Stochastic Trend: DP16

Showing posts with label DP16. Show all posts

Tuesday, October 12, 2021

How Large is the Economy-Wide Rebound Effect in Middle Income Countries? Evidence from Iran

We have a new working paper in our rebound effect series. Previous papers reviewed the literature on the economy-wide rebound effect, estimated the economy-wide rebound effect for the United States, and estimated it for some European countries (as well as the United States). The new paper is about Iran. This is a middle income country with a resource intensive and quite regulated economy. Is it a lot different to the developed economies we have already looked at?

The rebound effect is large in Iran too. A major difference between Iran and the developed economies is that energy intensity has been rising in Iran:

Total energy use tripled from 1988 to 2017, which is the sample period used in our econometric analysis (quarterly data):

The econometric model is the same as that used in the US paper that is now published in Energy Economics, except we only use the distance covariance method for the independent component analysis in this paper. The next figure shows the estimated impulse response functions of energy, GDP, and the price of energy to energy efficiency, GDP, and price shocks:

The top left panel shows the rebound effect. Initially, there is a large drop in energy use, but this diminishes over time. We estimate that the rebound is 84% after six years. The confidence interval is wide and includes 100%.

On the other hand, the GDP shock has large positive effects on energy (top middle panel) and GDP (middle). These are similar in size. By contrast, in the US, the effect on energy is much smaller than on GDP. This seems to be "why" energy intensity falls in the US but rises in Iran.

In this paper we also conduct a forecast error variance decomposition:

This shows how much each of the shocks explain each of the variables at different time horizons. Energy efficiency shocks explain most of the forecast error variance in the first few quarters after a shock. But over time, the GDP shock comes to explain most of the forecast error variance. This is why I argue that the relative GDP shocks are what drives energy intensity.

The paper is coauthored with Mahboubeh Jafari at Shiraz University and Stephan Bruns at University of Hasselt.

Thursday, June 3, 2021

Do Energy Efficiency Improvements Reduce Energy Use? Empirical Evidence on the Economy-Wide Rebound Effect in Europe and the United States

We have just posted a new working paper on RePEc and SSRN extending our structural vector autoregression methodology for estimating the economy-wide rebound effect and applying it to several European countries as well as the United States. I coauthored the paper with Anne Berner at University of Göttingen, Stephan Bruns at Hasselt University, and Alessio Moneta at the Sant'Anna School of Advanced Studies in Pisa.

We developd this approach as part of our DP16 Australian Research Council funded project on energy efficiency. This is a multivariate time series model using time series for energy use, GDP, and the price of energy. The model allows us to control for shocks to GDP and the price of energy but to model the responses of those variables to the energy efficiency shock.

We estimate the effect of an energy efficiency shock on the use of energy. Initially, energy use falls, but we found using U.S. data that it then ends up bouncing back to almost where it started. This means that the rebound effect is around 100%. Energy efficiency improvements don't end up saving energy in the long run. That paper has now been published in Energy Economics.

This new paper extends this research in two ways:

1. We control for a wide array of macroeconomic variables that might affect our key variables of interest. In order to squeeze all that information into our model, we carry out a factor analysis and use the first two principal components. This time series model incorporating these factors is called a Structural Factor-Augmented Vector Autoregressive (S-FAVAR) model. The extracted principal components for our five countries are shown in this figure:

2. We apply the model to five countries rather than just the United States. The downside is that we ended up with much shorter time series, only covering 2008-2019.

We also use a Kalman filter method to derive monthly GDP series for the European countries. The choice of countries was restricted by the availability of reliable energy data. As we didn't have separate monthly primary electricity data for the European countries, our energy variable for these countries is just fossil fuels.

Our results are quite similar to our previous U.S. study:

The graph on the left shows how energy use changes over time following an energy efficiency shock. In all countries, it bounces back a lot. It seems like there is more chance of permanent energy savings in the UK than in the other countries. On the other hand, in the long run, the 90% confidence interval of the rebound effect overlaps 100% in all countries. So, energy savings aren't large and may be zero in the long run.

Of course, despite including more information, the results depend on a lot of assumptions. Most importantly, we are talking about an improvement in energy efficiency that is uncorrelated with shocks to the GDP such as total factor productivity improvements. It's possible that the rebound to shocks that are correlated to TFP shocks, if they exist, is quite different. Also, energy efficiency policies that get consumers and firms to do costly things to save energy theoretically have negative rebound. They should end up saving even more energy than is mandated. Given our results, these don't seem to be that important, but we shouldn't say that such policies won't save energy.

Monday, April 5, 2021

Third Francqui Lecture: The Rebound Effect

The video of my third Francqui lecture on the rebound effect is now on Youtube:

The first part of the presentation – "What is the Rebound Effect" – mostly comes from my teaching material on the rebound effect. The graph of the macroeconomic price effect comes from Gillingham et al. (2016). In the following two slides, I modified it to show infinitely elastic (assumed by Lemoine (2020) for example) and totally inelastic energy supply, which results in 100% rebound.

