The Environmental Kuznets Curve
Summary and Keywords
The environmental Kuznets curve (EKC) is a hypothesized relationship between environmental degradation and GDP per capita. In the early stages of economic growth, pollution emissions and other human impacts on the environment increase, but beyond some level of GDP per capita (which varies for different indicators), the trend reverses, so that at high income levels, economic growth leads to environmental improvement. This implies that environmental impacts or emissions per capita are an inverted U-shaped function of GDP per capita. The EKC has been the dominant approach among economists to modeling ambient pollution concentrations and aggregate emissions since Grossman and Krueger introduced it in 1991 and is even found in introductory economics textbooks. Despite this, the EKC was criticized almost from the start on statistical and policy grounds, and debate continues. While concentrations and also emissions of some local pollutants, such as sulfur dioxide, have clearly declined in developed countries in recent decades, evidence for other pollutants, such as carbon dioxide, is much weaker. Initially, many understood the EKC to imply that environmental problems might be due to a lack of sufficient economic development, rather than the reverse, as was conventionally thought. This alarmed others because a simplistic policy prescription based on this idea, while perhaps addressing some issues like deforestation or local air pollution, could exacerbate environmental problems like climate change. Additionally, many of the econometric studies that supported the EKC were found to be statistically fragile. Some more recent research integrates the EKC with alternative approaches and finds that the relation between environmental impacts and development is subtler than the simple picture painted by the EKC. This research shows that usually, growth in the scale of the economy increases environmental impacts, all else held constant. However, the impact of growth might decline as countries get richer, and richer countries are likely to make more rapid progress in reducing environmental impacts. Finally, there is often convergence among countries, so that countries that have relatively high levels of impacts reduce them more quickly or increase them more slowly, all else held constant.
The environmental Kuznets curve (EKC) is a hypothesized relationship between various indicators of environmental degradation and income per capita. In the early stages of economic growth, pollution emissions increase and environmental quality declines, but beyond some level of income per capita (which varies for different indicators), the trend reverses, so that at high income levels, economic growth leads to environmental improvement. This implies that environmental impacts or emissions per capita are an inverted U-shaped function of income per capita whose parameters can be statistically estimated. Figure 1 shows a very early estimate of an EKC.
The EKC has been the dominant approach among economists to modeling ambient pollution concentrations and aggregate emissions since Grossman and Krueger (1991) introduced it. The EKC has been applied to a wide range of issues, from threatened species (McPherson & Nieswiadomy, 2005) to nitrogen fertilizers (Zhang et al., 2015), but debate continues in the academic literature on its usefulness or validity (Carson, 2010; Chow & Li, 2014; Kaika & Zervas, 2013b; Wagner, 2015).
Some dimensions of environmental quality in developed countries have definitely improved as they have become richer. City air and rivers in these countries have become cleaner since the mid-20th century, and in some countries, forests have expanded. Emissions of some pollutants, such as sulfur dioxide, have clearly declined in most developed countries in recent decades, but there is less evidence that other pollutants, such as carbon dioxide, ultimately decline as a result of economic growth. There is also evidence that emerging countries take action to reduce severe pollution. For example, Japan was one of the first countries to cut sulfur dioxide emissions in the early 1970s following a rapid increase in pollution—the most famous example of which was the Minamata mercury poisoning—when its income was still below that of the developed countries (Stern, 2005; Ziegler, 1995), and China has also acted to reduce sulfur emissions in recent years (Zhao et al., 2013). Furthermore, most estimates of the EKC are not econometrically robust.
The EKC has also been controversial because of its potential policy implications. Initially, many understood the EKC to imply that environmental problems might be due to a lack of sufficient economic development, rather than the reverse, as was conventionally thought, and some, such as Beckerman (1992), argued that the best way for developing countries to improve their environment was to get rich. This alarmed others, such as Arrow et al. (1995), as while this might address some issues like deforestation or local air pollution, it would likely exacerbate other environmental problems, such as climate change. Furthermore, the irreversibility of some environmental degradation could stop rich countries from correcting the environmental damage that occurred earlier in their development.
Background and Critique of the EKC as a Policy Prescription
Until the 1980s, mainstream environmental thought held that environmental impacts increase with the scale of economic activity, although either more or less environmentally friendly technology could be chosen. This approach is represented by the IPAT identity (Ehrlich & Holdren, 1971), which is given by Impact Population*Affluence*Technology. If affluence is income per capita, then the technology term is impact or emissions per dollar of income. The sustainable development concept first appeared in the 1980s, arguing that, in fact, development was not necessarily damaging to the environment and, also, that poverty reduction was essential for environmental protection (World Commission on Environment and Development, 1987). Grossman and Krueger (1991) introduced the EKC concept in their study of the potential impacts of the North American Free Trade Agreement (NAFTA). Environmentalist critics of NAFTA claimed that the economic growth that would result from introducing free trade would damage the environment in Mexico. Grossman and Krueger (1991) argued instead that increased growth would improve environmental quality in Mexico. To support this argument, they carried out an empirical analysis of the relationship between ambient pollution levels in many cities around the world and income per capita. They found that the concentrations of various pollutants peaked when a country reached roughly the level of Mexico’s per capita income at the time.
