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.




 


Friday, August 6, 2021

Data for "Interfuel Substitution: A Meta-Analysis"

I've long thought that there was an error in the way I calculated the shadow elasticity of substitution (SES) in my 2012 paper on interfuel substitution in the Journal of Economic Surveys. This would have been a big problem as the paper carries out a meta-analysis of SESs. But no primary paper reported the results in terms of the SES. I computed all this data from the various ways results were presented in the original studies. I never got around to doing anything about it or even checking carefully whether there was a mistake. I suppose this is because I hate finding mistakes in my papers and as a result procrastination goes into superdrive.

Yesterday a student wrote to me and requested the data. I have now checked the derivation of the SES in my database and also computed it in an alternative way. There is in fact no mistake. This is great news!

The reason I thought that there was a mistake is because of the confusing notation used for the Morishima Elasticity of Substitution (MES). Conventionally, the MES is written as MES_ij for the elasticity of substitution between inputs i and j when the price of i changes. By contrast, the cross-price elasticity is written eta_ij for the elasticity of demand for the quantity of input i with respect to the price of input j!*

I have now uploaded the database used for the meta-analysis to my data website. The following is a description of what is in the Excel spreadsheet:

Each line in the main "data" worksheet is for a specific sample/model in a specific paper. Each of these typically has multiple elasticity estimates.

Column A: Identification number for each paper.

Columns B to L: Characteristics of the authors. Including their rank in the Coupe ranking that was popular at the time.

Column M: Year paper was published.

Columns N to V: Characteristics of the journals in which the papers were published. This includes in Column O the estimated impact factor in the year of publication. Others are impact factors in later years.

Column W: Number of citations the paper had received in the Web of Science at the time the database was compiled.

Column X: Number of citations the lead author has had in their career apart from for this paper.

Columns Y to AO: Characteristics of the sample used for the estimates on that line. So looking at the first line in the table, as an example, we have:

Data from Canada for 1959-1973. Annual observations. This is a panel for different industries. N=2, so there are two industries but a single estimate for both. T is the length of the time series dimension. Sample size is N*T*Number of equations - i.e if there are 4 fuels usually 3 equations are estimated. This could be different if the cost function itself is also estimated, but it looks like no papers did that. (There are also papers using time series for individual industries etc and cross-sections at one point in time.)

Column AH: Whether fixed effects estimation was used or not (only makes sense for panel data).

Column AC: The standard deviation of change in the real oil price in that period.

Column AD: PPP GDP per capita of the country from the Penn World Table. Probably the mean for the sample period.

Column AE: Population of the country in millions. Looks like the mean for the sample period.

Columns AP to AZ are the specification of the model:

Column AP: Not4 - if there weren't 4 fuels in the analysis.

Column AQ: Partial elasticity - this is holding the level of total energy use constant.

Column AR: Total elasticity - this allows the level of total energy use to change.

Columns AS and AT: If this is a dynamic model these are estimates of the short-run or the long-run elasticity.

Column AU: The model is derived from a cost function, or something else.

Column AW: Functional form of the model.

Column AW: Form of the equations estimated - usually cost shares - log ratios means the log of the ratio of cost shares.

Column AX to AZ: How technical change is modeled. Many papers don't model any technical change explicitly. Energy model means there is biased technical change for energy inputs. Aggregate model means that if other inputs are also modeled they also have biased technical change. Kalman means that the Kalman filter was used to estimate stochastic technical change.

Columns BA to the end have the actual estimates. Different papers provide different information. All the various estimates eventually are converted into Shadow Elasticities of Substitution. 

Columns BA to BP: Own price and cross-price elasticities of demand. For example: Coal-Oil means the cross-price elasticity of demand for coal with respect to the price of oil.

Columns BQ to CF: Reported translog cost function parameters.

Columns CP to CS: Cost shares at the sample mean. These are used in various elasticity formulae. They were derived in a variety of ways from the information in papers. One of these methods is the quadratic solution in Columns CG to CO. It uses demand elasticities and translog parameters to reverse engineer the cost shares. Other estimates take the ratio of demand and Allen elasticities.