The next section – "The Economy-wide Rebound Effect: Evidence" – starts with a graph from my 2017 paper in Climatic Change: "How Accurate are Energy Intensity Projections?". The graph compares the historical rate of growth of energy intensity to the two "business as usual projections" in the 2016 World Energy Outlook. "Current policies" only includes implemented policies while "New policies" includes announced but not yet implemented policies. The latter is at the extreme of historical decline in energy intensity. This doesn't mean that it can't happen, but we should be sceptical given the performance of IEA projections described in my paper. The following slide shows the first page of another Gillingham et al. article, this time their 2013 paper in Nature. The rest of this section is based on my 2020 Energy Policy article: "How Large is the Economy-wide Rebound Effect?". A sad aspect of this article was that it was invited by Stephen Brown who died while I was writing it.

Saunders (1992) was one of the early papers in the modern revival in interest in the rebound effect. Lemoine (2019) is just a working paper version of Lemoine (2020), mentioned above. Lemoine does for general equilibrium what Saunders did for partial equilibrium. I kind of mangled my explanation of "Intensity vs. growth effects". The proper explanation is in my 2020 Energy Policy article.* Both elasticities on the RHS of the equation will be small if rebound is large and the energy cost share is small. Using Saunders' (1992) model as an example, the first elasticity is equal to sigma-1, where sigma is the elasticity of substitution between capital and energy. But the rebound holding GDP constant is sigma. If the elasticity of substitution is one – which is the case for the Cobb-Douglas function – then rebound is 100% holding GDP constant. The contribution of the second term to rebound is small if the energy cost share is small.

There are two graphs of "historical evidence". The monochrome one is from Arthur van Benthem's 2015 JAERE paper. The color one is based on one in my 2016 Energy Journal paper coauthored with Mar Rubio and Zsuzsanna Csereklyei, which I discussed in the previous lecture. The remaining references in this section are: Saunders (2008), Turner (2009), Rausch and Schwerin (2018), and Adetutu et al. (2016). They're all discussed in my Energy Policy paper.

The final section on "Using SVARs to Estimate the Economy-wide Rebound Effect" is mostly based on Bruns et al. (2020) (working paper). At the end, I added unpulished results on several European countries and Iran. This work was carried out in collaboration with Anne Berner and Mahboubeh Jafari. We haven't posted working papers for this research yet.

The "Conclusion" discusses Fullerton and Ta.

* Note, that almost all my papers also have an open-access working paper version accessible from the RePEc page for the article.

Friday, July 17, 2020

How Large is the Economy-Wide Rebound Effect?

Last year, I published a blogpost about our research on the economy-wide rebound effect. The post covers the basics of what the rebound effect is and presents our results. We found that energy efficiency improvements do not save energy. In other words, the rebound effect is 100%. This doesn't mean that improving energy efficiency is a bad thing. It's a good thing, because consumers get more energy services as a result. But it probably doesn't help the environment very much.

I now have a new CAMA working paper, which surveys the literature on this question. Contributions to the literature are broadly theoretical or quantitative. Theory provides some guidance on the factors affecting rebound but does not impose much constraint on the range of possible responses. There aren't very many econometric studies. Most quantitative studies are either calculations using previously estimated parameters and variables or simulations.

Theory shows that the more substitutable other inputs are for energy in production the greater the rebound effect. This means that demand for energy services by producers is more elastic and so reducing the unit costs of energy services increases the amount used by more.

The most comprehensive theoretical examination of the question is Derek Lemoine's new paper in the European Economic Review: "General Equilibrium Rebound from Energy Efficiency Innovation." Lemoine provides the first mathematically consistent analysis of general equilibrium rebound, where all prices across the economy can adjust to a change in energy efficiency in a specific production sector. He shows that the elasticity of substitution in consumption plays the same role as the elasticity of substitution in production: the greater the elasticity, the greater the rebound, ceteris paribus.

Beyond that, the predictions of the model depend on parameter values. The most likely case, assuming a weak response labor to changes in the wage rate, is that the general equilibrium effects increase energy use relative to the partial equilibrium direct rebound effect for energy intensive sectors and reduce it for labor intensive sectors.

Lemoine uses his framework and previously estimated elasticities and other parameters to compute the rebound to an economy-wide energy efficiency improvement in the US. The result is 38%. There are two main reasons why the real rebound might be higher than this. First, most of the elasticities of substitution in production that he uses are quite low because of how they were estimated. Second, an energy efficiency improvement in any sector apart from the energy supply sector does not trigger a fall in the price of energy. A fall in the price of energy would boost rebound. This is because there are no fixed inputs and there are constant returns to scale in energy production.

There are similar issues with simulations from computable general equilibrium models (CGE). The assumptions that modellers make and the parameter values they choose make a huge difference to the results. Depending on these choices, any result from super-conservation, where more energy is saved than the energy efficiency improvement alone would save, to backfire, where energy use increases, is possible.

Rausch and Schwerin estimate the rebound using a small general equilibrium model calibrated to US data. This is somewhere between the typical CGE model and econometric models. They use the putty-clay approach to measuring and modeling energy efficiency. Increases in the price of energy relative to capital are 100% translated into improvements in the energy efficiency of new capital equipment. Once capital is installed, energy and capital must be used in fixed proportions. Rebound in this model depends on why the relative price changes. If the price of energy rises, energy use falls. However, if the price of capital falls energy use increases. These are very strong assumptions, which determine how the data are interpreted. Are they realistic? Rausch and Schwerin find that historically rebound has been around 100% in the US.

