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.

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.

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.

Managing the Transition to a Sustainable Economy

On Thursday morning at 9:35am I'll be presenting at the Managing the Transition to a Sustainable Economy Conference at Griffith University in Brisbane. I'll be presenting my paper on Drivers of Industrial and Non-Industrial Greenhouse Gas Emissions. This is a slight change from the original paper I was supposed to present, as that got published in the meantime, and the organizers only want unpublished research. I blogged about the paper in March. Right after me, John Foster is speaking.  John Gowdy from my former university RPI is also speaking at the conference. The full schedule is here.

Global Energy Use: Decoupling or Convergence Accepted to be Published in Energy Economics

My paper with Zsuzsanna Csereklyei on trends in global energy use and whether they can be better explained by decoupling or convergence in energy intensity or some mixture has been accepted to be published in Energy Economics. I wrote a blogpost about the paper in December last year when we first completed the paper.

This was not a bad review experience though Zsuzsanna who is in a short-term post-doc position would have liked it to be faster! We first sent the paper to World Development who desk-rejected it. Then we sent it to Energy Economics. The first review took seven months due to one of the referees dropping out due to family health problems and then the journal needed to get another referee. We turned around the paper in 12 days and then got a conditional acceptance a month later. We turned that around in one day and got the final acceptance a day later. This paper applies the method developed in Anjum et al. (2014) to energy and also adds treatment of spatial autocorrelation. The whole process of writing and publishing this paper took place while Anjum et al. has been in review...

Update 5 October

The journal also got the final version of the paper with page numbers onto the web incredibly quickly following us returning the final proofs.

Update

Haven't blogged for over a month. Partly this is because I am doing more tweeting and also because I have been busy with the end of semester and traveling to Turkey (IAEE conference), Israel, and Abu Dhabi (International Energy Workshop). This was my first time attending the IEW. I think there is good feedback in the parallel sessions - better than at the IAEE meeting. On the other hand, the IAEE plenaries are more consistent, I think. IEW is strongly tied to the ETSAP modeling forum, which precedes it - TIMES/MARKAL models - and is attended by "modelers" rather than the mix of business, government, and academic communities at IAEE.

In other news, our paper on the behavior of carbon dioxide emissions in the short-run has been published in Global Environmental Change. The article is open access until 26 July. Also our survey paper in the Review of Economics is also open access.

Carbon dioxide emissions in the short run: The rate and sources of economic growth matter

Another paper in our ""trends and drivers" series that emerged from my IPCC work. We already released a paper on total greenhouse gas emissions in the long run that I coauthored with Luis Sanchez and a more methodological paper with Reyer Gerlagh, Paul Burke, and Zeba Anjum on fossil fuel CO2 and SO2 emissions in the long run.

The new paper coauthored with Paul Burke and Md. Shahiduzzaman focuses on what happens to fossil fuel carbon dioxide emissions over the business cycle time frame. This topic has received a bit of attention recently. We looked at the issue of what happened after the 2008-9 "Great Recession" in a short 2012 paper in Nature Climate Change that followed up on a paper by Glen Peters and others. Emissions grew very strongly in the recovery in 2010. Our new research shows that 2010 was an unusual year and usually emissions do not rise strongly in the recovery from a recession. In another 2012 paper in Nature Climate Change,, Richard York reported that the response of emissions to expansions and recessions is asymmetric. When the economy is growing the elasticity of emissions with respect to GDP is greater than when it is declining. Our paper tests York’s results and finds that asymmetry is only statistically significant when expansions and recessions of several years in length are considered.

That Gross World Product and CO2 emissions growth rates are tightly linked can be easily seen in the following graph. But there are many details to the story. For example, using country-level data we find that lagged effects are important: around 40% of the effect of economic growth on emissions isn’t realized until a subsequent year.


Some of the paper’s results are summarized in the graph below. We find that the average same-year emissions-income elasticity is about 0.5, but that this elasticity varies depending on the source of economic growth. Agricultural growth has relatively small emissions effects, whereas industrial growth is relatively emissions intensive. External shocks from export markets have quite large domestic emissions implications, presumably because they mostly affect industrial output.