Relying on research by Shafik (1994), the World Bank’s 1992 World Development Report (WDR) popularized the EKC, arguing that: “The view that greater economic activity inevitably hurts the environment is based on static assumptions about technology, tastes, and environmental investments” (p. 38), and that “As incomes rise, the demand for improvements in environmental quality will increase, as will the resources available for investment” (p. 39). Others made this argument even more forcefully, with Beckerman (1992) claiming that “there is clear evidence that, although economic growth usually leads to environmental degradation in the early stages of the process, in the end the best—and probably the only—way to attain a decent environment in most countries is to become rich” (p. 482). However, Shafik’s (1994) research showed that not all environmental impacts decline at high income levels—both urban waste and carbon emissions rise monotonically with income per capita. Subsequent research confirmed her findings and has cast doubt on the validity of the EKC hypothesis for emissions of other pollutants as well. The ambient concentrations of many pollutants have declined in developed countries over time, as a result of increasingly stringent environmental regulations and technological innovations. However, the mix of pollution has shifted—in the case of air pollution, from particulate pollution to sulfur and nitrogen oxides to carbon dioxide. Economic activity is inevitably environmentally disruptive in some way because satisfying the material needs of people requires the use and disturbance of energy flows and materials. Therefore, an effort to reduce some environmental impacts may just aggravate other problems.
Arrow et al. (1995) criticized the application of the EKC to policy because it assumes that there is no feedback from environmental damage to economic production. The assumption is that environmental damage does not reduce economic activity sufficiently to stop the growth process and that any irreversibility is not too severe to reduce the level of income in the future. But, if higher levels of economic activity are not sustainable, attempting to grow fast in the early stages of development when environmental degradation is rising may prove counterproductive.
Arrow et al. (1995) and Stern et al. (1996) argued that an EKC-type relationship might be partly or largely a result of the effects of trade on the distribution of polluting industries. Trade theory suggests that, under free trade, countries will specialize in the production of goods that are intensive in the production inputs that they are endowed with in relative abundance. For developing countries, these are labor and natural resources, and for the developed countries, human capital and capital-intensive activities. If using natural resources results in environmental degradation, then improvements in environmental quality in developed countries and increases in environmental degradation in middle-income countries could reflect this specialization. Environmental regulation in developed countries might further encourage polluting activities to shift to developing countries. Eventually, however, poor countries, as they themselves become wealthy, would be unable to find further countries from which to import resource-intensive products and would then face the more difficult task of abating these activities rather than outsourcing them to other countries. Testing whether offshoring drives emissions reductions is not simple. The popular consumption-based emissions approach (Peters & Hertwich, 2008) does not answer this question. Developed countries might be net importers of emissions because developing countries use more emissions-intensive technologies in general than do developed countries rather than because developed countries offshored production of their own most emissions-intensive goods to developing countries (Kander et al., 2015). Research has found a weak role if any for offshoring of production in reducing emissions in developed countries (Antweiler et al., 2001; Cole, 2004; Levinson, 2010; Stern, 2007), although trade in electricity among states in the United States might have allowed a reduction in carbon emissions in the richer states (Aldy, 2005).
Some early EKC studies showed that a number of indicators, including SO2 concentrations and deforestation, peaked at around the then world mean per capita income. The WDR implied that this meant that growth would reduce these impacts going forward. However, income is not normally distributed, but is very skewed, with much larger numbers of people below mean income per capita than above it. Therefore, it is median, rather than mean, income that is the relevant variable for predicting the future trend in emissions. Selden and Song (1994) and Stern et al. (1996) performed simulations that, assuming that the EKC relationship is valid, showed that global environmental degradation was set to rise for a long time to come. More recent estimates show that the emissions turning point is higher, and so this issue is less important than it was.
Another possible interpretation of the EKC is that developing countries do not have any environmental policies and only when a certain income threshold is passed are policies introduced (Stokey, 1998). This does not actually seem to be the case. Dasgupta et al. (2002) presented evidence that environmental improvements are possible in developing countries and they argued that peak levels of environmental degradation in those countries will be lower than in countries that developed earlier. Some developing countries have also pledged quite ambitious climate policies (Stern & Jotzo, 2010), and China and other countries have taken extensive action to reduce emissions of air pollutants (Zhao et al., 2013). However, the high rate of economic growth in some middle-income countries has in many cases overwhelmed their efforts at environmental improvement. Instead of arguing that growth is the only way to reduce environmental impacts, existing environmental institutions in developing countries need to be strengthened in order to further offset the effects of that growth.