Historical evidence also hints that the economy-wide rebound effect could be near 100%. Energy intensity in developing countries today isn't lower than it was in the developed countries when they were at the same level of income. This is despite huge gains in energy efficiency in all kinds of technologies from lighting to car engines. This makes sense if consumers have shifted to more energy intensive consumption goods and services over time. Commuters and tourists on trains in the 19th and early 20th centuries have been replaced by commuters and tourists in cars and on planes in the late 20th and early 21st centuries.

I only found three fully empirical econometric analyses. One of them is our own paper. The others are by Adetutu et al. (2016) and Orea et al. (2015). Both use stochastic production frontiers to estimate energy efficiency. This is a potentially promising approach. Adetutu et al. then model the effect of this energy efficiency one energy use, using an autoregressive model. This includes the lagged value of energy use as an explanatory variable, which means that the long-run effect of all variables is greater in absolute value than the short-run effect. As in the short run, energy efficiency reduces energy use, in the long run it reduces it even more. The result is super-conservation even though short-run rebound is 90%. In Orea et al.'s model, the purely stochastic inefficiency term is multiplied by [1-R(γ'z)] where z is a vector of variables including GDP per capita, the price of energy, and average household size. R(γ'z) is then supposed to be an estimate of the rebound effect. But really this is just a reformulation of the inefficiency term – nothing specifically identifies R(γ'z) as the rebound effect.

In conclusion, the economy-wide rebound effect might be near 100%. But I wouldn't describe the evidence as conclusive. Both our research and the historical investigations might be missing some important factor that has moved energy use in a way that makes us think it is due to changes in energy efficiency, and Rausch and Schwerin make very strong assumptions about analysing the data.

Tuesday, February 19, 2019

Energy Efficiency Improvements Do Not Save Energy

I have a new working paper out, coauthored with Stephan Bruns and Alessio Moneta, titled: "Macroeconomic Time-Series Evidence That Energy Efficiency Improvements Do Not Save Energy". It's another paper from our ARC funded project: "Energy Efficiency Innovation: Diffusion, Policy and the Rebound Effect". We estimate the economy-wide effect on energy use of energy efficiency improvements in the U.S. We find that the rebound is around 100%, implying that in the long run energy efficiency improvements do not save energy or reduce greenhouse gas emissions.

At the micro level, we might naïvely expect a 1% improvement in energy efficiency to reduce energy use by 1%. But people adjust their behavior. Efficiency improvements reduce the cost of energy services like heating, transport, or lighting. Because these are now cheaper to produce, people consume more of them, and so the percentage reduction in energy use is less than the improvement in efficiency. This is known as the direct rebound effect.

People might also redirect their spending to consume more of complementary goods, like larger houses in the case of residential heating improvements, and reduce their consumption of substitute goods and services, like bus rides or cycling, in the case of car fuel economy improvements. These changes have implications for the energy used to produce these goods and services. Additionally, the reduction in demand for energy should lower the price of energy further boosting the rebound in energy use. Finally, the improvement in energy efficiency is an increase in productivity, which should result in economic growth. Higher incomes mean higher demand for energy. Adding these indirect rebound effects to the direct rebound effect we get the economy-wide rebound effect.

The size of the economy-wide rebound effect is crucial for estimating the contribution that energy efficiency improvements can make to reducing energy use and greenhouse gas emissions. Our study provides the first empirical general equilibrium estimate of the economy-wide rebound effect. Previous studies use simulation models, known as computable general equilibrium models, or partial equilibrium econometric models that don't allow the price of energy to adjust. Some of the latter studies also measure rebound incorrectly, for example assuming that energy intensity – energy used per dollar of GDP – measures energy efficiency. In fact, the majority of the rebound effect happens when energy intensity rebounds as people shift to more energy intensive consumption after an energy efficiency improvement. Economic growth induced by the efficiency improvement is expected to contribute less to total rebound.

We use a structural vector autoregressive model, or SVAR, that is estimated using search methods developed in machine learning. We apply the SVAR to U.S. monthly and quarterly data. An SVAR explains changes in the vector of variables, x, in terms of its past values and a vector of serially and mutually uncorrelated shocks, ε:

In our basic model, the vector, x, contains three variables: primary energy use, GDP, and the price of energy. The first of the shocks is a shock to energy use, holding constant shocks to GDP and the price of energy and the past values of all three variables. We think this is a reasonable definition of an energy efficiency shock. The other two shocks are income and price shocks.

The matrix, B, which transmits the shocks to the dependent variables cannot be estimated without imposing some restrictions or conditions on the model. Usually economists use economic theory to impose restrictions on the coefficients in B (short-run restrictions) and the Π_i (long-run restrictions). Alternatively, they sample a range of models, rejecting only those that don't meet qualitative "sign restrictions" on the matrix B. Instead, we use independent component analysis, an approach that is relatively new to econometrics. This imposes conditions on the nature of the shocks instead and estimates B without direct restrictions. Unlike the short- and long-run restrictions approach, it doesn't impose a priori restrictions on the data, and unlike the sign restrictions approach, it estimates a unique model.

Using the estimated SVAR model we compute the impulse response functions of the dependent variables to the shocks:

The top left graph shows the effect of an energy efficiency shock on energy use. The grey shading is a 90% confidence interval, the x-axis is in months, and the y-axis in log units.

Initially, an energy efficiency shock strongly reduces energy use, but this effect wears off over the following years as consumers and the economy adjusts. Eventually, there is no change in energy use so that rebound is 100%.