Full abstract: This paper investigates the short-run effects of economic growth on carbon dioxide emissions from the combustion of fossil fuels and the manufacture of cement for 189 countries over the period 1961–2010. Contrary to what has previously been reported, we conclude that there is no strong evidence that the emissions-income elasticity is larger during individual years of economic expansion as compared to recession. Significant evidence of asymmetry emerges when effects over longer periods are considered. We find that economic growth tends to increase emissions not only in the same year, but also in subsequent years. Delayed effects – especially noticeable in the road transport sector – mean that emissions tend to grow more quickly after booms and more slowly after recessions. Emissions are more sensitive to fluctuations in industrial value-added than agricultural value-added, with services being an intermediate case. On the expenditure side, growth in consumption and in investment have similar implications for national emissions. External shocks have a relatively large emissions impact, and the short-run emissions-income elasticity does not appear to decline as incomes increase. Economic growth and emissions have been more tightly linked in fossil-fuel rich countries.

P.S. 4 May 2015
The paper was accepted for publication in Global Environmental Change. Yeah, we posted the working paper when we resubmitted the paper to the journal. Still, overall that was a very fast publication experience with the first journal we submitted to (on 18 December 2014) accepting the paper. This isn't always the case :)

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.

Australian Energy Intensity Decomposition

This post is based on part of a presentation that I gave yesterday in a short course here in at ANU. Australia has improved its energy intensity over recent decades but the gains have been quite small. My research suggests that adjusted for industrial structure etc. Australia is relatively energy inefficient compared to other developing countries. The following graph removes all these other factors, resulting in a measure of "pure" energy efficiency:

A declining trend implies increasing energy efficiency. I advised Muhammad Shahiduzzaman on his PhD research at the University of Southern Queensland. Subsequently, he published a paper in Energy Policy on decomposing Australian energy intensity. The following graph from the paper shows the effects of structural change at two levels of aggregation on Australian energy intensity:

The shift between the larger sectors helped reduce energy intensity but the shifts between smaller categories of industries tended to increase energy intensity. Real intensity is the residual energy intensity once the structural effects are removed. The Australian Bureau of Resource and Energy Economics has also carried out research on energy intensity trends and decomposition. The following graph shows the decomposition for the transport sector in Petajoules:

The activity effect here is the energy use increase due to increased passenger and tonne kilometres for passenger and freight transport respectively. Structural change - shifts between road, rail, air etc. contributed very little. There was a substantial efficiency improvement but it was outweighed by the large increase in activity resulting in a substantial increase in energy use in the sector. The final graph in this post, shows the decomposition for the residential sector in Australia:

In this decomposition, the activity effect is the increase in energy use due to increased  population. The BREE researchers defined the structural effect as due to larger houses, smaller households etc. I think it is debateable as to whether this should be considered structural change rather than change in the level of activity. In any case the efficiency improvement is of the same order as either the activity or structural effect and so energy use increased here too.

Modeling the Emissions-Income Relationship Using Long-Run Growth Rates

We have a working paper out on a new way of modelling the relationship between emissions and GDP per capita, a literature that has been dominated for more than two decades by the environmental Kuznets curve (EKC) approach. I presented an early version of this paper at the AARES conference at Port Macquarie in February. I will also be presenting it at the 6th Atlantic Workshop in A Toxa, Spain in late June and then at the World Congress of Environmental and Resource Economics in Istanbul a few days later.

The paper emerged from our work on Chapter 5 of the recently released Working Group III volume of the IPCC 5th Assessment Report. Reyer Gerlagh, who was one of the coordinating lead authors on my chapter drew up a version of the following graph and asked me if it would be suitable for the section I was writing on economic growth and emissions:

 

I liked this graph so much that I said we should write a paper about it, which we have now completed. Rather than compare the levels of emissions and GDP per capita as is usually done in the EKC literature, the graph compares the average growth rates of these two variables over a 40 year period (1971-2010). We can see that faster economic growth is associated with faster carbon emissions growth but that there is also a lot of variation around this main trend in the data. The further "southeast" a bubble is, the faster emissions per dollar of GDP (emissions intensity) declined in that country. As you can see China (the big red circle) and the US the big blue circle both had rapid declines in emissions intensity. But emissions intensity also rose in many countries and it is not immediately obvious how it relates to development status.*

One of the nice things about using growth rates rather than levels of variables is that it avoids several econometric problems that have plagued this literature. First and foremost is the issue of unit roots and non-linear functions of unit roots raised by Martin Wagner. Differencing the variables removes that issue, but using long-run growth rates focuses attention on long-run behavior, whereas using first differences would focus on the short-run. Then there is the issue of time effects raised by Vollebergh et al. We think our approach does a good job there too. The constant in a regression of emissions growth rates on income growth rates represents the rate of emissions growth if there were no economic growth. We think this is a good definition of a time effect. The paper discusses further econometric issues.