The existence of an EKC can be explained either in terms of deep determinants, such as technology and preferences, or in terms of “proximate factors” derived from index decomposition analysis.1 These include scale, composition, and technique effects (Grossman & Krueger, 1991):
Scale refers to the level of production. If there were no change in the structure or technology of the economy, pure growth in the scale of the economy would result in a proportional growth in pollution. The traditional view that economic development and environmental quality are conflicting goals reflects the scale effect alone.
The composition effect refers to the mix of different industries and products in an economy. These each have different pollution intensities, and typically, over the course of economic development, the output mix changes in predictable ways. Economies start out mostly agricultural and the share of industry in economic activity first rises and then falls as the share of agriculture declines and the share of services increases. Impacts associated with agriculture, such as deforestation, might be expected to decline, while the impacts associated with industry, such as pollution, would first rise and then fall, ceteris paribus.
The technique effect is the change in pollution intensity, controlling for industry and product mix. It consists of three subfactors:
1. Changes in input mix that involve the substitution of less environmentally damaging inputs to production for more damaging inputs and vice versa. For example, natural gas might substitute for coal.
2. Changes in productivity that result in less use, ceteris paribus, of polluting inputs per unit of output.
3. Pollution-control technologies that result in less pollutant being emitted per unit of potentially polluting input. For example, sulfur can be scrubbed from the emissions of a power station, reducing pollution, even though the amount of coal used is not reduced and actually will increase in order to power the scrubber.
These proximate factors may in turn be driven by changes in variables like environmental regulation, innovation policy, or trade, which themselves may be driven by other more fundamental underlying factors, such as consumer preferences and technology.
Various theoretical models attempt to explain how preferences and technology might interact to produce different environmental quality time paths. There are two main approaches in this literature—static models that treat economic growth as simply shifts in the level of output, and dynamic models that model the economic growth process as well as the evolution of emissions or environmental quality (Kijima et al., 2010).
In the typical static model, a representative consumer maximizes a utility function that depends on consumption and the level of pollution, which is also an input to production. These models assume that there are no un-internalized externalities. Pasten and Figueroa (2012) show that, under the simplifying assumption of additive preferences:
where P is pollution, K is “capital”—all other inputs to production apart from pollution—σ is the elasticity of substitution between K and P in production, and η is the (absolute value of the) elasticity of the marginal utility of consumption with respect to consumption. The smaller σ is, the harder it is to reduce pollution by substituting other inputs for pollution. The larger η is, the harder it is to increase utility with more consumption. So, in other words, pollution is more likely to increase as the economy expands the harder it is to substitute other inputs for pollution and the easier it is to increase utility with more consumption. This result also implies that, if both the parameters are constant, then pollution cannot first increase and then decrease. In the various static models, the EKC is primarily driven by either changes in the elasticity of substitution or changes in the elasticity of marginal utility as the economy grows (Pasten & Figueroa, 2012).
Additive preferences imply that the marginal utility of consumption is not a function of the level of environmental quality. This might be an overly restrictive assumption. If preferences are nonadditive, but homothetic, the elasticity of substitution between consumption and environmental quality in the utility function, ϕ, becomes the critical parameter in place of η (Figueroa & Pasten, 2015). The second inequality in (1) then becomes ϕ ≥ σ.2
Dynamic models of the EKC vary in their assumptions about how institutions govern environmental quality, and there is no simple way to summarize their results. The nature of collective decision making influences the income–pollution path chosen, and, hence, societal utility. For example, in Jones and Manuelli’s (2001) model, the young can choose to tax the pollution that will exist when they are older, while Stokey (1998) assumes that countries do not adopt any environmental policies until they reach a threshold income level.3
Brock and Taylor’s work takes into account more features of the data than previous research had; specifically, the constant ratio of abatement costs to GDP and the decline over time in emissions intensity. But their Green Solow Model makes no explicit assumption about either consumer preferences or the pricing of pollution. Rather, they assume, on the basis of the stylized facts, that a constant share of economic output is spent on abating pollution. Their model builds on Solow’s (1956) economic growth model by adding the assumptions that production generates pollution but that allocating some final production to pollution abatement can reduce pollution. The resulting model implies that countries’ levels of emissions will converge over time, though emissions may rise initially in poorer countries due to rapid economic growth. While the predictions of the Green Solow model seem plausible given the recent empirical evidence (see “Empirical Evidence”), it leaves various assumptions unexplained. Furthermore, there is actually little correlation between countries’ initial levels of income per capita and their subsequent growth rates, the mechanism that is supposed to drive convergence of income in the Solow model (Durlauf et al., 2005; Stefanski, 2013).