The other graphs in the first column show the effect of the energy efficiency shock on GDP and the price of energy. The second column shows the effect of a shock to GDP, and the final column an energy price shock.

The implications for policy are that encouraging energy efficiency innovation is unlikely to make a contribution to reducing greenhouse gas emissions. This is one reason why I am skeptical of projections that predict that energy intensity will fall much faster in the future than in the past because of energy efficiency policies.

On the other hand, if these policies raise rather than reduce the costs of producing energy services then the direct rebound (and presumably the economy-wide rebound) will be negative rather than positive. As, apart from their environmental effects, these would reduce economic welfare, it seems that there would be better options to reduce emissions by switching to low carbon energy.

Sunday, December 23, 2018

Annual Review 2018

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. This year was a bit of a struggle at times, so it's a good idea to remind myself of what I did manage to accomplish.

Me and my mother holding my brother in 1967

Going into this year, I had high expectations for getting more research done, as I finished my term as economics program director at Crawford at the end of 2017. In the first semester, I was teaching a new course, or rather a subject I last taught more than a decade ago – environmental economics – but I thought that should be manageable and had three weeks of class prepared at the beginning of the semester. I definitely don't have a comparative advantage in teaching, it takes me a lot of time and effort to prepare. Then my mother died in the week that class began. This was quite expected – she was not doing well when I visited in December – but of course the exact timing is never known. I didn't travel to Israel for the funeral. I had already agreed with my brother up front to travel for the "stone-setting", which in Israel is 30 days after the death. It is the custom to bury someone on the day they die, if possible, so I didn't want to delay that. After I got back, I got ill with a flu/cold, which resulted in me completely losing my voice so I couldn't teach at all. So this was a difficult semester. In October/November I again got ill with flu/lung infection of some sort and lost a month of research time.

Noah and me in Sweden

But there were also happier travels during the year. In June and July, I traveled with my wife, Shuang, and son, Noah, to the Netherlands, Finland, Sweden, and Japan. I went to three conferences: the IAEE meeting in Groningen and the IEW and World Congress in Gothenburg. Shuang also attended the World Congress. The visits to Finland and Japan were just for fun. Stephan Bruns was also at the IAEE meeting and actually presented our paper on rebound, which got very positive feedback. Stephan and Alessio Moneta did most of the econometric work on the paper, which we are about to submit now.

In September I went to Rome and Singapore for two workshops. At the Villa Mondragone, near Frascati, outside of Rome, was the Climate Econometrics Conference. I presented a paper that compared different estimators of the climate sensitivity. This produced some unexpected results, and it looks like it needs a lot more work some time! I met lots of people including meeting my coauthor Richard Tol for the first time.

Villa Mondragone near Frascati

In Singapore, I attended the 5th Asian Energy Modelling Workshop, which mostly focuses on integrated assessment modeling. By then, I was confident enough to present the rebound paper myself.

I also went to the Monash Environmental Economics Workshop in Melbourne in November. This is a small meeting with just one stream of papers, but they are all focused on environmental economics, whereas the larger annual AARES conference mostly focuses on agriculture.

Akshay Shanker and I finally put out a working paper that was our contribution to a Handelsbanken Foundation funded project headed by Astrid Kander. We are also branding this as part of our ARC funded DP16 project, as we have also been using ARC funding on it. We also completed work this year on the major part of the work on rebound that was part of the DP16 proposal. Zsuzsanna Csereklyei, who was working on the DP16 project, moved to a lecturer position at RMIT.

The Energy Change Institute at ANU won the annual ANU Grand Challenge Competition with a proposal on Zero-Carbon Energy for the Asia-Pacific. Actually, the project already received several hundred thousand dollars of interim funding from the university in 2018 and I have been working with Akshay on the topic of electricity markets as part of this project. We'll continue research on the topic during 2019.

ECI Grand Challenge Presentation Team: Paul Burke, Kylie Catchpole, and Emma Aisbett

We only managed to publish two papers with a 2018 date:

Burke P. J., D. I. Stern, and S. B. Bruns (2018) The impact of electricity on economic development: A macroeconomic perspective, International Review of Environmental and Resource Economics 12(1) 85-127. Working Paper Version | Blogpost

Csereklyei Z. and D. I. Stern (2018) Technology choices in the U.S. electricity industry before and after market restructuring, Energy Journal 39(5), 157-182. Working Paper Version | Blogpost

But we have several papers in press:

Bruns S. B., J. König, and D. I. Stern (in press) Replication and robustness analysis of 'Energy and economic growth in the USA: a multivariate approach', Energy Economics. Working Paper Version | Blogpost

Bruns S. B., Z. Csereklyei, and D. I. Stern (in press) A multicointegration model of global climate change, Journal of Econometrics. Working Paper Version | Blogpost

Bruns S. B. and D. I. Stern (in press) Lag length selection and p-hacking in Granger causality testing: Prevalence and performance of meta-regression models, Empirical Economics. Working Paper Version | Blogpost

We posted five new working papers, three of which haven't been published yet:

Flying More Efficiently: Joint Impacts of Fuel Prices, Capital Costs and Fleet Size on Airline Fleet Fuel Economy Blogpost
November 2018. With Zsuzsanna Csereklyei.

