The Environmental Kuznets Curve: 2021 Edition

The second encyclopedia chapter. First one is here.

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 a country’s economic development, 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 large 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 investigated most. Panayotou (1993) was the first to call this relationship the EKC, where 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 can be seen as an empirical version of the interpretation of sustainable development as the idea that development is not necessarily damaging to the environment and, also that poverty reduction is essential to protect the environment (World Commission on Environment and Development, 1987).

Figure 1. An Environmental 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 its beginning on empirical and policy grounds, and debate continues. It is undoubtedly true that some dimensions of environmental quality have improved in developed countries at the same time that 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 more mixed evidence for other pollutants such as carbon dioxide. Carbon emissions have fallen in the last 40 years in some developed countries such as the United Kingdom or Sweden, while they have increased in others such as Australia or Japan. 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 (Stern, 2005) 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. 

Initially, many understood the EKC to imply that the best way for developing countries to improve their environment was to get rich (e.g. Beckermann, 1992). This alarmed others (e.g. 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. Even if there is an EKC for per capita impacts, environmental impacts would increase for a very long time if the majority of the population is on the rising part of the curve and/or the population is also growing (Stern et al., 1996). 

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 should increase environmental impacts. The composition and technique effects must outweigh this scale effect for pollution or other environmental impacts 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, for example substituting natural gas for coal; changes in productivity that result in less use, ceteris paribus, of polluting inputs per unit of output; and pollution control technologies that result in less pollutant being emitted per unit of polluting 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. On the other hand, offshoring of pollution probably plays only a small role in cutting emissions in developed economies (Kander et al., 2015). 

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, which summarizes how difficult it is to cut pollution; and the elasticity of the marginal utility of consumption with respect to consumption, which summarizes how hard it is to increase consumer well-being with more consumption (Pasten and Figeroa, 2012). 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 Green Solow Model developed by Brock and Taylor (2010) 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 

Recent empirical research builds on these dynamic models to paint a subtler picture than early EKC studies did (Stern, 2017). 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. We can also distinguish between the effects of economic growth and the simple passage of time. 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. In econometric terms, the time effect – the change in emissions if economic growth is zero – may be higher in richer countries. Rapid growth in middle-income countries, such as China or India, is more likely to overwhelm the time effect in those countries as suggested by Brock and Taylor (2010).

Finally, there is often convergence among countries, so that those that have relatively high levels of impacts reduce them faster or increase them slower than countries with low levels of impacts. 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. 

References 

Arrow, K., Bolin, B., Costanza, R., Dasgupta, P., Folke, C., Holling, C. S., Jansson, B.-O., Levin, S., Mailer, K.-G., Perrings, C., Pimental, D., 1995. Economic growth, carrying capacity, and the environment. Science 268, 520–521. 

Beckermann, W., 1992. Economic growth and the environment: whose growth? Whose environment? World Development 20, 481–496. 

Brock, W. A.,Taylor, M. S., 2010. The green Solow model. Journal of Economic Growth 15, 127–153. 

Grossman, G. M., Krueger, A. B., 1991. Environmental impacts of a North American Free Trade Agreement. NBER Working Papers 3914. 

Kander, A., Jiborn, M., Moran, D. D., Wiedmann T. O., 2015. National greenhouse-gas accounting for effective climate policy on international trade. Nature Climate Change 5, 431–435. 

Ordás Criado, C., Valente, S., 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. 

Pasten, R., Figueroa, E., 2012. The environmental Kuznets curve: A survey of the theoretical literature. International Review of Environmental and Resource Economics 6, 195–224. 

Stern, D. I., 2005. Beyond the environmental Kuznets curve: Diffusion of sulfur-emissions-abating technology. Journal of Environment and Development 14(1), 101–124. 

Stern, D. I., 2017. The environmental Kuznets curve after 25 years. Journal of Bioeconomics 19, 7–28.

Stern, D. I., Common, M. S., Barbier, E. B., 1996. Economic growth and environmental degradation: the environmental Kuznets curve and sustainable development. World Development 24, 1151–1160. 

World Commission on Environment and Development, 1987. Our Common Future. Oxford: Oxford University Press.  