The other nice thing about using growth rates is that we can test the three main leading approaches to modelling the emissions-income relationship in a single framework:

In this equation all variables are in logs and "hats" (or more elegantly circumflex accents) indicate growth rates. On the lefthand side is the emissions per capita growth rate. As mentioned above the constant, alpha, represents the time effect. G-hat is the growth rate of GDP capita. The estimate of beta(1), therefore, tests the IPAT theory that growth causes increases in impacts. The term beta(2)*G(i) tests the EKC theory. This is because if beta(2) is negative then beyond a certain income level (the "turning point") more growth reduces emissions rather than increases emissions. The other main approach to modelling emissions growth has been the convergence approach, including the Green Solow Model of Brock and Taylor. We test this with the fourth term in the regression, which is the level of emissions intensity in the first year of the sample. If delta is negative, then countries with high initial emissions intensities saw more rapid decline in emissions. We also test for any effect of the level of GDP (gamma*G(i)) and for various other exogenous variables including fossil fuel endowments, legal origin, and climate.

It turns out that for both carbon and sulfur dioxide the effect of growth is very significant and close to a one to one effect. For sulfur there is a significant time effect - emissions fell by about 1.2% a year for a typical country when there was no economic growth. The convergence effect is also highly significant and probably explains a lot of the reduction in emissions intensity in both China and the US. But there is no environmental Kuznets curve effect in the full sample estimates.** While there is a marginally significant coefficient for one dataset, all the turning points are far out of sample and insignificant.

The environmental Kuznets curve has become so iconic that it often appears in introductory environmental economics textbooks. It probably is valid as a stylized fact for urban air pollution concentrations but it's not a good model of emissions of either carbon dioxide or sulfur emissions. We're hoping that the figure of the growth effect above and this one of emissions convergence:

might replace it.

* The blue circles are the developed countries that were members of the OECD in 1990. Orange is "economies in transition"  - Eastern Europe and the former Soviet Union. The other colors are the developing regions in Asia, Latin America, and the Middle East and North Africa.

** When we split the sample into two periods we find a very significant coefficient for sulfur in the second period, but the turning point is at $38k and is not statistically significant.

Regional Kaya Identities

As well as the global Kaya identity, Chapter 5 of the WGIII IPCC report also includes regional Kaya identity graphs:

OECD-90 are the countries that were members of the OECD in 1990 and are considered to be the "developed countries" for the purpose of this study. Economies in Transition are the former Soviet Union and formerly centrally planned Eastern European economies. The remaining countries are the developing world and are split into three geographic regions.

We drew the graph for each region with the same y-axis scale so that the huge differences in growth rates between regions would be more apparent. GDP per capita grew by far more in (developing) Asia than anywhere else and grew least in the Middle East and North Africa. But emissions grew the second most in the latter region mainly because population grew fastest in that region but also because, unlike other regions, energy intensity rose over time. Emissions growth was also quite strong in Latin America because energy intensity did not decline much there. Both these regions had low energy intensity at the beginning of the period. The global pattern in changes in energy intensity reflects convergence across countries in energy intensity.

The Global Kaya Identity

Another post on our chapter in the recently released Working Group III IPCC report, Chapter 5. I previously posted on what got left out of the final edition of the Summary for Policymakers and on the key messages from the whole report.

A key feature of our chapter is that we organized it around the idea of the Kaya Identity, which is an extension of the famous IPAT identity to explain changes in carbon emissions:

or in terms of formulae:

Sections of our chapter deal with each of the terms in the identity. It's important to understand that the identity is not really a causal relationship. A 1% increase in GDP per capita might be associated with less than a 1% increase in carbon emissions if energy intensity for example declines as a result of the increase in income. This seems to be the case in fact as our upcoming research will show. Still, it is a useful accounting framework for understanding the factors driving change. If the other terms in the identity are held constant then a 1% increase in any of the right hand side factors increases emissions by 1%.

In the technical summary we use the Kaya identity to decompose the changes in global energy related carbon dioxide emissions for each of the last four decades:

 

The bars show that reductions in energy intensity have contributed to the slowing in growth in carbon emissions. However, this has been overwhelmed by the increase in population and income per capita. This exercise made me much more aware of how important population growth has been in driving emissions growth over the last 40 years. In the most recent decade, though, income per capita growth became the most important factor and emissions growth accelerated to a record level.