Columns CT to DE: Morishima elasticities of substitution. These are asymmetric - so we have oil-coal and coal-oil. Here the terminology is very confusing. The standard terminology is that MES_ij is for a change in the price of i. So coal-oil is for a change in the price of coal. This is the reverse of what is used for cross-price elasticities! It is super-confusing.

Columns DF to DK have the shadow elasticities I actually used in the meta-analysis.

Columns DL to EA have the Allen elasticities of substitution. Some of these are reported in the papers and some I computed from the cross-price elasticities.

* You can learn more about all these elasticities in my 2011 Journal of Productivity paper on the topic.

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.


Wednesday, May 12, 2021

Fifth Francqui Lecture: Econometric Modeling of Climate Change

The video of my fifth and final Francqui lecture on the economic modeling of climate change is now on Youtube:  


The lecture begins by introducing the issue of global climate change. The first image of the Earth's energy balance is from an IPCC assessment report. Probably, the 4th Assessment Report. The graph of global temperature is the Berkeley Earth combined land and sea series. The graph of CO2 concentration is based on the data we used in our Journal of Econometrics paper updated with recent observations from Hawaii. The original source of the global CO2 emissions series is the now defunct CDIAC website updated from the BP Statistical Review of World Energy. Following that are three charts from the IPCC 5th Assessment Report. World sulfur dioxide emissions are from the CEDS datasite.

The next section – "Why Econometrics" – opens with a graph of the relationship between economic growth and CO2 emissions, which I put together from World Bank, International Energy Agency, and BP data sources.

The following section – "Do GHG Emissions Cause Climate Change?" starts with original research using the temperature and CO2 time series in the previous graphs. The CO2 concentration acts as a proxy variable for all radiative forcing in this analysis. It then goes on to present results from my 2014 paper with Robert Kaufmann published in Climatic Change. Details of the data are given in that paper.

Finally, I presented my paper coauthored with Stephan Bruns and Zsuzsanna Csereklyei, which was published in the Journal of Econometrics.

Public Policy Schools in the Asia-Pacific Ranked

I have a new paper with my Crawford School colleague Bjoern Dressel published in Asia & the Pacific Policy Studies (open access). The data and figures for the article are on Figshare. Bjoern has been interested for a while in ranking public policy schools in the Asia-Pacific region.  But a comprehensive ranking seemed hard to achieve. Recently, I came across an article by Ash and Urquiola (2020) that ranks US public policy schools according to their research output and impact. Well, we thought, if they can rank schools just by their research output and not by their education and public policy impact then so can we 😀. Research is the easiest component to evaluate.

We compare the publication output of 45 schools with at least one publication listed in Scopus between 2014 and 2018, based on affiliations listed on the publications rather than current faculty. We compute the 5-Year impact factor for each school. This is identical to the impact factor reported for academic journals, but we compute it for a school rather than a journal. It is the mean number of citations received in 2019 by a publication published between 2014 and 2018. This can be seen as an estimate of research quality. We also report the standard error of the impact factor as in my 2013 article in the Journal of Economic Literature. If we treat the impact factor as an estimate of the research quality of a school then we can construct a confidence interval to express how certain or uncertain we are about that estimate. This graph shows the schools ranked by impact factor with a 90% confidence interval:

Peking and Melbourne are the two top-ranked schools but the point estimates have a very wide confidence interval. This is because their research output is relatively small and the variance of citations is quite large. The third ranked school – SGPP in Indonesia – only had two publications in our target period. After that there are several schools with much narrower confidence intervals. These mostly have more publications.


Here we can see the impact factors on the y-axis and the number of publications of each school on the x-axis. Three schools clearly stand out at the right: Crawford, Lee Kwan Yew, and Tsinghua. These schools are also top-ranked by total citations, which combines the quality and quantity variables. The three top schools account for 54% of publications and 63% of citations from the region.