Inaugural Francqui Lecture: Economic Growth and the Environment

The video of my inaugural Francqui lecture on economic growth and the environment is now on Youtube:

 

The first part of the presentation comes from my teaching material on the environmental Kuznets curve. The slide of turning points in the literature is based on my 2001 paper with Mick Common in JEEM: "Is there an environmental Kuznets curve for sulfur?". The cross-sectional graphs on sulfur and carbon emissions are from my 2017 paper in the Journal of Bioeconomics: "The environmental Kuznets curve after 25 years". The longitudinal EKC for five countries uses data from the latest release of CEDS. The idea behind "explaining the paradox" – that there is a monotonic frontier that shifts down over time – is, I think, first expressed in the JEEM paper and then developed in my following papers in Ecological Economics (2002), World Development (2004), Journal of Environment and Development (2005), and then more recently in EDE (2017). Reyer Gerlagh created the original growth rates figure for greenhouse gas emissions, which was in the part I wrote of Chapter 5 of the WG3 volume of the 5th IPCC Assessment Report. A paper on carbon and sulfur emissions was eventually published with Reyer and Paul Burke as the EDE (2017) paper. The research on total greenhouse gas emissions was carried out with my masters student Luis Sanchez and published in Ecological Economics in 2016. This was before the first paper in this series – the EDE one – was eventually published because of the long review process that one went through. The research on PM 2.5 was carried out with my masters student Jeremy Van Dijk and published in Climatic Change in 2017.

Francqui Lectures Plan

I have now made a plan for my series of Francqui Lectures at Hasselt University. Unfortunately, given Australian government pronouncements, we have decided to make this an online only series. I had hoped to travel to Belgium mid-year, but that is now not going to be possible.

 

The inaugural lecture will take place in March and following that there will another 4 lectures over the next couple of months. They will focus on key areas of my research in recent years with introductions based on my ANU course material in environmental and energy economics. I have now written abstracts and made plans for each one:

Inaugural Lecture: Economic Growth and the Environment
What is the relationship between economic growth and environmental quality? The environmental Kuznets curve (EKC) hypothesis proposes that growth initially damages the environment but at higher income levels eventually improves the quality of the environment. The EKC has been a very popular idea over the last three decades despite being criticized almost from the start. The lecture will first review the history of the EKC and alternative approaches. Then applying an approach that synthesizes the EKC and alternative convergence approaches, it will show that convergence and non-growth time-related effects are important for explaining both pollution emissions and concentrations. Future research should focus on developing and testing alternative theoretical models and investigating the non-growth drivers of pollution reduction.

Lecture 2: Energy and Economic Growth and Development
All economic activity requires energy, but what is the relationship between energy use and economic growth and development? Richer countries tend to use more energy per person than poorer countries, but energy used per dollar of GDP tends to be lower in richer countries and decline over time globally. Countries are also becoming more similar – converging – in their energy use. This lecture will present evidence on these patterns and investigate the drivers of change.

Lecture 3: The Rebound Effect
Energy efficiency improvements that reduce the cost of providing energy services result in more use of those services reducing the energy saved. This is the direct rebound effect. There are also follow-on effects across the economy – such as the energy required to produce the other goods and services that consumers buy instead of energy – that can potentially make the economy-wide rebound much larger. Could the rebound be large enough for energy efficiency improvements to “backfire” by actually increasing rather than reducing energy use? The lecture will show how we can use a structural vector autoregression model to estimate the effect of energy efficiency shocks on energy use. The model is applied to the US, several European countries, and Iran demonstrating that economy-wide rebound is large, and backfire may be possible.

Lecture 4: Energy and the Industrial Revolution
Ecological and mainstream economists disagree on how important energy is for economic growth, and economic historians are divided on the importance of coal in fueling the increase in the rate of economic growth known as the Industrial Revolution. The lecture will argue that energy is much more important for growth when it is scarce than when it is abundant. Increasing energy services has much less effect on growth in developed economies than in pre-industrial or developing economies. The lecture will present models of the role of energy, and coal specifically, in economic growth and apply them to understanding the Industrial Revolution in Britain and Sweden, two countries with extensive historical data.

Lecture 5: Econometric Modelling of Global Climate Change
Economic growth has increased anthropogenic emissions of greenhouse gases and their concentration in the atmosphere leading to climate change. This means that greenhouse gases follow similar stochastic processes to macroeconomic variables, allowing us to apply the toolkit of time series econometrics to analyzing global climate change. However, though economic activity has immediate impacts on the climate, there is also a “tail” of much slower effects due the role of the ocean in storing heat and the slow processes of the carbon cycle and changing land-cover. The lecture will show how time series econometrics can be applied to understanding global climate change and estimating the impact of economic activity on the climate.

Data and Code for "Modeling the Emissions-Income Relationship Using Long-run Growth Rates"

I've posted on my website the data and code used in our paper "Modeling the Emissions-Income Relationship Using Long-run Growth Rates" that was recently published in Environment and Development Economics. The data is in .xls format and the econometrics code is in RATS. If you don't have RATS, I think it should be fairly easy to translate the commands into another package like Stata. If anything is unclear, please ask me. I managed to replicate all the regression results and standard errors in the paper but some of the diagnostic statistics are different. I think only once does that make a difference, and then it's in a positive way. I hope that providing this data and code will encourage people to use our approach to model the emissions-income relationship.