Energy Intensity, Growth and Technical Change
September 2018. With Akshay Shanker. Blogpost

How to Count Citations If You Must: Comment
January 2018. With Richard Tol. Blogpost

Google Scholar citations approached 16,000 with an h-index of 51.

The trend to fewer blogposts continued – this is only the 9th blogpost this year. Twitter followers rose from 750 to 950 over the year.

Akshay Shanker – his primary adviser was Warwick McKibbin and I was on his supervisory panel – received his PhD with very positive feedback from the examiners. He has a part time position at ANU working on the Grand Challenge Project and I am supervising him on that.

There doesn't seem to have been any major progress on the issues surrounding economics at ANU, that I mentioned in last year's post. Arndt Corden seems to be heading towards being a specialist program dealing with developing Asia and there is no overall identity for economics at Crawford. I increasingly identify with the Centre for Applied Macroeconomic Analysis.

On a related theme, I applied for three jobs on three different continents. One of these – the one in Australia – went as far as an onsite interview, but the more I learnt about the job the less enthusiastic I was, and I wasn't offered it. It was a 50/50 admin/leadership and research position.

Looking forward to 2019, a few things can be predicted:

We're about to submit our first paper on the rebound effect and should also put out a working paper or two on the topic.
I'll continue research with Akshay on the Grand Challenge project.
I'm not planning to go to any conferences this year. I have one seminar presentation lined up at Macquarie University in the second half of the year.
My PhD student Panittra Ninpanit will submit her thesis at the beginning of the year, and I have a new student, Debasish Kumar Das, starting. The plan is for him to work on electricity reliability.
I'll be teaching environmental economics and the masters research essay course again in the first semester.

Trying to understand the menu in Finland

Monday, November 26, 2018

Flying More Efficiently

I have another new working paper out, coauthored with Zsuzsanna Csereklyei on airline fleet fuel economy. Zsuzsanna worked as research fellow here at the Crawford School on my Australian Research Council funded DP16 project on energy efficiency and the rebound effect. This paper reports on some of our research in the project. We also looked at energy efficiency in electric power generation in the US.

The nice thing about this paper is that we have plane level data on the aircraft in service in 1267 airlines in 174 countries. This data is from the World Airliner Census from Flight Global. We then estimated the fuel economy of 143 aircraft types using a variety of data sources. We assumed that the plane would fly its stated range with the maximum number of passengers and use all its fuel capacity. This gives us litres of fuel per passenger kilometre. Of course, many flights are shorter or are not full, and so actual fuel consumption per passenger kilometre will vary a lot, but this gives us a technical metric which we can use to compare models.

The graph shows that the fuel economy of new aircraft has steadily improved over time. One of the reasons for the scatter around the trendline is that large aircraft with longer ranges tend to have better fuel economy than small aircraft:

This is also one of the reasons why fuel economy has improved over time. Still, adjusted for size, aircraft introduced in earlier decades had (statistically) significantly worse fuel economy than more recent models. We used these regressions to compute age and size adjusted measures of fuel economy, which we used in our main econometric analysis.

The main analysis assumes that airlines choose the level of fuel economy that minimizes costs given input prices and the type of flying that they do. There is a trade off here between doing an analysis with very wide scope and doing an analysis with only the most certain data. We decided to use as much of the technical aircraft data as we could, even though this meant using less certain and extrapolated data for some of the explanatory variables.

We have data on wages in airlines and on the real interest rates in each country. The wage data is very patchy and noisy and we extrapolated a lot of values from the observations we had in the same way that, for example, the Penn World Table extrapolates from surveys. There are no taxes on aircraft fuel for international travel and the price of fuel reported by Platts does not vary a lot around the world. But countries can tax fuel for domestic aviation. We could only find data on these specific taxes for a small number of countries in a single year. So, we used proxies, such as the price of road gasoline and oil rents, for this variable. We proxy the type of flying airlines do using the characteristics of their home countries.

The most robust results from the analysis – that hold whether we use crude fuel economy or fuel economy adjusted for size and age – are that – all things constant – larger airlines select planes with higher fuel economy, higher interest rates are associated with poorer fuel economy, higher fuel prices are associated with higher fuel economy (but the elasticity is small), and fuel economy is worse in Europe and Central Asia than other regions.

It seems that for a given model age and size, more fuel efficient planes cost more. This would explain why, even holding age and size factors constant, higher interest rates are correlated with worse fuel economy. Also, if larger airlines have more access to finance or a lower cost of capital they will be able to afford the more fuel efficient planes.

What effect could carbon prices have on fleet fuel economy? The most relevant elasticity is the response of unadjusted fuel economy to the price of fuel. This allows airlines to adjust the size and model age of planes in response to an increase in the price of fuel. We estimate that this elasticity is -0.09 to -0.13, which suggests the effect won't be very big. Because we use proxies for the price of fuel, we expect that the true value of this elasticity is actually higher. The elasticity also assumes that there is a given set of available aircraft models. Induced innovation might result in more efficient models being developed. There might also be changes in the types of airlines and flights. So the effect could be quite a bit larger in the long run.

Wednesday, October 3, 2018

Energy Intensity, Growth, and Technical Change

I have a new working paper out, coauthored with Akshay Shanker. Akshay recently completed his PhD at the Crawford School and is currently working on the Energy Change Institute's Grand Challenge Project among other things. This paper was one of the chapters in Akshay's thesis. Akshay originally came to see me a few years ago about doing some research assistance work. I said: "The best thing you could do is to write a paper with me – I want to explain why energy intensity has declined using endogenous growth theory." This paper is the result. Along the way, we got additional funding from the College of Asia and the Pacific, the Handelsbanken Foundation, and the Australian Research Council.