In general, the elite schools are in China and Australia. Australia has three out of the top ten schools ranked by impact factor and total citations, despite its small population size. China, on the other hand has at least five schools ranked in the top ten across both rankings, which is remarkable given that many of these schools have been established only in the last 15 years (though linked to well-established research universities).

We found more schools that had no publications in Scopus in the target period. Perhaps in some cases they are too new, or faculty use their other affiliations, but clearly there is a lot of variation in research-intensiveness. Somewhat surprising is the low ranking of public policy schools in Japan and India – both countries with a considerable number of public policy schools, but none in the top ten schools when ranked by 5-year citation impact factor or total number of citations. 

One reason for the strong performance of the Chinese schools is that they focus to some degree on environmental issues, and particularly climate change, where citation numbers tend to be higher. We did not adjust for differences in citations across fields in this research, but this is something that future research should address.



Wednesday, April 28, 2021

Fourth Franqui Lecture: Energy and the Industrial Revolution

The video of my fourth Francqui lecture on the energy and the industrial revolution is now on Youtube:

 


The opening graph of population and GDP per capita in the United Kingdom since 0CE combines data from the Maddison Project at the University of Groningen and data produced by Steven Broadberry. The energy data in the next graph was compiled in a 2007 publication by Paul Warde. The graph of energy use in Europe since 1500 and the graph of the composition of energy use are from "Power to the People" by Astrid Kander, Paolo Malanima, and Paul Warde.

The next section of the presentation gives a high level summary of Daron Acemoglu's theory of directed technical change and applies it to the two case studies. The first is my paper coauthored with Jack Pezzey and Yingying Lu, forthcoming in JAERE, on directed technical change and the British industrial revolution. The second is my 2012 paper coauthored with Astrid Kander on the role of energy in the industrial revolution and modern economic growth. As I mentioned in the lecture, we didn't know much about the theory of directed technical change when we wrote this paper and it didn't influence our research. Yet we can explain the results in terms of the theory.

The graphs that open the section on the British industrial revolution use data from Broadberry and Warde as well as from Robert Allen's book on the industrial revolution (the price data). The painting of the Iron Bridge is by William Williams.

Opening the section on Sweden is a photo of the Aitik copper mine. We used data from the Historical National Accounts of Sweden and Astrid's PhD research. If you are wondering how the value of energy could be as large as the GDP in 1800 in Sweden this is because energy is an intermediate good. GDP is value added by labor and capital with land included in capital usually. Gross output of the economy is much larger than the GDP. A huge amount of economic activity was dedicated to producing food, fuel, and fodder.

The solar panels that open the concluding section are in Japan. I've forgotten where.

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.



Wednesday, March 24, 2021

Second Francqui Lecture: Energy and Economic Growth and Development

The video of my second Francqui lecture on energy and economic growth is now on Youtube:

The first part of the presentation comes from my teaching material on the biophysical foundations of economics. There are a couple of slides of energy units and energy flows from the Global Energy Assessment. The slide of the Earth and economic system is from Perman et al.

The next section of the lecture on the "stylized facts" is based on my 2016 paper with Zsuzsanna Csereklyei and Mar Rubio published in the Energy Journal. I updated the data from 2010 to 2018 using the Penn World Table and International Energy Agency data. The third section on the meta-analysis of the energy and economic growth literature is based on my 2014 paper with Stephan Bruns and Christian Gross also published in the Energy Journal. Finally, I talked about my work with Akshay Shanker in our 2018 working paper: "Energy Intensity, Growth and Technical Change". This material was the most technical and "inside baseball" of the lecture (though a lot less technical than the paper). I think I got a bit lost towards the end when I was talking about the effect of the price of energy on energy intensity and other speculations... But the key message is that there is a lot to research still in this area.