March Update

Just realized that we are already in the third month of the year and I haven't posted anything here yet! Things have been very busy with both work and family, so there hasn't been time to put out the blogposts only indirectly related to my research that I used to do - instead I'll usually tweet something on those topics - and research-wise things have either been at the relatively early research stages or the final publication stages. But there will soon be some new working papers going up and some blogposts here discussing them!

On the research front, in January we were mainly focused on putting the final touches on our climate change paper in time for the deadline for the special issue of the Journal of Econometrics. My coauthors want to wait for some feedback before posting a working paper on that. Then in February my collaborators Stephan Bruns and Alessio Moneta visited Canberra to work with me on modeling the economy-wide rebound effect as part of our ARC DP16 project. I spent the first half of the month working hard on the topic to prepare for their visit. We made good progress but it will be at least a few months till we have a paper on the topic ready. So far, it seems robust that the rebound effect is big. Then since they left, I've been catching up.

Recently, Paul Burke said: "You've already got three papers accepted this year - are you going to keep that pace up? ;)" He'd been keeping better count than me! Our original paper on the growth rates approach to modeling emissions and economic growth was accepted at Environment and Development Economics. Two related papers were also accepted - at Journal of Bioeconomics and Climatic Change. I also have three revise and resubmits to be working on... though one of those came in 2016... I'll put out one or two of those as working papers when we resubmit them.

Annual Review 2016

I've been doing these annual reviews since 2011. They're mainly an exercise for me to see what I accomplished and what I didn't in the previous year. The big change this year mentioned at the end of last year's review is that we had a baby in February. I ended up taking six weeks leave around the birth. Since then, I've been trying to adjust my work-life balance :) I'm trying to get more efficient at doing things, dropping things that aren't really necessary to do, trying to schedule work time more. None of these things are that easy, at least for me. It's mainly anything that isn't work, baby, or housework that gets squeezed out. I'm still director of the International and Development Economics program at Crawford. I will now be director for the next six months at least, after which I hope to pass this role on to someone new, but they haven't been identified as yet. During my time as director, we've made less progress on various initiatives than I would have liked due to internal ANU politics.

The highlights for the year were being elected a fellow of the Academy of the Social Sciences in Australia. I attended the annual ASSA symposium and other events in November where new fellows are welcomed. Also, our consortium was awarded a five year contract by the UK DFID to research energy for economic growth in Sub-Saharan Africa and South Asia. In particular, we are looking at how electrification can best enhance development. Also in November I attended the "Research and Matchmaking Conference" in Washington DC, where we presented the results of our first year of research and interacted with policymakers from developing countries and others. In the first year, the main activity has been writing 18 state of knowledge papers. I've have writing a paper with Stephan Bruns and Paul Burke on macroeconomic evidence for the effects of electrification on development.

Work got started on our ARC DP16 project. Zsuzsanna Csereklyei joined us at ANU as a research fellow working on the project. She is focusing on the technology diffusion theme. 

I published a record number of journal articles - in total, eight! Somehow a lot of things just happened to get published this year. It's easiest just to list them with links to the blogposts that discuss them:

Ma C. and D. I. Stern (2016) Long-run estimates of interfuel and interfactor elasticities, Resource and Energy Economics 46, 114-130. Working Paper Version | Blogpost

Bruns S. B. and D. I. Stern (2016) Research assessment using early citation information, Scientometrics 108, 917-935. Working Paper Version | Blogpost

Stern D. I. and D. Zha (2016) Economic growth and particulate pollution concentrations in China, Environmental Economics and Policy Studies 18, 327-338. Working Paper Version | Blogpost | Erratum

Lu Y. and D. I. Stern (2016) Substitutability and the cost of climate mitigation policy, Environmental and Resource Economics 64, 81-107. Working Paper Version | Blogpost

Sanchez L. F. and D. I. Stern (2016) Drivers of industrial and non-industrial greenhouse gas emissions, Ecological Economics 124, 17-24. Working Paper Version | Blogpost 1 | Blogpost 2

Costanza R., R. B. Howarth, I. Kubiszewski, S. Liu, C. Ma, G. Plumecocq, and D. I. Stern (2016) Influential publications in ecological economics revisited, Ecological Economics. Working Paper Version | Blogpost

Csereklyei Z., M. d. M. Rubio Varas, and D. I. Stern (2016) Energy and economic growth: The stylized facts, Energy Journal 37(2), 223-255. Working Paper Version | Blogpost

Halkos G. E., D. I. Stern, and N. G. Tzeremes (2016) Population, economic growth and regional environmental inefficiency: Evidence from U.S. states, Journal of Cleaner Production 112(5), 4288-4295. Blogpost

I also updated my article on economic growth and energy in the Elsevier Online Reference Materials. Citations shot past 11,000 on Google Scholar (h-index: 42) and will total more than 12,000 when all citations for this year are eventually collected by Google.

