Introduction
The environmental Kuznets curve (EKC) is a hypothesized relationship between various indicators of environmental degradation and countries’ gross domestic product (GDP) per capita. In the early stages of economic growth environmental impacts and pollution increase, but beyond some level of GDP per capita (which will vary for different environmental impacts) 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, whose parameters can be statistically estimated. Figure 1 shows a very early example of an EKC. A vast number of studies have estimated such curves for a wide variety of environmental impacts ranging from threatened species to nitrogen fertilizers, though atmospheric pollutants such as sulfur dioxide and carbon dioxide have been most commonly investigated. The name Kuznets refers to the similar relationship between income inequality and economic development proposed by Nobel Laureate Simon Kuznets and known as the Kuznets curve.
The EKC has been the dominant approach among economists to modeling ambient pollution concentrations and aggregate emissions since Grossman and Krueger (1991) introduced it in an analysis of the potential environmental effects of the North American Free Trade Agreement. The EKC also featured prominently in the 1992 World Development Report published by the World Bank and has since become very popular in policy and academic circles and is even found in introductory economics textbooks.
Critique
Despite this, the EKC was criticized almost from the start on empirical and policy grounds, and debate continues. It is undoubtedly true that some dimensions of environmental quality have improved in developed countries 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 cut sulfur dioxide emissions in the early 1970s following a rapid increase in pollution when its income was still below that of the developed countries and China has also acted to reduce sulfur emissions in recent years.
As further studies were conducted and better data accumulated, many of the econometric studies that supported the EKC were found to be statistically fragile. Figure 2 presents much higher quality data with a much more comprehensive coverage of countries than that used in Figure 1. 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. A cluster of mostly European countries had succeeded in sharply cutting emissions by 2005 but other wealthy countries reduced their emissions by much less.
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 argued that the best way for developing countries to improve their environment was to get rich. This alarmed others, as while this might address some issues like deforestation or local air pollution, it would likely exacerbate other environmental problems such as climate change.
Explanations
The existence of an EKC can be explained either in terms of deep determinants such as technology and preferences or in terms of scale, composition, and technique effects, also known as “proximate factors”. Scale refers to the effect of an increase in the size of the economy, holding the other effects constant, and would be expected to increase environmental impacts. The composition and technique effects must outweigh this scale effect for pollution to fall in a growing economy. The composition effect refers to the economy’s mix of different industries and products, which differ in pollution intensities. Finally the technique effect refers to the remaining change in pollution intensity. This will include contributions from changes in the input mix – e.g. substituting natural gas for coal; changes in productivity that result in less use, everything else constant, of polluting inputs per unit of output; and pollution control technologies that result in less pollutant being emitted per unit of input.
Over the course of economic development the mix of energy sources and economic outputs tends to evolve 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. We might expect the impacts associated with agriculture, such as deforestation, to decline, and naively expect the impacts associated with industry such as pollution would first rise and then fall. However, the absolute size of industry rarely does decline and it is improvement in productivity in industry, a shift to cleaner energy sources, such as natural gas and hydro-electricity, and pollution control that eventually reduce some industrial emissions.
Static theoretical economic models of deep determinants, that do not try to also model the economic growth process, can be summarized in terms of two parameters: The elasticity of substitution between dirty and clean inputs or between pollution control and pollution, which summarizes how difficult it is to cut pollution; and the elasticity of marginal utility, which summarizes how hard it is to increase consumer well-being with more consumption. It is usually assumed that these consumer preferences are translated into policy action. Pollution is then more likely to increase as the economy expands, the harder it is to substitute other inputs for polluting ones and the easier it is to increase consumer well-being with more consumption. If these parameters are constant then either pollution rises or falls with economic growth. Only if they change over time will pollution first rise and then fall. The various theoretical models can be classified as ones where the EKC is driven by changes in the elasticity of substitution as the economy grows or models where the EKC is primarily driven by changes in the elasticity of marginal utility.
Dynamic models that model the economic growth process alongside changes in pollution, are harder to classify. The best known is the Green Solow Model developed by Brock and Taylor (2010) that explains changes in pollution as a result of the competing effects of economic growth and a constant rate of improvement in pollution control. Fast growing middle-income countries, such as China, then having rising pollution, and slower growing developed economies, falling pollution. An alternative model developed by Ordás Criado et al. (2011) also suggests that pollution rises faster in faster growing economies but that there is also convergence so that countries with higher levels of pollution are more likely to reduce pollution faster than countries with low levels of pollution.