Friday, March 12, 2021

Inaugural Francqui Lecture: Economic Growth and the Environment

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

 

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

Monday, February 8, 2021

Energy and Economic Growth: Updated Animation

Almost seven years ago, I posted an animation of a series of Excel graphs showing the relationship between energy use and GDP per capita over time in a sample of 99 countries. In preparation for my Francqui Lectures, I've updated the animation to 2018 using the new PWT 10 GDP data (and still using IEA energy data). I also replaced Cuba with Botswana, but not changed any of the other countries:

 

The outlier that starts getting poorer but maintains its energy use near the end of the sequence is Venezuela. The curve does look like it twists a bit clockwise over time but it is still pretty consistent. So, I ran 48 annual cross section regressions and plotted the values of the coefficients over time with a 95% confidence interval:



The drop off in the slope coefficient in the last 2 years seems to be due to the behavior of the Venezuela outlier. Otherwise, both coefficients drift without a clear trend.


Monday, February 1, 2021

Francqui Lectures Plan

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

 

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

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

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

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

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

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

Saturday, December 19, 2020

Annual Review 2020

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. In the first half of the year we had the bushfires, the hailstorm, and then the pandemic and the "shutdown".

On 5 January we woke up to orange light and visibility of only a couple of hundred metres at best where I live. It felt like being on the surface of Titan (but much warmer :)).


My brother visited from Israel later in the month when conditions were a little better. The day he arrived was the hailstorm. Shortly after that the Orroral Valley Fire got going. At one point we had ash falling in Canberra like snowflakes. In early February I went on my only trip outside Canberra this year to Auckland, New Zealand for the IAEE Asia-Pacific Conference. 
 
 
ANU switched from in person teaching to online teaching in late March. An extra week was added to the semester to give us time to adapt. It wasn't too hard as we already have lot of material online, including lectures recorded in the previous two years. My masters research essay course was very easy to shift online. My environmental economics course was harder. I took the whiteboard in my office home to do tutorials: 
 
 
The big challenge was that the schools closed down for around 8 weeks, I think (my son Noah is 4 years old and in preschool for most of the week usually), at exactly the same time and we also have a baby who was 9 months old then. So, I didn't have much work time that wasn't occupied with teaching. In total, there have been 117 cases of COVID-19 in Canberra (population: 426k) and 3 people have died.

In the second half of the year, school and daycare came back and gradually things got more under control. I was actually quite productive research-wise and finished all the papers that were waiting to be revised and resubmitted when the shutdown struck. Well, after doing a lot of work on a revise and resubmit for Climatic Change, I gave up, resulting in this blogpost instead.

I even started four new projects towards the end of the year. One is about ranking public policy schools in the Asia-Pacific, which we have already submitted.  This is a paper that my colleague, Björn Dressel, long wanted to write. My first paper coauthored with a political scientist. Another is a citation analysis, following up on my 2013 paper in the Journal of Economic Literature. The third is about animal power and energy quality... The fourth is a follow on to our paper in the Journal of Econometrics this year on time series modeling of global climate change. Actually, we might give up on this one too. I was supposed to give a presentation on it at the AGU meeting in December, but we withdrew the paper as our early results were hard to understand.

We also wrote a policy brief for the Energy and Economic Growth Programme on prepaid metering in developing countries.

We published five papers with a 2020 date:

Leslie G. W., D. I. Stern, A. Shanker, and M. T. Hogan (2020) Designing electricity markets for high penetrations of zero or low marginal cost intermittent energy sources, Electricity Journal 33, 106847. Working Paper Version | Blogpost

Stern D. I. (2020) How large is the economy-wide rebound effect? Energy Policy 147, 111870. Working Paper Version | Blogpost

Nobel A., S. Lizin, R. Brouwer, S. B. Bruns, D. I. Stern, and R. Malina (2020) Are biodiversity losses valued differently when they are caused by human activities? A meta-analysis of the non-use valuation literature, Environmental Research Letters 15, 070030.