I have two papers currently under review (also two book chapters, see below). First, there is a survey paper on the environmental Kuznets curve, which I have now resubmitted to a special issue of the Journal of Bioeconomics that emerged from the workshop at Griffith University I attended last year. So, this should be published soon. Then there is our original paper on the growth rates approach to modeling the emissions-income relationship. I have resubmitted our paper on global particulate concentrations. We have a revise and resubmit for the paper on meta-Granger causality testing.

Some other projects are nearing completion. One is a new climate econometrics paper. Stephan Bruns presented our preliminary results at the Climate Econometrics Conference in Aarhus in October. I posted some excerpts from our literature review on this blog. We are also still wrapping up work on our paper on the British Industrial Revolution. Last year, I forecast we would soon have a working paper out on it. I'll have to make that forecast again! We also want to turn our state of knowledge paper for the EEG project into a publication. Of course, there is a lot more work at much earlier stages. For example, this week so far I've been working on a paper with Akshay Shanker on explaining why energy intensity has declined in countries such as the US over time. It's not as obvious as you might think! We've been working on this now and then for a couple of years, but now it looks much more like we will really complete the paper. I'm going to see if I can complete a draft in the next day or so of a paper following up from this blogpost. And, of course, there are the DP16 projects on energy efficiency and there are some long-term projects that I really want to return to and finish, but other things keep getting in the way.

My first PhD student here at Crawford, Alrick Campbell, submitted his PhD thesis in early December. It consists of four papers on energy issues in small island developing states (SIDS). The first of these looks at the effect of oil price shocks on economic growth in SIDS using a global vector autoregression model. He finds that oil price shocks have only small negative effects on most oil importing SIDS and positive effects, as expected, on oil exporting countries such as Bahrain or Trinidad and Tobago. These results are interesting as many of the former economies are fairly dependent on imported oil and would be expected to be susceptible to oil price shocks. The remaining papers estimate elasticities of demand for electricity for various sectors in Jamaica, look at the choice between revenue and price caps for the regulation of electric utilities, and benchmark the efficiency of SIDS electric utilities using a data envelopment analysis. My other student (I'm also on a couple of other PhD panels), Panittra Ninpanit, presented her thesis proposal seminar.

Because of the baby, I didn't travel as much this year as I have in previous years. I gave online keynote presentations at conferences in Paris and at Sussex University on energy and growth.  In September and October I visited Deakin U., Curtin U., UWA, and Swinburne U. to give seminars. Then in late October and early November I visited the US for a week to attend the EEG conference in Washington DC, mentioned above.

I only taught one course this year - Energy Economics. I got a reduction in teaching as compensation for being program director instead of receiving extra pay. As a result, I didn't teach in the first semester, which was when the baby arrived.

Total number of blogposts this year was slightly less last year, averaging three per month. As my Twitter followers increase in number - now over  500 - I find that readership of my blog is becoming very spiky with a hundreds of readers visiting after I make a post and tweet it and then falling back to a low background level of 20-30 visits per day. The most popular post this year was Corrections to the Global Temperature Record with about 650 reads.

Looking forward to 2017, it is easy to predict a few things that will happen that are already organized:

1. Alessio Moneta and Stephan Bruns will visit Canberra in late February/early March to work on the rebound effect component of the ARC DP16 project.
2. I will visit Brisbane for the AARES annual conference and Singapore for the IAEE international conference. I just submitted an abstract for the latter, but it's pretty likely I'll go, especially as there are now direct flights from Canberra to Singapore.
3. I will be the convener for Masters Research Essay in the first semester and again teach Energy Economics in the second semester.
4. I will publish two book chapters on the environmental Kuznets curve in the following collections: Oxford Research Encyclopedia of Environmental Economics and The Companion to Environmental Studies (Routledge).

In the realm of the less predictable, for the first time in five years I actually applied for a job. I had a Skype interview for it a two weeks ago. I wasn't really looking for a job but just saw an attractive advertisement that a former Crawford PhD student sent me. No idea if anything more will come of that...

Missing Coefficient in Environmental Economics and Policy Studies Paper

I don't like looking at my published papers because I hate finding mistakes. Today I saw that there is a missing coefficient in Table 2 of my recent paper with Donglan Zha "Economic growth and particulate pollution concentrations in China". In the column for Equation (2) for PM 2.5 the coefficient for the interaction between growth and the level of GDP per capita is missing. The table should look like this:

I checked my correspondence with the journal production team. They made lots of mistakes in rendering the tables and I went through more than one round of trying to get them to fix them. But the version I eventually OK-ed had this missing coefficient. At least the working paper version has the correct table.

The EKC in a Nutshell

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.