Recent Empirical Research and Conclusion
Recent empirical research builds on these dynamic models painting a subtler picture than did early EKC studies. We can distinguish between the impact of economic growth on the environment and the effect of the level of GDP per capita, irrespective of whether an economy is growing or not, on reducing environmental impacts. Economic growth usually increases environmental impacts but the size of this effect varies across impacts and the impact of growth often declines as countries get richer. However, richer countries are often 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 faster or increase them slower. These combined effects explain more of the variation in pollution emissions or concentrations than either the classic EKC model or models that assume that either only convergence or growth effects alone are important. Therefore, while being rich means a country might do more to clean up its environment, getting rich is likely to be environmentally damaging and the simplistic policy prescriptions that some early proponents of the EKC put forward should be disregarded.
References
Brock, W. A. and Taylor, M. S. (2010). The green Solow model. Journal of Economic Growth 15, 127–153.
Grossman, G. M. and Krueger, A. B. (1991). Environmental impacts of a North American Free Trade Agreement. NBER Working Papers 3914.
Ordás Criado, C., Valente, S., and Stengos, T. (2011). Growth and pollution convergence: Theory and evidence. Journal of Environmental Economics and Management 62, 199-214.
Panayotou, T. (1993). Empirical tests and policy analysis of environmental degradation at different stages of economic development. Working Paper, Technology and Employment Programme, International Labour Office, Geneva, WP238.
Smith, S. J., van Ardenne, J., Klimont, Z., Andres, R. J., Volke, A., and Delgado Arias S. (2011). Anthropogenic sulfur dioxide emissions: 1850-2005. Atmospheric Chemistry and Physics 11, 1101-1116.
Stern, D. I. (2015). The environmental Kuznets curve after 25 years. CCEP Working Papers 1514.
Stern, D. I., Common, M. S., and Barbier, E. B. (1996). Economic growth and environmental degradation: the environmental Kuznets curve and sustainable development. World Development 24, 1151–1160.
The environmental Kuznets curve (EKC) is a hypothesized relationship between various indicators of environmental degradation and countries’ gross domestic product (GDP) per capita. In the early stages of economic growth environmental impacts and pollution increase, but beyond some level of GDP per capita (which will vary for different environmental impacts) 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, whose parameters can be statistically estimated. Figure 1 shows a very early example of an EKC. A vast number of studies have estimated such curves for a wide variety of environmental impacts ranging from threatened species to nitrogen fertilizers, though atmospheric pollutants such as sulfur dioxide and carbon dioxide have been most commonly investigated. The name Kuznets refers to the similar relationship between income inequality and economic development proposed by Nobel Laureate Simon Kuznets and known as the Kuznets curve.
The EKC has been the dominant approach among economists to modeling ambient pollution concentrations and aggregate emissions since Grossman and Krueger (1991) introduced it in an analysis of the potential environmental effects of the North American Free Trade Agreement. The EKC also featured prominently in the 1992 World Development Report published by the World Bank and has since become very popular in policy and academic circles and is even found in introductory economics textbooks.
Critique
Despite this, the EKC was criticized almost from the start on empirical and policy grounds, and debate continues. It is undoubtedly true that some dimensions of environmental quality have improved in developed countries 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 cut sulfur dioxide emissions in the early 1970s following a rapid increase in pollution when its income was still below that of the developed countries and China has also acted to reduce sulfur emissions in recent years.
As further studies were conducted and better data accumulated, many of the econometric studies that supported the EKC were found to be statistically fragile. Figure 2 presents much higher quality data with a much more comprehensive coverage of countries than that used in Figure 1. 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. A cluster of mostly European countries had succeeded in sharply cutting emissions by 2005 but other wealthy countries reduced their emissions by much less.
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 argued that the best way for developing countries to improve their environment was to get rich. This alarmed others, as while this might address some issues like deforestation or local air pollution, it would likely exacerbate other environmental problems such as climate change.