Csereklyei Z. and D. I. Stern (2020) Flying more efficiently: Joint impacts of fuel prices, capital costs and fleet size on airline fleet fuel economy, Ecological Economics 175, 106714. Working Paper Version | Blogpost | Data and Code

Bruns S. B., Z. Csereklyei, and D. I. Stern (2020) A multicointegration model of global climate change, Journal of Econometrics 214(1), 175-197. Working Paper Version | Blogpost | Data

We posted four working papers. Two of those were already published this year and the links are above. The third is a revised version of our paper on the industrial revolution:

Directed technical change and the British Industrial Revolution. December 2020. With Jack Pezzey and Yingying Lu. Blogpost 1, Blogpost 2

The fourth is a nineteen author review for Annual Review of Environment and Resources:

Energy efficiency: What has it delivered in the last 40 years? December 2020. With Harry Saunders et al. Blogpost

We have five papers under review at the moment (three are resubmissions), one revise and resubmit we are working on, and eight more that we are actively working on or trying to finish.

Google Scholar citations exceeded 19,000 with an h-index of 53. I wrote a few more blogposts this year. This is the 10th this year compared to only three last year. Twitter followers rose from 1250 to 1500 over the year. At one point, I actually unfollowed everyone and then added back people I wanted to follow. This made my Twitter feed more manageable and I lost very few followers in the process. 
 
In July, I moved all my email (more than 160k messages) from Outlook on local hard drives to GMail. I use Thunderbird as the front end. Now all my data is in the cloud (everything else is on Dropbox) and can be accessed from anywhere. I still use locally stored applications, so if I want to use specialized software – for example, my econometrics package RATS – I still need to use my own computer.
 
I did 7 external assessments of people for promotion, tenure, or fellowships for universities in Pakistan, Australia, South Africa, USA, Sudan, and Singapore. I'd only done 9 of these previously in my career according to my records. Hard to explain this sudden rush! As a result, I only did 12 reviews for journals, which was lower than typical in the past. And a bunch of papers for EAERE, a proposal for the ARC...

I taught environmental economics and the masters research essay course again. This was the third time I taught the environmental economics course. After a few weeks we had to shift both courses online as I mentioned above. One of the challenges was carrying out a final exam remotely, which I discussed in a workshop ANU ran in the following semester
 
Xueting Zhang started as my PhD student.  In the first year, she has been focused on coursework, we are now transitioning to research. I have one other student for whom I am the primary supervisor, Debasish Das. He's working on prepaid metering in Bangladesh and other energy related topics. This involves struggling with a big data set. We only used a small sample in the Energy Insight linked above.

Looking forward to 2021, a couple of things can be predicted:
  • I was awarded a Francqui Chair at the University of Hasselt in Belgium for the 2020-21 academic year. So, now I have to come up with ten hours of lectures. What can't be predicted is if I will actually travel to Belgium.
  • I'll be teaching environmental economics and the master's research essay course again in the first semester. This year, we are also introducing a year long "Master's Research Project" in parallel with the one semester "essay".
  • I'm hoping we get the resubmitted papers and the revise and resubmit published, but that is in the hands of the editors, referees, and journal publishers...

Friday, December 11, 2020

Energy Efficiency: What Has It Delivered in the Last 40 Years?

I'm one of nineteen authors of a new review of energy efficiency economics. It was commissioned for the Annual Review of Environment and Resources, where it is still in (second-round) review. The team was put together and led by Harry Saunders and Joyashree Roy.

Over the past four decades different disciplinary approaches independently adopted different definitions of energy efficiency to answer specific problems. Even within economics there are at least three different ideas of energy efficiency. Technical efficiency in economics compares the quantity of inputs used to produce given outputs (or vice versa) to the best practice or frontier level. This is a relative measure of energy efficiency. But economists often talk about energy efficiency in absolute terms too,  measured as either simply an increase in energy services per unit input or using the concept of energy augmenting technological change where the amounts of other inputs and the technology associated with them are held constant. Energy augmenting technological change is usually used when modeling economy-wide rebound, whereas the energy services per unit input might be used when investigating the energy efficiency gap.