World and U.S. energy intensities have declined over the past century, falling at an average rate of approximately 1.2–1.5 percent a year. As Csereklyei et al. (2016) showed, the relationship has been very stable. The decline has persisted through periods of stagnating or even falling energy prices, suggesting the decline is driven in large part by autonomous factors, independent of price changes.

In this paper, we use directed technical change theory to understand the autonomous decline in energy intensity and investigate whether the decline will continue. The results depend on whether the growing stock of knowledge makes R&D easier over time – known as state-dependent innovation – or whether R&D becomes harder over time.

Along a growth path where real energy prices are constant, energy use increases, energy-augmenting technologies – technologies that improve the productivity of energy ceteris paribus – advance, and the price of energy services falls. The fall in the price of energy services reduces proﬁtability and incentives for energy-augmenting research. However, since the use of energy increases, the "market size" of energy services expands, improving the incentives to perform research that advances energy-augmenting technologies. In the scenario with no state dependence, the growing incentives from the expanding market size are enough to sustain energy-augmenting research. Energy intensity continues to decline, albeit at a slower rate than output growth, due to energy-augmenting innovation. There is asymptotic convergence to a growth path where energy intensity falls at a constant rate due to investment in energy-augmenting technologies. Consistent with the data, energy intensity declines more slowly than output grows, implying that energy use continues to increase.

This graph shows two growth paths – for countries that are initially more or less energy intensive – that converge to the balanced growth path G(Y) as their economies grow:

This is very consistent with the empirical evidence presented by Csereklyei et al. (2016).

However, the rate of labor-augmenting research is more rapid along the balanced growth path and there will be a shift from energy-augmenting research to labor-augmenting research for a country that starts out relatively energy intensive. This explains Stern and Kander's (2012) finding that the rate of labor-augmenting technical change increased over time in Sweden as the rate of energy-augmenting technical change declined.

The following graph shows the ratio of the energy-augmenting technology to the labor-augmenting technology over time in the US, assuming that the elasticity of substitution between energy and labor services is 0.5:

Up till about 1960, energy-augmenting technical change was more rapid than labor-augmenting technical change and the ratio rose. After this point labor-augmenting technical change was more rapid, but the rise in energy prices in the 1970s induced another period of more rapid energy-augmenting technical change.

In an economy with extreme state-dependence, energy intensity will eventually stop declining because labor-augmenting innovation crowds out energy-augmenting innovation. Our empirical analysis of energy intensity in 100 countries between 1970 and 2010 suggests a scenario without extreme state dependence where energy intensity continues to decline.

Tuesday, April 24, 2018

Replicating Stern (1993)

Last year, Energy Economics announced a call for papers for a special issue on replication in energy economics. Together with Stephan Bruns and Johannes König we decided to do a replication of my 1993 paper in Energy Economics on Granger causality between energy use and GDP. That paper was the first chapter in my PhD dissertation. It is my fourth most cited paper and given the number of citations could be considered "classic" enough to do an updated robustness analysis on it. In fact, another replication of my paper has already been published as part of the special issue. The main results of my 1993 paper were that in order to find Granger causality from energy use to GDP we need to use both a quality adjusted measure of energy and control for capital and labor inputs.

It is a bit unusual to include the original author as an author on a replication study, and my role was a bit unusual. Before the research commenced, I discussed with Stephan the issues in doing a replication of this paper, giving feedback on the proposed design of the replication and robustness analysis. The research plan was published on a website dedicated to pre-analysis plans. Publishing a research plan is similar to registering a clinical trial and is supposed to help reduce the prevalence of p-hacking. Then, after Stephan and Johannes carried out the analysis, I gave feedback and helped edit the final paper.

Unfortunately, I had lost the original dataset and the various time series I used have been updated by the US government agencies that produce them. The only way to reconstruct the original data would have been to find hard copies of all the original data sources. Instead we used the data from my 2000 paper in Energy Economics, which is quite similar to the original data. Using this close to original data, Stephan and Johannes could reproduce all my original results in terms of the direction of Granger Causality and the same qualitative significance levels. In this sense, the replication was a success.

But the test I did in 1993 on the log levels of the variables is inappropriate if the variables have stochastic trends (unit roots). The more appropriate test is the Toda-Yamamoto test. So, the next step was to redo the 1993 analysis using the Toda-Yamamoto test. Surprisingly, these results are also very similar to those in Stern (1993). But, when Stephan and Johannes used the data for 1949-1990 that are currently available on US government websites, the Granger causality test of the effect of energy on GDP was no longer statistically significant at the 10% level. Revisions to past GDP have been very extensive, as we show in the paper:

Results were similar when they extended the data to 2015. However, when they allowed for structural breaks in the intercept to account for oil price shocks and the 2008-9 financial crisis, the results were again quite similar to Stern (1993) both for 1949-1990 and for 1949-2015.

They then carried out an extensive robustness check using different control variables and variable specifications and a meta-analysis of those tests to see which factors had the greatest influence on the results.