Ordás Criado et al. (2011) also develop a neoclassical growth model, which finds that, along the optimal path, pollution growth rates are positively related to the growth rate of output and negatively related to emission levels. The latter arises because utility is a function of both the consumption of goods and the level of pollution, and defensive expenditures can be used to reduce pollution. Econometrically, this model reduces to a beta convergence equation with the addition of an economic growth effect. This is a more elegant theoretical model than the Green Solow model, and empirically the model explains more of the variation in the data. However, the initial level of emissions could explain the growth rate of emissions for reasons other than the defensive expenditures effect, such as the diffusion of technology from low emissions countries to high emissions countries.
In order to take the composition effect into account, Lopez and Yoon (2014) generalize a dynamic model with endogenous growth to multiple outputs. To overcome the increase in complexity, the model makes many strong assumptions. The clean sector consists of an AK endogenous growth model,4 while the dirty sector consists of a constant elasticity of substitution (CES) production function that uses capital and pollution inputs and constant total factor productivity. The consumer also has CES preferences over the dirty and clean goods, but pollution damage enters welfare additively. The government internalizes the pollution externality with a pollution tax. As a result of the two different technologies in the two production sectors, the elasticity of substitution between pollution and capital will vary over time, and, depending on the value of the elasticity of substitution in dirty production, the elasticity of substitution between dirty and clean goods in consumption, and the elasticity of marginal utility, an EKC may or may not be generated.
Grossman and Krueger’s original EKC estimates used a simple cubic function of the levels of income per capita, while Shafik (1994) regressed levels of the environmental indicators on quadratic or cubic functions of the log of income per capita. Neither of these approaches constrains the dependent variable to be nonzero. Regressions that allow levels of environmental impact to become negative are inappropriate except in the case of the net rates of change of the stock of renewable resources, where, for example, afforestation can occur. The nonzero restriction can be applied using a logarithmic dependent variable. The standard EKC regression model is then:
where E is the natural logarithm of either ambient environmental pollution or other environmental impacts or emissions per person, Y is the natural logarithm of gross domestic product per capita, ειτ is a random error term, and i indexes countries and t, time. The first two terms on the right-hand side of the equation are, respectively, country and time effects. The assumption is that, although the level of emissions per capita may differ over countries at any particular income level, the elasticity of emissions with respect to income is the same in all countries at a given income level. The time effects are intended to account for time-varying omitted variables and stochastic shocks that are common to all countries. We can find the “turning point” level of income, τ, where emissions or concentrations are at a maximum, using:
Usually the model is estimated with panel data, most commonly using the fixed-effects estimator. But time-series and cross-section data have also been used, and a very large number of estimation methods have been tried, including nonparametric methods (Azomahou et al., 2006; Carson et al., 1997; Tsurumi & Managi, 2015), though these do not generally produce radically different results from parametric estimates.
There are several econometric problems that can affect interpretation of EKC estimates, and much research has focused on addressing them. The most important are: omitted variables bias, integrated variables and the problem of spurious regression, and the identification of time effects. There is plenty of evidence that Equation (2) is too simple a model and that other explanatory variables are also important. Early studies used data that were mostly from developed countries. Subsequent studies that used data sets with greater income variation found increasingly higher turning points (Stern, 2004). Using an emissions database produced for the U.S. Department of Energy (Lefohn et al., 1999), which covered a greater range of income levels than any previous sulfur EKC studies, Stern and Common (2001) estimated the turning point for SO2 emissions at over $100,000. Stern and Common (2001) showed that estimates of the EKC for sulfur emissions were very sensitive to the choice of sample. For OECD countries alone, the turning point was at $9,000. Both Harbaugh et al. (2002) and Stern and Common found using Hausman test statistics that there is a significant difference in the regression parameter estimates when Equation (2) is estimated using the random-effects estimator and the fixed-effects estimator. This indicates that the regressors are correlated with the country and time effects, which suggests that the regressors are correlated with omitted variables. Harbaugh et al. (2002) re-examined an updated version of Grossman and Krueger’s data. They found that the locations of the turning points for the various pollutants, as well as even their existence, were sensitive both to variations in the data sampled and to reasonable changes in econometric specification.
Many studies extend the basic EKC model by introducing additional explanatory variables intended to model underlying or proximate factors, such as “political freedom” (Torras & Boyce, 1998), output structure (Panayotou, 1997), or trade (Suri & Chapman, 1998). Usually, the included variables turn out to be significant at traditional significance levels (Stern, 1998). Testing different variables individually is, however, still subject to the problem of potential omitted variables bias and there do not seem to be robust conclusions that can be drawn from these studies (Carson, 2010).