Explanations
The existence of an EKC can be explained either in terms of deep determinants such as technology and preferences or in terms of scale, composition, and technique effects, also known as “proximate factors”. Scale refers to the effect of an increase in the size of the economy, holding the other effects constant, and would be expected to increase environmental impacts. The composition and technique effects must outweigh this scale effect for pollution to fall in a growing economy. The composition effect refers to the economy’s mix of different industries and products, which differ in pollution intensities. Finally the technique effect refers to the remaining change in pollution intensity. This will include contributions from changes in the input mix – e.g. substituting natural gas for coal; changes in productivity that result in less use, everything else constant, of polluting inputs per unit of output; and pollution control technologies that result in less pollutant being emitted per unit of input.
Over the course of economic development the mix of energy sources and economic outputs tends to evolve 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. We might expect the impacts associated with agriculture, such as deforestation, to decline, and naively expect the impacts associated with industry such as pollution would first rise and then fall. However, the absolute size of industry rarely does decline and it is improvement in productivity in industry, a shift to cleaner energy sources, such as natural gas and hydro-electricity, and pollution control that eventually reduce some industrial emissions.
Static theoretical economic models of deep determinants, that do not try to also model the economic growth process, can be summarized in terms of two parameters: The elasticity of substitution between dirty and clean inputs or between pollution control and pollution, which summarizes how difficult it is to cut pollution; and the elasticity of marginal utility, which summarizes how hard it is to increase consumer well-being with more consumption. It is usually assumed that these consumer preferences are translated into policy action. Pollution is then more likely to increase as the economy expands, the harder it is to substitute other inputs for polluting ones and the easier it is to increase consumer well-being with more consumption. If these parameters are constant then either pollution rises or falls with economic growth. Only if they change over time will pollution first rise and then fall. The various theoretical models can be classified as ones where the EKC is driven by changes in the elasticity of substitution as the economy grows or models where the EKC is primarily driven by changes in the elasticity of marginal utility.
Dynamic models that model the economic growth process alongside changes in pollution, are harder to classify. The best known is the Green Solow Model developed by Brock and Taylor (2010) that explains changes in pollution as a result of the competing effects of economic growth and a constant rate of improvement in pollution control. Fast growing middle-income countries, such as China, then having rising pollution, and slower growing developed economies, falling pollution. An alternative model developed by Ordás Criado et al. (2011) also suggests that pollution rises faster in faster growing economies but that there is also convergence so that countries with higher levels of pollution are more likely to reduce pollution faster than countries with low levels of pollution.
Recent Empirical Research and Conclusion
Recent empirical research builds on these dynamic models painting a subtler picture than did early EKC studies. We can distinguish between the impact of economic growth on the environment and the effect of the level of GDP per capita, irrespective of whether an economy is growing or not, on reducing environmental impacts. Economic growth usually increases environmental impacts but the size of this effect varies across impacts and the impact of growth often declines as countries get richer. However, richer countries are often 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 faster or increase them slower. These combined effects explain more of the variation in pollution emissions or concentrations than either the classic EKC model or models that assume that either only convergence or growth effects alone are important. Therefore, while being rich means a country might do more to clean up its environment, getting rich is likely to be environmentally damaging and the simplistic policy prescriptions that some early proponents of the EKC put forward should be disregarded.
References
Brock, W. A. and Taylor, M. S. (2010). The green Solow model. Journal of Economic Growth 15, 127–153.
Grossman, G. M. and Krueger, A. B. (1991). Environmental impacts of a North American Free Trade Agreement. NBER Working Papers 3914.
Ordás Criado, C., Valente, S., and Stengos, T. (2011). Growth and pollution convergence: Theory and evidence. Journal of Environmental Economics and Management 62, 199-214.
Panayotou, T. (1993). Empirical tests and policy analysis of environmental degradation at different stages of economic development. Working Paper, Technology and Employment Programme, International Labour Office, Geneva, WP238.
Smith, S. J., van Ardenne, J., Klimont, Z., Andres, R. J., Volke, A., and Delgado Arias S. (2011). Anthropogenic sulfur dioxide emissions: 1850-2005. Atmospheric Chemistry and Physics 11, 1101-1116.
Stern, D. I. (2015). The environmental Kuznets curve after 25 years. CCEP Working Papers 1514.
Stern, D. I., Common, M. S., and Barbier, E. B. (1996). Economic growth and environmental degradation: the environmental Kuznets curve and sustainable development. World Development 24, 1151–1160.
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