The energy intensity of economies (a metric measuring energy consumption per unit of GDP), which is often interpreted as a proxy for energy efficiency, has trended downwards (increasing efficiency) globally and in many major economies over the last century. But as panel (a) below shows, in many regions of the world, especially poorer or hotter regions, energy intensity instead increased. Today, energy intensity is more similar around the world than in the past.

Innovation in energy-saving technologies is an important driver in improving aggregate energy efficiency deployment by lowering costs and inducing adoption. The productivity of numerous energy-using products has improved dramatically. (e.g., lighting had a 10,000-fold improvement in lumens/Watt since the start of the industrial revolution). 

Energy efficiency improvements, in themselves, generally increase economic welfare. But when we consider negative externalities, such as pollution emissions, welfare effects are more ambiguous. Interventions such as improperly calibrated subsidies to improve energy efficiency or mandates to use costly technologies can lead to a reduction in household welfare.

There is still uncertainty and difficulty in measuring economy-wide rebound effects. Rebound may limit the ability to reduce or constrain overall energy use. In general, it makes more sense to address the environmental impacts of energy use with specific environmental policies rather than trying to reduce energy use with energy efficiency policies.

The contribution of different factors to the persistent “energy efficiency gap”, i.e., the difference between the energy consumption observed and the potential energy consumption levels that would result from the adoption of cost-efficient energy efficient technologies and strategies, is still not well understood. Market and regulatory failures, departure of consumer behavior from rational choice theory, lack of information, the principal-agent problem, among other issues may all contribute to the energy efficiency gap.

Policy interventions aimed at overcoming or reducing barriers to energy efficiency deployment target behavioral anomalies and perceived market failures. They include provision of feedback to energy users, the use of social norms, commitment devices, rewards and regulatory mechanisms such as taxes, subsidies, building codes, etc. The literature and evidence are mixed on the effectiveness of each of these, but all seem to show promise to some degree. 

Methodological advances for examining energy efficiency effects on energy use have been substantial. Primary advances include randomized control trials coupled with appropriate econometric methods, developments in econometric methods and lab/field experiments, agent-based modeling formulations, general equilibrium methods, and behavioral science. 

The following diagram summarizes the state of knowledge across different scales and the needed scope of future research:

Future research should bring together researchers from different fields to shed new light onto energy efficiency questions. Examples of such endeavors include: (i) at the micro-level, a better understanding of consumer choice and behavior by combining insights from engineering and the advanced metering and sensing infrastructure, with those from micro-economic theory as well as the theory of choice and with behavioral economists’ models; (ii) at the program evaluation level, there is a need to continue to develop methods to understand causal inferences using econometrics as well as machine learning to better understand program outcomes; (iii) at the macro-level, developing flexible and credible general equilibrium models that also capture environmental and climate externalities outcomes, and that have good input data to enable us to understand the dynamics of energy efficiency improvements across the economy, the environment, and society, are needed.

Thursday, November 26, 2020

Francqui Chair

I am one of the two recipients (one is Belgian and one international) of a Francqui Chair at Hasselt University. This sounds like an academic position but actually is a one year appointment during which the chair is supposed to give 10 hours of lectures in their field. I'm not sure yet what they are exactly looking for. But here are Siem Jan Koopman's planned lectures at the University of Antwerpen.

 


So, I'm thinking if I weave my research into a more pedagogical narrative I would be on the right track. I am hoping the lectures will be recorded and I will be able to post them here on Stochastic Trend. My coathors Stephan Bruns and Robert Malina nominated me for this award.

I am hoping I will actually be able to visit Belgium next year assuming that I will be able to access a COVID-19 vaccine.


Asymmetric Carbon Emissions-Output Elasticities

This semester my masters' research essay student, Kate Martin, revisited the topic of whether the carbon emissions-output elasticity is greater in recessions than in economic expansions. In other words, does a 1% increase in output increase carbon emissions by less than a 1% fall in output reduces them?