Panel data unit root tests find that sulfur and carbon emissions as well as GDP per capita are integrated variables. This means that, unless the cross-sectional dimension of the panel is much larger than the time-series dimension, we can only rely on regression estimates of Equation (2) using panel (or time-series) data if the regression cointegrates. Otherwise, we must estimate the model using another approach, such as differencing the data or the between estimator, which first averages the data over time (Stern, 2010). As an illustration of this point, Verbeke and De Clerq (2006) carried out a Monte Carlo analysis generating large numbers of artificial integrated time series and testing for an inverted U-shape relationship between the series. They found an “EKC” in 40% of cases despite using entirely arbitrary and unrelated data series.
Using data on sulfur emissions in 74 countries from 1960 to 1990, Perman and Stern (2003) found that around half the individual country EKC regressions cointegrate using standard panel data cointegration tests but that many of these regressions had parameters with “incorrect signs,” such as those that generate U-shaped EKCs. Some panel cointegration tests indicated cointegration in all countries, while some could not reject the noncointegration hypothesis. But even when cointegration was found, a common cointegrating vector for all countries was strongly rejected. These results also suggest that the simple EKC model omits important factors. This is because variables may be related but not cointegrate due to omitted unit root variables.
Wagner (2008) noted that standard cointegration tests are not appropriate when a model has nonlinear functions of unit root variables or there is cross-sectional dependence in the data. Wagner (2008) uses de-factored regressions and so-called second-generation panel unit root tests to address these two issues. Wagner (2015) uses time-series tests for nonlinear cointegration instead and finds cointegration in only a subset of the 19 countries tested.
Vollebergh et al. (2009) pointed out that time, income, or other effects are not uniquely identified in reduced form models like the EKC and that existing EKC regression results depend on the specific identifying assumptions that are implicitly imposed. For example, Equation (2) assumes that the time effect is common to all countries. Vollebergh et al. instead assume that there is a common time effect in each pair of most similar countries. They argue that this imposes the minimum restrictions on the nature of the time effect. Instead, Stern (2010) uses the between estimator—a regression using the cross-section of time-averaged variables—to estimate the effect of income. This model is then used to predict the effect of income on emissions using the time series of income in each country. The difference between this prediction and reality is the individual time effect for that country. This approach is, though, particularly vulnerable to omitted variables bias.
These studies find that the relationship between the levels of both sulfur and carbon dioxide emissions and income per capita is monotonic when the effect of the passage of time is controlled for (Stern, 2010; Vollebergh et al., 2009; Wagner, 2008). Both Vollebergh et al. (2009) and Stern (2010) find very large negative time effects for sulfur and smaller negative time effects for carbon since the mid-1970s.5 Musolesi and Mazzanti (2014) also find heterogeneous country-specific time effects. These, rather than the income effect, are the reason why carbon emissions have fallen in some European countries.
Besides the EKC, there are several other popular approaches to modeling the income-emissions relationship. The most prominent of these are decomposition analysis and convergence analysis.
Decomposition analysis breaks down emissions into the proximate sources of emissions changes (see “Theory”). The usual approach is to utilize index numbers and detailed sectoral information on fuel use, production, emissions, etc. Stern (2002) and Antweiler et al. (2001) developed econometric decomposition models that require less detailed data, and cruder decompositions that ignore structural change can use the Kaya identity (Raupach et al., 2007). These studies find that time-related technique effects, and in particular those directed specifically at emissions reduction, are the main means by which emissions of pollutants have been reduced. General productivity growth or declining energy intensity has a role to play, particularly in the case of carbon emissions where specific emissions-reduction technologies do not yet exist (Stern, 2004). Though changes in the output mix of the economy and shifts in fuel composition may be important in some countries at some times, their average effect seems less important quantitatively.
Those studies that include developing countries find that changes in technology occur in both developing and developed countries. Innovations seem to be adopted in developing countries with relatively short lags (Stern, 2004). This is seen, for example, in the case of lead in petrol, where most developed countries had substantially reduced the average lead content by the early 1990s but many poorer countries also had low lead contents (Hilton & Levinson, 1998).
Convergence analysis is motivated by the finding that emissions intensity has declined at a fairly constant rate over time in some developed emissions-intensive countries, such as the United States, that emissions intensity often falls long before emissions themselves do (Brock & Taylor, 2010), and that relatively more-emissions- intensive countries have seen a more rapid decline in emissions intensity than countries with low emissions intensity (Sanchez & Stern, 2016). These approaches also do not use time series of the levels of variables and, therefore, avoid the question of unit roots and cointegration.