Dynamics of the Environmental Kuznets Curve

Just finished writing a survey of the environmental Kuznets curve (EKC) for the Oxford Research Encyclopedia of Environmental Economics. Though I updated all sections, of course, there is quite a bit of overlap with my previous reviews. But there is a mostly new review of empirical evidence reviewing the literature and presenting original graphs in the spirit of IPCC reports :) I came up with this new graph of the EKC for sulfur emissions:

The graph plots the growth rate from 1971 to 2005 of per capita sulfur emissions in the sample used in the Anjum et al. (2014) paper against GDP per capita in 1971. There is a correlation of -0.32 between the growth rates and initial log GDP per capita. This shows that emissions did tend to decline or grow more slowly in richer countries but the relationship is very weak -  only 10% of the variation in growth rates is explained by initial GDP per capita. Emissions grew in many wealthier countries and fell in many poorer ones, though GDP per capita also fell in a few of the poorest of those. 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 within countries over time. On the other hand, we shouldn't be restricted to a single explanation of the data and the EKC can be treated as one possible explanation as in Anjum et al. (2014). In that paper, we find that when we consider other explanations such as convergence the EKC effect is statistically significant but the turning point is out of sample - growth has less effect on emissions in richer countries but it still has a positive effect.

The graph below compares the growth rates of sulfur emissions with the initial level of emissions intensity. The negative correlation is much stronger here: -0.67 for the log of emissions intensity. This relationship is one of the key motivations for pursuing a convergence approach to modelling emissions. Note that the tight cluster of mostly European countries that cut emissions the most appears to have had both high income and high emissions intensity at the beginning of the period.

The Time Effect in the Growth Rates Approach

After a very long process our original paper on the growth rates approach was rejected by JEEM about a month ago. I think the referees struggled to see what it added to more conventional approaches. A new referee in the 2nd round hadn't even read the important Vollebergh et al. paper, so it's not surprising they missed how we were trying to build on that paper. That, discussion with my coauthor, Paul Burke, and preparing a guest lecture for CRWF8000 on the environmental Kuznets curve, got me thinking about a clearer way to present what we are trying to do. It really is true that teaching can improve your research!

Vollebergh et al. divide the total variation in emissions in environmental Kuznets curve models into time and income effects:

where G is GDP per capita, E is emissions per capita, and i indexes countries and t time. They point out that the standard fixed effects panel estimation of the EKC imposes very strong restrictions on the first term:

Each country has a constant "country effect" that doesn't vary over time while all countries share a common "time effect" that varies over time. They think that the latter is unreasonable. Their solution is to find pairs of similar countries and assume that just those two countries each share a common time effect.

In my paper on "Between estimates of the emissions-income elasticity" I solved this problem by allowing the time effect to take any arbitrary path in any country by simply not modeling the time effect at all and extracting it as a residual. The downside of the between estimator is that it is more vulnerable to omitted variables bias than other estimators.

We introduced the growth rates approach to deal with several issues in EKC models, one of them is this time effects problem. The growth rates approach partitions the variation in the growth rate of emissions like this:

where "hats" indicate proportional growth rates, and X is a vector of exogenous variables including the constant. The time effect is the expected emissions growth rate in each country when the economic growth is zero. This is a clear definition. The formulation allows us to model the time effect in each individual country i as a function of a set of country characteristics including the country's emissions intensity, legal origin, level of income, fossil fuel endowment etc. I don't think this is that clear in the papers I've written so far. We focused more on testing alternative emissions growth models and, in particular, comparing the EKC to the Green Solow and other convergence models.

So what do these time effects look like? Here are the time effects for the most general model for the CDIAC CO2 data plotted against GDP per capita in 1971:

Yes, I also computed standard errors for these, but it's a lot of hassle to do a chart with confidence intervals and a continuous variable on the X-axis in Excel.... There is a slight tendency for the time effect to decline with increased income but there is a big variation across countries at the same income level. And here are the results for SO2:

These are fairly similar, but more negative as would be expected. Clearly the time effects story is not a simple one and one that has largely been ignored in the EKC literature.

Economic Growth and Global Particulate Pollution Concentrations

I have just posted another working paper in the Trends and Drivers series, this time coauthored with recent Crawford masters student Jeremy van Dijk.

Particulate pollution, especially PM2.5, is thought to be the form of pollution with the most serious human health impacts. It is estimated that PM2.5 exposure causes 3.1 million deaths a year, globally, and any level above zero is deemed unsafe, i.e. there is no threshold above zero below which negative health effects do not occur. Black carbon is an important fraction of PM2.5 pollution that may contribute significantly to anthropogenic radiative forcing and, therefore, there may be significant co-benefits to reducing its concentration. In our paper, we use recently developed population-weighted estimates of national average concentrations of PM2.5 pollution that are available from the World Bank Development Indicators. These combine satellite and ground based observations.