Sheldon (2017) used quarterly US GDP data and carbon emissions data from the 1950s to 2011 and found that the elasticity in recessions was much larger than in expansions when it was not significantly different to zero. There was also a strong positive drift in emissions of 5.8% p.a.

To measure output, Kate used monthly US industrial production data from 1973 to 2020 and monthly GDP data from 1992 that are available from Macroeconomic Advisers. The advantage of the longer time series is that it covers more recessions and expansions. She also compared this monthly data to quarterly data to test the effect of data frequency. She found that using industrial production and, in particular industrial CO2 emissions rather than total CO2 emissions from fossil fuels, the elasticity is actually larger in expansions but it is not statistically significantly different from the elasticity in recessions. Using GDP data at both monthly and quarterly frequencies and including the last decade of data confirmed Sheldon's basic result.

When Kate restricted the estimation period to the end of 2019, the resulting model projected emissions during the COVID recession (using the reported industrial production data) very well:

This difference between the effect of industrial production and overall GDP on emissions doesn't seem to have been commented on before. However, Eng and Wong (2017) used monthly industrial production data and found that in the short-run the elasticity is symmetric but in the long run the recession elasticity is larger.

Tuesday, November 17, 2020

Prepaid Metering and Electricity Consumption in Developing Countries

I've written an Energy Insight policy brief for the EEG Programme with my PhD student Debasish Das on prepaid metering and its effect on electricity consumption.

The bottom line is that consumers who are switched to prepaid metering significantly reduce their electricity consumption. 

Debasish is working on a study of the effect of prepaid metering in Bangladesh and some preliminary results are in this paper. This graph shows the estimated difference in monthly electricity consumption between consumers in two areas of Dhaka, Bangladesh around the time that one group was switched to prepaid metering:

 
 
Electricity consumption in the treated group fell by 17%. This graph didn't make it into the final version of the paper, because it was deemed to be too mathy. Debasish has a very large dataset that he obtained from the Bangladesh electric utilities. He's still working on getting this into a usable form. But hopefully we will have some more results soon.

Wednesday, October 28, 2020

Assessing Students during the Pandemic

Last week I was a panelist at an ANU webinar on assessing students during the current pandemic conditions.

You can watch just my part where I talk about reorganizing my course to deal with online exams:

Or the whole discussion here:

Thursday, October 15, 2020

Climate Econometrics and the Carbon Budget

Though I recently abandoned a follow up paper on our Journal of Econometrics climate modeling paper, we are now working on a different one. I'm scheduled to give a presentation (remotely) on it at the American Geophysical Union conference in December. In the course of our research, I ran some simple simulations on our Journal of Econometrics model. This model is a two equation vector autoregression of global surface temperature and radiative forcing with energy balance restrictions imposed. This is done using the concept of multicointegration. But it is still a simple time series model once the complicated estimation is complete.

I ran three scenarios that all have the same peak level of radiative forcing equivalent to doubling CO2:

Single Shock: Forcing is doubled in one year and then the system is allowed to move to equilibrium.

Shock and Maintain: Forcing is doubled suddenly and then that level of forcing is maintained forever. This is the scenario in our published paper and is used to estimate the equilibrium climate sensitivity in general circulation models.

Transient: Forcing is increased linearly for 70 years until the CO2 equivalent would be doubled. Then emissions are cut to zero.

This is what happens to temperature in the three scenarios:

Under the Shock and Maintain scenario, we reach the equilibrium climate sensitivity of 2.78ºC. Under the Transient scenario, the temperature increases by 1.85ºC when emissions are cut to zero and then continues to increase by about 0.3ºC before flatlining. Under the Single Shock scenario, temperature increases quite rapidly, reaching equilibrium in around 40 years with only a 0.98ºC increase.