Pettersson et al. (2013) provide a review of the literature on convergence of carbon emissions. There are three main approaches to testing for convergence: sigma convergence, which tests whether the dispersion of the variable in question declines over time using either just the variance or the full distribution (Ezcurra, 2007); stochastic convergence, which tests whether the time series for different countries cointegrate and, therefore, share a common stochastic trend; and beta convergence, which tests whether the growth rate of a variable is negatively correlated to its initial level. Using beta and stochastic convergence tests, Strazicich and List (2003) found convergence among the developed economies. Using a sigma convergence approach, Aldy (2006) also found convergence for the developed economies but not for the world as a whole. Using stochastic convergence, Westerlund and Basher (2008) reported convergence for a panel of 28 developed and developing countries over a very long period. Recent research using stochastic convergence finds evidence of club convergence rather than global convergence (Herrerias, 2013; Pettersson et al., 2013). By contrast, Brock and Taylor (2010) find beta convergence across 165 countries between 1960 and 1998.
Beta convergence has been heavily criticized (Evans, 1996; Evans & Karras, 1996; Quah, 1993) because dependence of the growth rate on the initial level of the variable is insufficient, though necessary (Pettersson et al., 2013), for sigma convergence. Beta convergence could also be purely due to regression to the mean (Friedman, 1992; Quah, 1993). However, it is hard to believe that, for example, the high levels of emissions intensity in formerly centrally planned economies are simply random fluctuations, which would have to be the case for regression to the mean to be relevant. In any case, some theories (discussed in the section “Theory”), such as Brock and Taylor’s (2010) Green Solow model and Ordás Criado et al.’s (2011) model, suggest that the initial level of emissions should be a factor in explaining emissions growth.
Three key early EKC studies were conducted by Grossman and Krueger (1991, 1995), Shafik (1994), and Selden and Song (1994). They set the stage for the empirical EKC literature that subsequently developed. The key results were that concentrations of local pollutants, such as suspended particulate matter (SPM), tended to decline as income rose or to peak at relatively low levels of income; that emissions of pollutants tended to peak later than concentrations did; and that global problems, such as carbon dioxide emission, might not peak at any relevant level of income.
Grossman and Krueger (1991) estimated the first EKC models as part of a study of the potential environmental impacts of NAFTA. They estimated EKCs for SO2, dark matter (fine smoke), and SPM using the GEMS dataset. The dataset consisted of a panel of ambient measurements from a number of locations in cities around the world. Each regression involved a cubic function in levels (not logarithms) of PPP (Purchasing Power Parity adjusted) per capita GDP, various site-related variables, a time trend, and a trade-intensity variable. The turning points for SO2 and dark matter were at around $4,000 to $5,000, while the concentration of suspended particles declined even at low income levels.
Shafik’s (1994) study was particularly influential, as the results were used in the 1992 WDR. Shafik estimated EKCs for ten different indicators using three different functional forms. She found that lack of clean water and lack of urban sanitation declined with increasing income and over time. Deforestation regressions showed no relation between income and deforestation. River quality worsened with increasing income. Local air pollutant concentrations, however, conformed to the EKC hypothesis, with turning points between $3,000 and $4,000. Finally, both municipal waste and carbon dioxide emissions per capita increased with rising income. Holtz-Eakin and Selden (1995) confirmed this result for carbon dioxide, which has stood the test of time despite a minority of contrary findings (Dobes et al., 2014).
Selden and Song (1994) estimated EKCs for four emissions series: SO2, NOx, SPM, and CO. The estimated turning points were all very high compared to the two earlier studies. For the fixed-effects version of their model, they are (in 1990 U.S. dollars): SO2, $10,391; NOx, $13,383; SPM, $12,275; and CO, $7,114. This showed that the turning points for emissions were likely to be higher than for ambient concentrations. In the initial stages of economic development, urban and industrial development tends to become more concentrated in a smaller number of cities, which also have rising central population densities, with the reverse happening in the later stages of development. So, it is possible for ambient pollution concentrations to fall in the later stages of development as income rises, even if total national emissions are rising (Stern et al., 1996).
A very large empirical literature developed in the wake of these pioneering studies. At the time of writing, the search term “environmental Kuznets curve” returns 1,072 records in the Scopus citation database. Several surveys (Carson, 2010; Dinda, 2004; Kaika & Zervas, 2013a, 2013b; Stern, 2004) have attempted to summarize the empirical findings. Carson (2010) concludes: “On the main message taken from Grossman and Krueger’s work by the economics profession—that trade and higher income levels would make for a better environment—the supporting evidence is scant, fleeting, and fragile ” (p. 19).
So, is the environmental Kuznets curve still a valuable approach to modeling the relationship between economic growth and environmental impacts or are the alternative approaches introduced in the previous section more powerful explanations? Amid the technicalities of the many econometric approaches, it is possible to lose sight of what the data actually say.
