont. Country R2 β0 yt|y y2t|y y∗t|y Et AR(1) y0 d AIC ndat 0 0 0 0.980 15.323 0.017 0.011 0.303 −1.770 0.374 31.939 2.190 1.138 55 Australia 2.377 0.038 1.339 6.550 −5.857 2.779 0.920 6.111 1.283 −0.025 0.145 −1.710 0.533 29.914 1.925 1.187 55 Belgium 1.747 4.442 −4.066 4.451 −12.347 6.090 0.962 12.515 1.035 −0.015 0.372 −3.604 0.572 29.783 2.146 1.467 55 Finland 4.434 4.110 −2.651 10.338 −14.972 3.370 0.987 6.944 0.444 −0.003 0.304 −0.853 0.215 48.672 1.963 −0.686 45 Ireland 21.229 17.876 −7.108 9.762 −30.418 1.438 0.948 28.179 −1.720 0.056 0.203 −1.064 0.735 22.421 2.606 0.898 55 Italy 7.205 −3.829 4.341 6.749 −19.555 6.321 0.835 8.743 0.341 −0.002 0.183 −1.061 0.754 70.458 1.935 2.745 55 Norway 1.604 1.486 −0.862 1.970 −6.057 8.580 0.966 15.863 −0.265 0.008 0.415 −0.848 0.439 29.008 2.219 0.729 55 Spain 11.514 −2.816 3.550 3.130 −15.830 3.166 0.955 −15.067 2.484 −0.047 0.200 −1.266 0.443 35.239 2.020 1.228 55 Sweden −4.588 11.399 −11.995 5.843 −9.595 3.312 * t-values on the second row. Appendix B Table A9. Simulation results. Business as Usual Scenario COP 21 Scenario Popul PIBpc CO2pc CO2 GHG CO2 GHG PIBpc CO2pc CO2 GHG CO2 GHG Year Mill US$ t Gt Gt ppm ppm US$ t Gt Gt ppm ppm 2014 7269 10.1 5.0 36.1 54.7 397.5 440.9 10.1 5.0 36.1 54.7 397.5 440.9 2015 7355 10.3 5.1 37.4 56.6 399.9 444.6 10.3 5.1 37.7 57.0 399.9 444.7 2016 7442 10.5 5.2 38.5 58.3 402.3 448.5 10.5 5.2 38.7 58.6 402.3 448.6 2017 7550 10.8 5.4 39.2 59.3 404.8 452.5 10.8 5.5 39.3 59.5 404.8 452.5 2018 7633 11.0 5.5 40.1 60.6 407.3 456.5 11.0 5.5 40.2 60.8 407.3 456.6 2019 7715 11.2 5.5 41.0 62.0 409.9 460.7 11.1 5.4 40.0 60.6 409.9 460.7 2020 7795 11.4 5.6 41.9 63.5 412.5 464.9 11.2 5.3 39.8 60.3 412.4 464.7 2021 7875 11.6 5.7 42.9 65.0 415.2 469.2 11.3 5.2 39.6 59.9 414.9 468.7 2022 7954 11.8 5.7 43.9 66.5 418.0 473.7 11.4 5.1 39.2 59.4 417.3 472.6 2023 8032 12.0 5.8 45.0 68.1 420.8 478.2 11.6 5.0 38.9 58.8 419.8 476.6 2024 8110 12.2 5.9 46.1 69.8 423.7 482.9 11.7 4.9 38.5 58.2 422.2 480.4 2025 8186 12.5 6.0 47.3 71.5 426.7 487.7 11.8 4.8 38.0 57.5 424.6 484.3 2026 8261 12.7 6.1 48.5 73.3 429.8 492.6 12.0 4.8 38.3 57.9 427.0 488.1 2027 8335 12.9 6.2 49.7 75.2 432.9 497.6 12.1 4.8 38.6 58.4 429.5 492.0 2028 8408 13.2 6.3 51.0 77.2 436.1 502.7 12.2 4.7 38.8 58.8 431.9 496.0 2029 8480 13.5 6.4 52.4 79.3 439.4 508.0 12.4 4.7 39.1 59.2 434.4 499.9 2030 8551 13.7 6.5 53.8 81.4 442.8 513.5 12.5 4.7 39.4 59.6 436.8 503.9 2031 8621 13.9 6.5 54.6 82.6 446.3 519.0 12.7 4.7 39.9 60.3 439.4 507.9 2032 8691 14.0 6.6 55.4 83.8 449.7 524.6 12.8 4.8 40.4 61.1 441.9 512.0 2033 8759 14.2 6.6 56.2 85.0 453.3 530.2 13.0 4.8 40.8 61.8 444.5 516.1 2034 8826 14.4 6.7 57.0 86.2 456.9 536.0 13.1 4.8 41.3 62.6 447.1 520.3 2035 8893 14.5 6.7 57.8 87.4 460.5 541.8 13.3 4.8 41.8 63.3 449.7 524.5 2036 8958 14.7 6.8 58.6 88.6 464.2 547.7 13.43 4.9 42.3 64.1 452.4 528.8 2037 9023 14.9 6.8 59.4 89.9 468.0 553.7 13.6 4.9 42.8 64.8 455.1 533.1 2038 9086 15.0 6.9 60.2 91.1 471.8 559.8 13.7 4.9 43.3 65.6 457.8 537.5 2039 9149 15.2 6.9 61.0 92.3 475.6 566.0 13.9 4.9 43.9 66.4 460.6 541.9 2040 9210 15.4 7.0 61.8 93.6 479.5 572.2 14.1 5.0 44.4 67.1 463.4 546.4 Sustainability 2019, 11, 2448 24 of 25 References 1. 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