Though the environmental Kuznets curve (EKC) was originally developed to model the ambient concentrations of pollutants, most subsequent applications focused on pollution emissions. Yet, previous research suggests that it is more likely that economic growth could eventually reduce the concentrations of local pollutants than emissions. We examine the role of income, convergence, and time related factors in explaining changes in PM2.5 pollution in a global panel of 158 countries between 1990 and 2010. We find that economic growth has positive but relatively small effects, time effects are also small but larger in wealthier and formerly centrally planned economies, and, for our main dataset, convergence effects are small and not statistically significant.

Crucially, when we control for other relevant variables, even for this particulate pollution concentration data there is no environmental Kuznets curve, if what we mean by that is that environmental impacts decline with increasing income once a given in sample level of income is passed - the turning point.

The following graph shows the relationship between the average growth rates over 20 years of particulate pollution concentrations and per capita GDP:

The two big circles are of course China and India where both GDP and particulate pollution grew strongly. We can see that there is a positive relationship between these two growth rates, especially when we focus on the larger countries. The main econometric estimate in the paper shows that a 1% increase in the rate of economic growth is associated with a 0.2% increase in the growth rate of particulate pollution. This is much weaker than the effects we found for emissions of carbon and sulfur dioxides. The estimated income turning point is $66k with a large standard error. On the other hand, when we estimate a model without the control variables, we obtain a turning point of only $3.3k with a standard error of only $1.2k. To check the robustness of this result, we estimate models with other data sets and time periods. These yield quite similar results.

We conclude that growth has smaller effects on the concentrations of particulate pollution than it does on emissions of carbon or sulfur. However, the EKC model does not appear to apply here either, casting further doubt on its general usefulness.

Economic Growth and Particulate Pollution Concentrations in China

A new working paper coauthored with Donglan Zha, who is visiting the Crawford School, which will be published in a special issue of Environmental Economics and Policy Studies. Our paper tries to explain recent changes in PM 2.5 and PM 10 particulate pollution in 50 Chinese cities using new measures of ambient air quality that the Chinese government has published only since the beginning of 2013. These data are not comparable to earlier official statistics and we believe are more reliable. We use our recently developed model that relates the rate of change of pollution to the growth of the economy and other factors as well as also estimating the traditional environmental Kuznets curve (EKC) model.

Though the environmental Kuznets curve (EKC) was originally developed to model the ambient concentrations of pollutants, most subsequent applications have focused on pollution emissions. Yet, it would seem more likely that economic growth could eventually reduce the concentrations of local pollutants than emissions. This is the first application of our new model to such concentration data.

The data show that there isn't much correlation between the growth rate of GDP between 2013 and 2014 and the growth rate of PM 2.5 pollution over the same period:

What is obvious is that pollution fell sharply from 2013 to 2014, as almost all the data points have negative pollution growth. We have to be really cautious in interpreting a two year sample. Subsequent events suggest that this trend did not continue in 2015.

In fact, the simple linear relationship between these variables is negative, though statistically insignificant. The traditional EKC model and its growth rate equivalent both have a U shape curve - the effect of growth is negative at lower income per capita levels and positive at high ones. But the (imprecisely estimated, so not statistically significant) turning point fro PM 2.5 is way out of sample at more than RMB 400k.* So, growth has a negative effect on pollution in the relevant range. When we add the initial levels of income per capita and pollution concentrations to the growth rates regression equation the turning point is in-sample and statistically significant. The initial level of pollution has a negative and highly statistically significant effect. So, there is "beta convergence" - cities with initially high pollution concentrations, reduced their level of pollution faster than cleaner cities did.

So what does all this mean? These results are very different than those we found for emissions of CO2, total GHGs, and sulfur dioxide. In all those cases, we found that growth had a positive and quite large effect on emissions. In some cases, the effect was close to 1:1. Of course, we should be cautious about interpreting this small Chinese data set. But our soon to be released research on global PM 2.5 concentrations, will again show that the effect of growth is smaller for these data than it is for the key pollution emissions data. This confirms early research that suggested that pollution concentrations turn down before emissions do, though it doesn't seem to support the traditional EKC interpretation of the data.

BTW, it is really important in this research to use the actual population of cities and not just the registered population (with hukou). If you divide the local GDP by the registered population you can get very inflated estimates of GDP per capita for cities like Shenzhen.

* The turning point is in-sample for PM 10.

Drivers of Industrial and Non-Industrial Greenhouse Gas Emissions to be Published in Ecological Economics

My paper with my former master's student Luis Sanchez has been accepted by Ecological Economics. This is one of the papers in the series using growth rates estimators of the income-emissions relationship that came out of my work on the IPCC 5th Assessment Report. This is the second paper I have published based on work done in our course: IDEC8011 Master's Research Essay. The previous one was with Jack Gregory who is now a PhD student at University of California, Davis. BTW, we previously submitted this paper to Nature Climate Change, Global Environmental Change, and Climatic Change in that order, with the first submission on 5 January 2015.