They conclude that p-values tend to be substantially smaller (test statistics are more significant) if energy use is quality adjusted rather than measured by total joules and if capital is included. Including labor has mixed results. These findings largely support Stern’s (1993) two main conclusions and emphasize the importance of accounting for changes in the energy mix in time series modeling of the energy-GDP relationship and controlling for other factors of production.

I am pretty happy with the outcome of this analysis! Usually it is hard to publish replication studies that confirm the results of previous research. We have just resubmitted the paper to Energy Economics and I am hoping that this mostly confirmatory replication will be published. In this case, the referees added a lot of value to the paper, as they suggested to do the analysis with structural breaks.

Thursday, December 28, 2017

Annual Review 2017

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. As I mentioned in last year's review, I am still struggling with work-life balance. It feels like that there is never enough time to get the work done I need to do and I am always making excuses for not getting things done. So, stopping and looking at what I did get done can help provide some perspective.

I was IDEC (Crawford's economics program) director till the end of the year. Ligang Song will take over as IDEC director in 2018. We continued to work on developing and seeking approval for new programs. We made some progress, but the final outcome will only be known in 2018 (hopefully). There was also quite a lot of work on the review of the Crawford School, the future of Asia-Pacific economics at ANU, economics at Crawford and ANU etc. The Arndt-Corden Department of Economics is officially a separate organizational unit from IDEC. Our plan is that going forward Arndt-Corden will represent the research, outreach, and PhD program components of all the economics activity at Crawford and IDEC will continue as the masters teaching program. This too is a work in progress.

ANU environment and resource economists, Paul Burke, Frank Jotzo, Quentin Grafton, Jack Pezzey, and me

I made two international trips - one to Singapore and one to Europe and two short trips in Australia to Brisbane and Melbourne. I went to the Singapore meeting for the IAEE international conference. My wife, Shuang, and son, Noah, came along too and we extended our stay to spend time in Singapore. We took the new direct flight from Canberra to Singapore, which is very convenient. From February there will also be Qatar Airways flights from Canberra, but apparently they will stop in Sydney before continuing to Singapore. That will just save time (maybe) on going between terminals in Sydney. To get to Europe I flew to Adelaide and then took Emirates via Dubai.

I was in Brisbane for the AARES conference. I have always found that the conference is much more dominated by agricultural economics than the journal but almost everything at the conference this time was agriculture related. Most of the environment papers dealt with agricultural impacts. I decided not to go in 2018, though the program is looking more balanced.

In December I traveled to Spain, Germany, and Israel. I gave a seminar at ICTA at the Autonomous University of Barcelona on the role of energy in modern economic growth. This was part of a series of seminars funded by the Maria de Maeztu program.

Speaking at ICTA, UAB, Barcelona

From there, I went on to Germany to work with Stephan Bruns on our ARC project and climate change paper. Alessio Moneta also visited from Pisa for a couple of days. Totally by coincidence, I arrived in Göttingen on the same day as Paul Burke who was touring Germany as part of his Energy Transition Hub activities:

Stephan Bruns, Krisztina Kis-Katos, Paul Burke, and me in Göttingen

We made quite good progress on both projects while I was there, but there is still much to do. We are just over the halfway point with the ARC DP16 project. One short paper is already published in Climatic Change, which discusses the accuracy of projections of future energy intensity. We also have another working paper on the restructuring of the US electricity generation industry and energy efficiency and have a paper under review on aircraft fuel economy.

We also completed and submitted our paper on the macroeconomic aspects of electricity and economic development for the DFID funded EEG project. Publication of the working papers and announcement of the next stages of the project have been much delayed, but there should be news on the latter soon. Together with our PhD student Akshay Shanker, I made a lot of progress on our contribution to a Handelsbanken Foundation funded project headed by Astrid Kander. Well, Akshay did most of the work... The paper – about why energy intensity declines over time – will now be part of Akshay's PhD thesis.

I published fewer papers than last year, which isn't a surprise, as last year was a record year. There were five articles with a 2017 date:

Stern D. I., R. Gerlagh, and P. J. Burke (2017) Modeling the emissions-income relationship using long-run growth rates, Environment and Development Economics 22(6), 699-724. Working Paper Version | Blogpost

Stern D. I. (2017) How accurate are energy intensity projections? Climatic Change 143, 537-545. Working Paper Version | Blogpost

Zhang W., D. I. Stern, X. Liu, W. Cai, and C. Wang (2017) An analysis of the costs of energy saving and CO2 mitigation in rural households in China, Journal of Cleaner Production 165, 734-745. Working Paper Version | Blogpost

Stern D. I. and J. van Dijk (2017) Economic growth and global particulate pollution concentrations, Climatic Change 142, 391-406. Working Paper Version | Blogpost

Stern D. I. (2017) The environmental Kuznets curve after 25 years, Journal of Bioeconomics 19, 7-28. Working Paper Version | Blogpost

and there is one in press at the moment:

Bruns S. B. and D. I. Stern (in press) Overfitting bias and p-hacking in Granger-causality testing: Meta-evidence from the energy-growth literature, Empirical Economics. Working Paper Version | Blogpost

I also published a comment on a paper in Scientometrics:

Stern D. I. (2017) Comment on Bornmann (2017): Confidence intervals for journal impact factors, Scientometrics 113(3), 1811-1813. Blogpost

Follow the links to the blogposts to find out more about each paper.