New Article in The Conversation

Michael Hopkins, an editor at The Conversation, suggested I follow up my blogpost on the IPCC 'censorship' controversy with a piece in The Conversation. That article is out today. The censorship story has also been picked up on by Science magazine.

If you are wondering whether to contribute to sites like The Conversation, I think it is well worth it, though it is a bit more work than I usually put into a blogpost. My blogpost last Thursday on the censorship issue has received 210 hits so far according to Google. People who just navigated to my home page rather than selecting the article title aren't counted, but probably don't amount to a huge number of additional views. Our 13th April article in The Conversation has got 4720 hits so far and my previous two articles there have gotten 1681 and 1859 hits each. Three of my blogposts have exceeded those numbers (11-13000 hits) and they are all about PLoS ONE and impact factors. My next best blogpost is this one with 1666 hits. Typical numbers are in the low hundreds if I'm lucky. So, I can get much more reach on a site like The Conversation than a typical post on my blog about my own research achieves. Of course, not all stories are going to be suitable for sites like these, but it's worth thinking about what might be suitable.

Chapter 5 and the Summary for Policy Makers

Chapter 5 was one of the main chapters of the Working Group III 5th Assessment Report at the centre of the controversy this week on so-called censorship of the Summary for Policy Makers (SPM). The SPM is an executive summary of the report for the IPCC member governments. Those member governments get to dictate what points from the underlying report get included in this summary and how they are "spun". However, there is also a Technical Summary that is written entirely by the researchers responsible for the main report. The material from Chapter 5 that was in the draft SPM but eliminated in the plenary meeting in Berlin referred to emissions from specific groups of countries. This blogpost provides a quick overview of the deleted figures, some of which are still in  the Technical Summary.

The first graph breaks down emissions by broad global regions:

 

The developed countries are represented by the members of the OECD as it stood in 1990 (since then Mexico, Korea, Czech Republic etc. have joined). Eastern Europe and the former Soviet Union are designated "Economies in Transition" and the developing world is broken down into Asia (importantly including China and India), Latin America, and the Middle East and Africa. The left-hand panel shows emissions year by year since the Industrial Revolution and also breaks them down into energy and industrial and land use related emissions. The former continue to increase but the latter appear to have peaked. Since the 1970s, the majority of growth in energy and industrial emissions has come from developing countries and particularly Asia. In an attempt to better represent the historical responsibilities of each group of countries the right-hand panel shows the cumulative historical emissions of greenhouse gases by region.* China and particularly India have campaigned to get historical contributions to global warming better-acknowledged. But the results of our analysis show that less than half of the cumulative emissions now come from the developed countries as a whole (more when only energy and industrial emissions are considered). This, presumably, isn't the message that developing country delegates wanted to see.

The next controversial figure breaks down total and per capita greenhouse gas emissions by country income groups:

The leftmost panel shows total emissions which increased everywhere due to population growth. But they particularly increased in upper middle income countries (which includes China). The total emissions from this group are now almost equal to that from the high income countries. On a per capita basis, emissions were flat in the developed world and declining in the poorest countries (as emissions from land use declined). They rose in the middle income countries. The figure does, however, also show that in all developing country groups per capita emissions remain much below those in the developed countries.

The final deleted figure deals with emissions embodied in trade:

Looking at the emissions generated in producing imports and exports, the developed countries and economies in transition ("Annex B") import more "embodied" emissions than they export. The opposite is true of the developing countries ("Non Annex B"). Emissions that include the net emissions embodied in trade are termed "consumption emissions" in contrast to the "production emissions" that are the total emissions emitted within a country and are the usual way of calculating emissions.** These numbers are derived using input-output modelling. The results are often used to argue that developed countries have reduced their emissions by offshoring production to developing countries, which is a controversial question. But properly answering this question is more complicated than this. They are also used to claim that developed countries are responsible for their consumption emissions rather than their production emissions. But both importers and exporters gain from this trade. Because of these controversies I can understand the decision to drop the discussion and figure from the SPM.

* These do not directly correspond to the amounts of gases in the atmosphere. A large fraction of annual carbon dioxide emissions are absorbed by the ocean, vegetation etc. and methane only survives for an average of 11 years in the atmosphere before being oxidised to carbon dioxide and water. So, I am not very enthusiastic about treating cumulative emissions of carbon dioxide equivalent greenhouse gases as an indicator of historical responsibility.