Between and Within

This will be obvious to anyone with a good understanding of econometrics, but it is quite stunning really to think that all the information you see in the first set of graphs in my previous post on the EKC is thrown away by fixed effects panel estimators. That is because the graphs plot the mean value over time in each country of the dependent variable against the mean value over time in each country of the explanatory variable. Fixed effects estimation first deducts these means from the data and then estimates the regression of the two residuals using ordinary least squares. This is why fixed effects is also called the "within estimator" because the "between (country) variation" you see in these graphs is ignored. Of course, you can estimate a model that just exploits this between variation using the between estimator.*

The reason the latter 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 their 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.

Differenced estimators sweep out any country fixed effects in the differencing operation.** So they also remove all the between variation in the data. However, they do allow us to include country characteristics that are constant over time to explain differences in growth rates across countries, which standard fixed effects does not allow.***

* The linked paper was eventually published in Ecological Economics.
** For a two period panel, fixed effects and first differences produce identical results.
*** There are variations of fixed effects that can allow this.

The Environmental Kuznets Curve after 25 Years

This year marks the 25th anniversary of the release of the working paper: "Environmental Impacts of a North American Free Trade Agreement" by Gene Grossman and Alan Krueger, which launched the environmental Kuznets curve industry. I have a new working paper out whose title capitalizes on this milestone. This is my contribution to the special issue of the Journal of Bioeconomics based on the workshop at Griffith University that I attended in October. It's a mix between a survey of the literature and a summary of my recent research with various coauthors on the topic.

Despite the pretty pictures of the EKC in many economics textbooks, there isn't a lot of evidence for an inverted U-shape curve when you look at a cross-section of global data:

Carbon emissions from energy use and cement production and sulfur dioxide emissions both seem to be monotonically increasing in income per capita. Greenhouse gas emissions from agriculture and land-clearing (AFOLU, lower left) or particulate concentrations (bottom right) just seem to be amorphous clouds. In fact, we do find an EKC with an in sample income turning point for PM 2.5 pollution, but only when we look at changes over time in individual countries. Interestingly, Grossman and Krueger originally applied the EKC to ambient concentrations of pollutants and it is there that it seems to work best.

The paper promotes our new "growth rates" approach to modeling emissions. Here are graphs of the growth rates of pollution and income per capita that exactly match the traditional EKC graphs above:



There is a general tendency for declining economies to have mostly declining pollution and vice versa, though this effect is strongest for energy-related carbon emissions. The graphs for sulfur and AFOLU GHG emissions are both shifted down by comparison. There is a general tendency unrelated to growth for these pollutants to decline over time - a negative "time effect". Growth has a positive effect though on all three. PM 2.5 (lower right) is a different story. Here economic growth eventually brings down pollution. We don't find a significant negative time effect.

I first got interested in the EKC in November 1993 when I was sitting in Mick Common's office at the University of York where I'd recently started as a post-doc (though I was still working on my PhD). He literally drew the EKC on the back of an envelope and asked whether more growth would really improve the environment even if the EKC was true. I did the basic analysis really quickly but then it took us another couple of years to get the paper published in World Development.

Business as Usual Emissions Projection from Sanchez and Stern Econometric Model

I finished preparing my presentation for Thursday in Brisbane. The topic of my talk is "Drivers of Industrial and Non-Industrial Greenhouse Gas Emissions". It's mostly based on my paper with Luis Sanchez. I'm also adding some material from Chapter 5 of the IPCC AR5 report (WG3) to give context. This is because this "trends and drivers" research theme came out of my work on the IPCC chapter. Reyer Gerlagh produced our original "iconic image" (yes, we called them that in the IPCC process) of the long-run growth rates of emissions and income per capita and then I suggested to do an econometric analysis along those lines. I think it was Reyer also who suggested how to model the EKC in a growth rates model.

I've also "added some value" by doing a business as usual projection using our model. This is something we are thinking to do as part of the revise and resubmit for a related paper. The graph shows projections to 2030 for 3 developing and 3 developed countries and the world (well, our 129 country sample) as a whole:

Indonesia and India have similar income per capita, so an EKC model would project similar emissions growth in both countries. The graph shows the value added of our model, which suggests that emissions will grow slower in Indonesia, which is more emissions intensive. The global outcome is similar (a little bit higher) to RCP 8.5, which is the highest emissions growth scenario used in AR5. RCP 8.5 assumes slower economic growth than we are here but slower than historic progress in energy intensity.

To get the projection, I used our model parameters estimated for the 1991-2010 period and the UN median projection for population growth. I used USDA ERS projections for economic growth rates in each country. Other variables are at their values for 2010.

Population, Economic Growth and Regional Environmental Inefficiency: Evidence from U.S. States

I have a new paper in the Journal of Cleaner Production coauthored with George Halkos and Nikalaos Tzeremes. George was a lecturer at University of York when I was a post-doc there. We haven't previously put out a working paper version of this paper.