I also published between 1 and 3 book chapters. It's often hard to work out when exactly a book chapter is published or not! This one is definitely published and it's open access for now. I only do book chapters where I can update an existing survey paper for the purpose. I posted 5 working papers, two of which have already been published and two are in the review process. In total, 6 papers are currently submitted, resubmitted, or in revision for resubmission.

Citations almost reached 14,000 on Google Scholar (h-index: 45) and will be well in excess of that for the end of 2017 when all this year's citations are finally included in Google's database.

I became an editor at PeerJ as part of their expansion into the environmental sciences. So far, I haven't actually handled a paper but I'm sure there will be some relevant submissions soon.

On the teaching side, I convened Masters Research Essay for the first time in the 1st Semester and taught Energy Economics for the last time for now in the second semester. My first PhD student here at Crawford, Alrick Campbell, received his PhD at the July graduation ceremony. He is currently a lecturer at the University of the West Indies in Jamaica.

I have been blogging even less this year than last. This will be the 19th post for 2017, whereas last year there were 35. Lack of time and increased use of Twitter are to blame. My Twitter followers now number more than 750, up from over 500 last year. The most popular blogpost this year was "Confidence Intervals for Journal Impact Factors".

Looking forward to 2018, it is easy to predict a couple of things that will happen that are already organized:

1. As mentioned above, I am ending my term as director of our economics program, IDEC, at the end of this calendar year. I am hoping to be able to focus a bit more on my research and get more balance in the coming year.

2. I will be the convener for Masters Research Essay and teach Environmental Economics in the first semester. I last taught environmental economics 10 years ago at RPI, so it will be quite a lot of work. I was getting a bit tired of teaching Energy Economics and if I did this course, Paul Burke could teach one of our compulsory first year masters microeconomics courses, so I decided to take it on. Both these courses are in the 1st semester and so I won't be teaching in the 2nd semester.

3. Early in the new year we will put out a working paper for our time series analysis of global climate change. We are currently revising the paper to resubmit to the Journal of Econometrics.

Nothing came of the job I applied for last year beyond the Skype interview, but I applied for another one this year...

Thursday, March 23, 2017

Two New Working Papers

We have just posted two new working papers: Technology Choices in the U.S. Electricity Industry before and after Market Restructuring and An Analysis of the Costs of Energy Saving and CO2 Mitigation in Rural Households in China.

The first paper, coauthored with Zsuzsanna Csereklyei, is the first to emerge from our ARC funded DP16 project. Our goal was to look at the factors associated with the adoption of more or less energy efficient electricity generating technologies using a detailed US dataset. For example, combined cycle gas turbines are more energy efficient than regular gas turbines and supercritical coal boilers are more efficient than subcritical. Things are complicated by the different roles that these technologies play in the electricity system. Because regular gas turbines are less energy efficient but have lower capital costs they are mainly used to provide peaking power, while combined cycle turbines contribute more to baseload. So comparing combined cycle gas to subcritical coal makes more sense as a test of how various factors affect the choice of energy efficiency than comparing the two types of gas turbine technologies.

Additionally, some US regions underwent electricity market reform where either just wholesale or both wholesale and retail markets were liberalized, while other regions have retained integrated regulated utilities, which are typically guaranteed a rate of return on capital. Unless regulators press utilities to adopt energy efficient technologies there is much less incentive under rate of return than under wholesale markets to do so.

The graph shows that following widespread market reform at the end of the 20th Century there was big boom in investment in the two main natural gas technologies. More recently renewables have played an increasing role and there was a revival of investment in coal up to 2012. These trends are also partly driven by the lagged (because investment takes time) effects of fuel prices:

We find that electricity market deregulation resulted in significant immediate investment in various natural gas technologies, and a reduction in coal investments. However, market deregulation impacted less negatively on high efficiency coal technologies. In states that adopted wholesale electricity markets, high natural gas prices resulted in more investment in coal and renewable technologies.

There is also evidence that market liberalization encouraged investments into more efficient technologies. High efficiency coal technologies were less negatively affected by market
liberalization than less efficient coal technologies. Market liberalization also resulted in increased investment into high efficiency combined cycle gas. In summary the effect of liberalization is most negative for the least efficient coal technology and most positive for the most efficient natural gas technology.

The second paper is based on a survey of households in rural China and assesses the potential for energy conservation and carbon emissions mitigation when energy saving technologies are not fully implemented. In reality, appliances do not always survive for their designed lifetime and households often continue to use other older technologies alongside the new ones. The effect is to raise the cost of reducing energy use and emissions by a given amount. The paper computes marginal abatement cost curves under full and partial implementation of the new technologies.

The graph shows the marginal abatement cost curve for rural households in Hebei Province, scaled up from the survey and our analysis. Full-Scenario is the curve with full implementation of new technologies and OII-Scenario is with actual partial implementation. This analysis does not take into account any potential rebound effect of energy efficiency improvements.

The first author, Weishi Zhang, is a PhD student at the Chinese University of Hong Kong. She contacted me last year about possibly visiting ANU, and I supported her application for a scholarship to fund the visit (which unfortunately she didn't get), because I thought her research was some of the more interesting research on Chinese energy use and pollution that I had seen. I helped write the paper (and responses to referees in our revise and resubmit).

Monday, March 13, 2017

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.

Monday, December 26, 2016

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...