Figure 2 plots the mean values by country over a few decades for four key indicators against GDP per capita in 2005 PPP dollars. Per capita carbon dioxide emissions from fossil fuel combustion and cement production (Boden et al., 2013), shown in the top left panel, are almost linear in GDP per capita when plotted on log scales. There is little sign of an EKC effect in the raw data. On the other hand, it does look like sulfur emissions (Smith et al., 2011) flatten out with increasing income but there is little sign of an inverted-U curve. Both nonindustrial greenhouse gas emissions (Sanchez & Stern, 2016) and PM 2.5 concentrations (World Bank Development Indicators) show little relationship with income per capita. Clearly, additional variables or country effects would be needed to tease out any relationship for these two indicators.
The latter is a key point, because the means shown in the graphs are exactly the country means that the standard fixed-effects (or “within”) estimator first removes from the data before estimating the effect of income per capita on pollution.6 The assumption is that the observed variation in the mean levels is either contaminating information that will bias the estimated income-pollution relationship or is redundant information that does not add to the information provided by the evolution of emissions within each country. Stern (2010) proposed instead to use this cross-sectional information to estimate the EKC using the “between estimator.” The reason this estimator is rarely used is because researchers are worried about omitted variables bias. Any omitted variables are subsumed in the error term, while the fixed-effects estimator eliminates the country-specific means and so reduces the potential bias. Hauk and Wacziarg (2009), however, found that, when there is also measurement error in the explanatory variables (which can also bias the regression estimates), the between estimator performs well compared to alternatives. Fixed-effects estimation tends to inflate the effect of the measurement error.
Using the sulfur emissions example, Figure 3 gives us some insight into how the emissions–income relationship evolves over time. The first panel shows the levels of sulfur emissions in 1971 and the second in 2005. In both 1971 and 2005, sulfur emissions tended to be higher in richer countries and the curve seems to have shifted down and to the right over time. A cluster of mostly European countries had succeeded in sharply cutting emissions by 2005, but other wealthy countries reduced their emissions by much less.
Figure 4 shows how emissions changed within each individual country in the sample. The upper panel shows the relationship between the growth rate of per capita sulfur emissions and the level of income per capita at the beginning of the period. There is a negative correlation between the growth rates and initial log GDP per capita of -0.32, which supports the EKC hypothesis. But the correlation is weak, and emissions rose in many rich countries and fell in many poor countries. So, this does not provide strong support for the EKC being the best or only explanation of either the distribution of emissions across countries or the evolution of emissions with countries over time.
The lower panel of Figure 4 compares the growth rates of sulfur emissions with the initial level of emissions intensity. The negative correlation is much stronger here. The correlation between the growth rate of sulfur emissions and the log of emissions intensity is −0.67. This relationship is one of the key motivations for pursuing a convergence approach to modeling emissions rather than the EKC. Note that the tight cluster of countries that cut emissions the most appears to have had both high income and high emissions intensity at the beginning of the period. However, we do not need to accept just one way of analyzing the data. Combining multiple drivers of emissions might be a more productive approach.
In Brock and Taylor’s (2010) empirical analysis, the growth rate of emissions is a function of initial emissions per capita and there is convergence in emissions per capita across countries over time. Depending on the specification chosen, this model explains 14% to 42% of the variance in average national 1960–1998 CO2 emissions growth rates. Stefanski (2013) challenges Brock and Taylor’s findings, arguing that GDP growth rates have declined over time at a slower rate than emissions-intensity growth rates have. Therefore, it does not make sense to argue that emissions growth has slowed mainly due to Solow-style convergence of GDP growth rates.
Ordás Criado et al.’s (2011) model reduces econometrically to a beta convergence equation with the addition of an economic growth effect. They estimate the model for a panel of 25 European countries from 1980 to 2005 using 5-year period averages. Parametric estimates for SO2 emissions find that the rate of convergence is −0.021, the income elasticity of emissions is 0.653, and that there are strong negative time effects, particularly in countries with initially high levels of income. For NOx, the rate of convergence is −0.036 and there are again strong negative time effects, but the initial level of income has only a small and not very significant effect. Nonparametric estimates largely confirm their parametric estimates.
An alternative way of visualizing the data, first used in Blanco et al. (2014), plots the growth rate of emissions per capita against the growth rate of income per capita. Figure 5 presents this alternative view. The three emissions series show some positive correlation between the growth rates of the two variables. Clearly, the distribution of data is shifted downward for sulfur and nonindustrial greenhouse gas emissions relative to CO2. This implies that for sulfur and nonindustrial emissions, the intercept of a simple regression is negative; therefore, for a country with zero economic growth, emissions will be declining. This indicates that there is a negative average time effect. There seems to be much less of a relationship between the growth rate of PM 2.5 concentrations and the rate of economic growth.