In this paper, we apply a conditional directional distance function allowing multiple exogenous factors to measure environmental performance to evaluating the air pollution performance levels of U.S. states for the years 1998 and 2008. The overall results reveal that there is much variation in environmental inefficiencies among the U.S. states. A second stage nonparametric analysis indicates a nonlinear relationship between states’ population size, GDP per capita levels and states’ environmental inefficiency levels.

Our results indicate that environmental inefficiency on the whole decreases with increased population and income per capita but there are limits to this improvement and at high income and population levels the tendency may reverse. In particular, small poor states tend to be environmentally inefficient, whereas large states tend to be more efficient regardless of their level of income. The results show that there is not so much of a trade off between environmental quality and economic development in small and poor US states in the South and Mid-West. As these states grow in income and population they can improve their environmental efficiency. However, large and richer states face more environmental challenges from growth. This may explain the differences in policy across states. For example, California which is already an environmentally efficient state is also a state which has lead in environmental regulation. There are fewer local environmental policies in states across the South and parts of the mid-West. Politicians and populations in these states may see less trade off between environmental quality and development and hence be reluctant to adopt specific environmental policies. These patterns also match recent trends in voting for the Republican and Democratic parties the so-called Blue and Red States. However, there are exceptions to a simplistic analysis along these lines as Texas for example is an environmentally efficient state in our analysis as would be expected from its large population size.

Two Papers Accepted for Publication

Two of our papers have just been accepted for publication. One is my paper with Yingying Lu on sensitivity analysis of climate policy computable general equilibrium models. It has been accepted for publication in Environmental and Resource Economics. The other is a paper with George Halkos and Nikalaos Tzeremes. The paper is on environmental efficiency across the U.S. States and has been accepted by the Journal of Cleaner Production. We haven't put out a working paper version of this one. I'll do a blogpost on it when it is available online at the journal. George was a lecturer at University of York when I was a post-doc there.

In the case of the first paper, we only sent it to one journal (JEEM) before the one it was finally published in. We sent the second paper to quite a few journals but managed to get it into one with a pretty high impact factor after significant revision.

Drivers of Industrial and Non-Industrial Greenhouse Gas Emissions

Another new working paper this time coauthored with my masters student Luis Sanchez. We use the new approach to modeling the income-emissions relationship pioneered by Anjum et al but using total greenhouse gas emission rather than just CO2 emissions from fossil fuel combustion and cement production. This is closer to the discussion I wrote in Chapter 5 of the Working Group III IPCC Report. Anjum et al. used the more limited emissions variable because the IPCC wouldn't allow us to use the data assembled for the report in other research and it took a lot of effort on Luis' part to put the data together from the raw Edgar data. Also, economists are more familiar with the narrow industrial CO2 emissions variable and so we thought we'd do an analysis of that first.

There has been extensive analysis of the drivers of carbon dioxide emissions from fossil fuel combustion and cement production, but these only constituted  55% of global greenhouse gas (GHG) emissions (weighted by global warming potential) in 1970 and 65% in 2010. There has been much less analysis of the drivers of greenhouse gases in general and especially of emissions of greenhouse gases from agriculture, forestry, and other land uses, which we call non-industrial emissions in the paper, that constituted 24% of total emissions in 2010.

The graphs show that non-industrial emissions have a different relationship to income than do industrial emissions. However, there is still a positive relationship between the growth rates of the two variables, especially when we give more weight to larger countries as we do in the paper. Increases in the economic growth rate have about half the effect on non-industrial emissions than they have on industrial emissions.

In both of these graphs China is the large circle on the right. The country with highest non-industrial emissions is Indonesia, which is the largish circle above and to the right of China in the second graph.

We econometrically analyze the relationship between both industrial and non-industrial greenhouse gas emissions and economic growth and other potential drivers for 129 countries over the period from 1971 to 2010. As in Anjum et al., our method combines the three main approaches in the literature to investigating the evolution of emissions and income. We find that economic growth is a driver of both industrial and non-industrial emissions, though growth has twice the effect on industrial emissions. Both sources of emissions decline over time though this effect is larger for non-industrial emissions. There is also convergence in emissions intensity for both types of emissions but given these other effects there is again no evidence for an environmental Kuznets curve.

Seminar at University of Kassel

I promised more details on my seminar at University of Kassel: here they are. I will present our paper on modeling the emissions income relationship using long-run growth rates. My presentation will be at 2pm on 18 November in Sitzungsraum K33/FB07. If you need more details about location, ask Stephan Bruns who is organizing the "Empirical Workshop on Energy, Environment, and Climate Change" of which this talk is part. The workshop starts at 10 am and there will also be presentations by Heike Wetzel, Andreas Ziegler, Astrid Dannenberg, and Stephan.