Thursday, May 29, 2014

Elsevier Article Usage Dashboards and Tips for Early Career Researchers

The new article usage dashboards are a nice feature of Elsevier journals. Here is the dashboard for my 2013 article in Energy Economics:

It's interesting to see where our paper is being downloaded. China is in the lead. Obviously there are a lot of downloads in Sweden and Australia as the authors are Swedish and Australian and the data is Swedish. Also, there are a lot of downloads in Turkey, which has a lot of researchers in energy economics.

There is also a link in the Dashboard to a page with lots of useful guides for early career researchers. Some of the advice will also be useful for more senior researchers looking to get greater impact from their publications.

Reforming IPCC Communications

The IPCC is currently discussing whether to change its approach to publications and communications in future years. We (Frank Jotzo and I) were asked about our opinions as former lead authors by the people in the relevant government departments here. It seems to make sense to have more of a continuous reporting model given the capability of the web. Perhaps each chapter could be updated one at a time on an ongoing cycle or even a Wikipedia style format could be adopted. On the other hand, the large septennial assessment reports do generate a big media splash for a few days when they are released, which the continuous communication format would not. If assessment reports are continued, then maybe they should all be released simultaneously, or each Working Group release its report 2 years apart. The 5th assessment report cycle saw WG1 release its report in September 2013 and then WG2 and WG3 released their reports two weeks apart in April 2013, which was somewhat confusing. We also thought that the IPCC plenary should be less prescriptive about the contents of each chapter including sub-sections and what should be in each subsection.

COIN in the UK have released a short report with some more radical suggestions. In particular, they think the IPCC should do human interest stories that are more suitable for most media outlets and use social media more effectively.

Wednesday, May 28, 2014

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.

Tuesday, May 27, 2014

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.


Saturday, May 24, 2014

Is Piketty's Work Flawed by Computational Errors Too?

The Financial Times claims that, like the work by Reinhart and Rogoff on debt and growth, Piketty's research on changes in inequality over time is also beset by computational errors. Though the theoretical component of Piketty's book has been controversial but as the article says, the critics have still praised the historical research. The biggest differences seem to be in estimates of wealth inequality in Britain in recent decades. The data the FT correspondent provides shows no increase in concentration in wealth in the top 1% and top 10% in the UK in recent decades. That would weaken Piketty's conclusions overall, but it doesn't seem it destroys them. Piketty's response is here.

Friday, May 23, 2014

Unionization in Australian Universities


After seeing that Alison Booth's paper from the Quarterly Journal of Economics was the most downloaded paper from ResearchGate at Crawford this week, I was curious what fraction of employees at Australian universities belonged to the National Tertiary Education Union. Apparently NTEU has 26,000 members. It also seems that there are 113,000 employees in the university sector. On that basis the unionization rate would be only 23%. Of course, quite a lot of those are casuals or PhD students working as lecturer A etc.* But the total number of full-time staff is 86,000. That implies 30%. Also there were 67,933 staff on continuing contracts. If the latter is the real target market for the union then the rate is 38%. Based on this, social custom doesn't work well in the university sector to overcoming free-riding. Let me know if any of my assumptions are wrong as this is the first time I've ever looked at this issue.

* There were 41,730 academics at levels B and above, but the union also represents non-academic staff.

Thursday, May 22, 2014

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.

Tuesday, May 20, 2014

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.

13 Years On Our Book Becomes an E-Book!

The book was published in 2001 but the conference it is based on was held in Boston in 1996! Anyway, the e-book is now online and the introductory chapter is open access.

Thursday, May 1, 2014

Submissions to AJARE Way Up, Acceptance Rate Way Down

Really interesting data from the editors of the Australian Journal of Agricultural and Resource Economics. It's unusual to see time series stretching back 15 years of submissions, acceptances, articles published etc. for a journal. The page budget today is slightly higher than in 1999 but less than what it was in 2000. The number of articles published though actually doubled. But submissions tripled. In the last year half of new submissions have been desk rejected.