These graphs support the theoretical model of Ordás Criado et al. (2011) that proposes a key role for economic growth in driving emissions. Stern et al. (in press), Sanchez and Stern (2016), Stern and van Dijk (2016), and Stern and Zha (2016) combine the Ordás Criado et al. (2011) approach with the EKC in a single equation framework that regresses the growth rate of pollution on the growth rate of GDP per capita, the initial level of GDP and pollution, the interaction between growth and initial GDP, and control variables.7 Stern et al. (in press) and Sanchez and Stern (2016) find that, for carbon dioxide emissions, sulfur emissions, and greenhouse gas (GHG) emissions from energy and industrial sources, the elasticity of emissions with respect to income is around 0.85 to 0.9. It is about half that for GHG emissions from land use and land-use change. Stern and van Dijk (2016) find the elasticity is around 0.2 for PM 2.5 concentrations. There are strong negative time effects for sulfur and industrial and nonindustrial GHG emissions, ranging from 1.0 to 1.5% per year in an English legal origin country at the mean level of both income and the other control variables that are included in the model. Time effects are insignificant for CO2 and PM 2.5.
All these studies find that when we control for convergence and other factors, the emissions–income relationship is either monotonic or the EKC turning point is at a very high level. However, for CO2 and sulfur emissions, Stern et al. (in press) show that the effect of economic growth on emissions growth diminishes as countries become richer, and Stern and van Dijk (2016) show the same for PM 2.5 concentrations. On the other hand, estimates of simple EKC models using the same data often result in an in-sample turning point. Stern and van Dijk (2016) find that the turning point for PM 2.5 is as low as $3,000 for a simple EKC model.
In common with the findings of Ordás Criado et al. (2011), these results show that both economic growth and initial emissions or concentration levels are needed to explain pollution growth, and that negative time effects are important for some pollutants. Although the results do not confirm early findings (Selden & Song, 1994; Stern et al., 1996) that concentrations of pollution have a lower income turning point, they do show that growth has relatively small impact on pollution concentrations compared to emissions and that this effect diminishes as countries get richer.
Conclusions and Future Research Directions
Despite the popularity of the EKC, the evidence supporting it is weak, especially when considering other ways of modeling the data. The relationship between economic growth and pollution emissions is monotonic. Negative time effects may be important for some pollutants, such as sulfur dioxide. The growth rate of emissions intensity declines with income per capita for both CO2 and sulfur emissions, as suggested by Stefanski (2013), but convergence is also important. Initial levels of pollution emissions, emissions intensity, or concentrations are associated with slower growth in pollution for all pollutants examined. The naïve econometric approaches used in much of the literature are also problematic. Convergence effects are important for most pollutants and time effects are important for many. These effects and others should get more attention than the EKC effect as opposing forces to the scale effect when modeling aggregate pollution emissions.
On the theoretical front, the assumption of most static models that pollution externalities are optimally internalized over the course of economic development does not seem very plausible. There is still scope for developing more complete dynamic models of the evolution of the economy and pollution emissions. Empirical research so far has not provided very sharp tests of alternative theoretical models, so that there is still scope for work of this sort, too. New, related topics also continue to emerge. One that has emerged in the wake of the great recession in North America and Europe in 2008–2009 is the question of what happens to emissions in the short run over the course of the business cycle. York (2012) found that carbon emissions rise faster with economic growth than they fall in recessions; however, Burke et al. (2015) conclude that there is no strong evidence that the emissions–income elasticity is larger during individual years of economic expansion as compared to recession but that significant evidence of asymmetry emerges when effects over longer periods are considered. Emissions tend to grow more quickly after booms and more slowly after recessions. More empirical research and theory building are needed on these short-run dynamics.
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(1.) The term “pollution” is used in the discussion for concreteness, but a similar analysis can be applied to other environmental impacts, too.
(2.) Under additive preferences . Under nonhomotheticity, a more complex expression determines whether there is an EKC or not. Homotheticity implies that the ratio of the marginal utility of consumption and the marginal utility of environmental quality is a function of the ratio of consumption and environmental quality alone.
(4.) The AK model (Romer, 1986) assumes that output is linear in capital. Because there are no diminishing returns to capital accumulation, in this model the economy grows endogenously without the exogenous source of technological change needed in the Solow growth model.
(5.) Negative time effect is used here to mean that emissions fall over time, ceteris paribus, so that for a levels model the first difference of the time effect is on average negative.
(6.) Of course, if an EKC is estimated in logs, it will be the mean log of pollution in each country that is actually subtracted from the dependent variable.