** Economists would usually use the term "production emissions" to refer to emissions from production activities  and "consumption emissions" to refer to emissions by consumers. This initially caused some communication problems among researchers from different disciplines in our chapter team.

The Economics of Global Climate Change: A Historical Literature Review

I have a new working paper coauthored with Frank Jotzo and Leo Dobes up on RePEc titled: The Economics of Global Climate Change: A Historical Literature Review. It is a by-product of a book of collected papers we edited for Edward Elgar to be titled Climate Change and the World Economy. The paper has three sections. The first is on trends and drivers of emissions, the second on mitigation and impacts, and the third on adaptation. I wrote the first section, Frank wrote the second and Leo wrote the third. I then edited all the sections together into a hopefully coherent whole. The paper is titled "A Historical Literature Review" because we focus to some degree on the evolution of the literature from some of the early classic papers to the latest contributions. Of course, there is no way we can write a review that is at all comprehensive. The IPCC reports struggle to do that. I think we do cover some of the key papers in the literature and it could be a useful reading guide for further research.

Other Emissions of Greenhouse Gases and Aerosols

I only cover three other types of emissions besides energy related CO2. I thought of including black carbon but in the end decided to skip it as I already have too many papers. I resisted the temptation to try to include two of my papers in the collection, though I ended up discussing my paper more below :) I also include a graphic that will not be appearing in our book. It is from Smith et al. (2011) and compares the various estimates of sulfur emissions.

Deforestation and land-use change is an important source of emissions of CO2. Levels of emissions are much lower than from energy related sources, more stable over time, but also very uncertain. Houghton (2003) presents estimates of CO2 emissions from land-use change from 1850 to 2000, globally and by region. In general the tend rises from 1 to 2 Gt C over the 150 years with an acceleration in the trend around 1950 in common with emissions from energy related sources. Therefore, there is a clear link with economic growth. Tropical deforestation, particularly in Asia and Latin America dominates. In recent decades there is net reforestation in developed countries. Unusually, the data are increasingly uncertain in recent decades with estimates from different researchers varying substantially (Houghton, 2010).

The third most important greenhouse gas in the atmosphere and the second most important anthropogenic source is methane. Relatively little work has been done on CH4 in comparison to CO2. Stern and Kaufmann (1996) used available data to reconstruct the first time series of historic emissions from 1860-1993. They found that anthropogenic emissions had increased from 80 million tonnes of carbon in 1860 to 380 million in 1990. The relative importance of the various emissions sources changed over time though rice farming and livestock husbandry remained the two most important sources.

Offsetting the radiative forcing due to greenhouse gases is a significant negative forcing due to aerosols derived from sulphur oxide (primarily dioxide) emissions. These aerosols do not persist in the atmosphere for usually more than a few days and so the source of emissions is important and effects are localized though they spread far beyond the sources to affect neighbouring countries. The main sources of anthropogenic sulphur emissions are the combustion of coal and metal smelting. Stern (2006) showed that that after increasing fairly steadily from 1850 to the early 1990s global emissions began to trend downwards. Emissions in Western Europe and North America as well as Japan had already been trending down since 1970 primarily due to policies to reduce acid rain (Stern, 2005). But this decline was offset by growth in other regions. Following 1990, there was a dramatic reduction in emissions from Eastern Europe and the former Soviet Union. The likelihood that emissions will continue to decline in the future will contribute to future warming. Whereas Stern (2006) uses a combination of previously published data and model estimates, Smith et al. (2011) provide an inventory of sulphur emissions from 1850 to 2005 using a uniform methodology. The results largely confirm Stern’s (2006) findings though the levels are generally lower by a few percent.

References

Houghton, R. A. (2003) Revised estimates of the annual net flux of carbon to the atmosphere from changes in land use and land management 1850-2000, Tellus 55B: 378-390.

Houghton, R. A. (2010) How well do we know the flux of CO2 from land use change? Tellus 62B: 337-351.

Smith, S. J., J. van Ardenne, Z. Klimont, R. J. Andres, A. Volke, S. D. Arias (2011) Anthropogenic sulfur dioxide emissions: 1850-2005, Atmospheric Chemistry and Physics 11: 1101-1116.

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. (2006) Reversal in the trend of global anthropogenic sulfur emissions, Global Environmental Change 16(2), 207-220.

Stern D. I. and R. K. Kaufmann (1996) Estimates of global anthropogenic methane emissions 1860-1993, Chemosphere 33, 159-176.