This is what happens to radiative forcing in the three scenarios:

Under the Single Shock scenario there is a steep fall in forcing after the single pulse of greenhouse gases. A new equilibrium concentration and temperature is reached. Under the Transient scenario the equilibrium level of forcing is much higher even though in both cases emissions are cut to zero. Of course, much more carbon would need to be pumped into the atmosphere to achieve the Transient path as all the time carbon is also being absorbed. This shows the importance of the carbon budget. The total amount of emissions, not just the peak concentration matters. It is interesting that our very simple model seems to pick this up from the data without imposing any information about the carbon budget on the model.



Wednesday, August 5, 2020

Abandoning a Paper

Now and then it's time to give up on a project. In September 2018, I attended a climate econometrics conference at Frascati near Rome. For my presentation, I did some research on the performance of different econometric estimators of the equilibrium climate sensitivity (ECS) including the multicointegrating vector autoregression (MVAR) that we used in our paper in the Journal of Econometrics. The paper included estimates using historical time series observations (from 1850 to 2014), a Monte Carlo analysis, estimates using output of 16 Global Circulation Models (GCMs), and a meta-analysis of the GCM results.


The historical results, which are mostly also in the Journal of Econometrics paper, appear to show that taking energy balance into account increases the estimated climate sensitivity. By energy balance, we mean that if there is disequilibrium between radiative forcing and surface temperature the ocean must be heating or cooling. Surface temperature is in equilibrium with ocean heat, and in fact follows ocean heat much more closely than it follows radiative forcing. Not taking this into account results in omitted variables bias. Multicointegrating estimators model this flow and stock equilibirum. The residuals from a cointegrating relationship between the temperature and radiative forcing flows are accumulated into a heat stock, which in turn cointegrates with surface temperature. If we have actual observations on ocean heat content or radiative imbalances we can use them. But available time series are much shorter than those for surface temperature or radiative forcing. The results also suggested that using a longer time series increases the estimated climate sensitivity.

The Monte Carlo analysis was supposed to investigate these hypotheses more formally. I used the estimated MVAR as the model of the climate system and simulated the radiative forcing series as a random walk. I made 2000 different random walks and estimated the climate sensitivity with each of the estimators. This showed that, not surprisingly, the MVAR was an unbiased estimator. The other estimators were biased using a random walk of just 165 periods. But when I used a 1000 year series all estimators were unbiased. In other words, they were all consistent estimators of the ECS. This makes sense, because in the end equilibrium is reached between forcing and surface temperature. But it takes a long time.

Each of the GCMs I used has an estimated ECS ("reported ECS") from an experiment where carbon dioxide is suddenly increased fourfold. I was using data from a historical simulation of each GCM, which uses the estimated historical forcings over the period 1850 to 2014. A major problem in this analysis is that the modelling teams do not report the forcing that they used. This is because the global forcing that results from applying aerosols etc depends on the model and the simulation run. So, I used the same forcing series that we used to estimate our historical models. This isn't unprecedented, Marvel et al. (2018) do the same.

In general, the estimated ECS were biased down relative to the reported ECS for the GCMs, but again, the estimators that took energy balance into account seemed to do better. In an meta-analysis of the results, I compared how much the reported radiative imbalance (=ocean heat uptake roughly) from each GCM increased to how much the energy balance equation said it should increase using the reported temperature series, reported ECS, and my radiative forcing series. A regression analysis showed, that where the two matched, the estimators that took energy balance into account were unbiased, while those that did not match, under-estimated the ECS.

These results seemed pretty nice and I submitted the paper for publication. Earlier this year, I got a revise and resubmit. But when I finally got around to working on the paper post-lockdown and post-teaching things began to fall apart.

First, I came across the Forster method of estimating the radiative forcing in GCMs. This uses the energy balance equation:

where F is radiative forcing, T is surface temperature, and N is radiative imbalance. Lambda is the feedback parameter. ECS is inversely proportional to it. The deltas indicate the change since some baseline period. Then, if we know N and T, both of which are provided in GCM results, we can find F! So, I used this to get the forcing specific to each GCM. The results actually looked nicer than in the originally submitted paper. These are the results for the MVAR for 15 CMIP5 GCMs:


The rising line is a 45 degree line, which marks equality between reported and estimated ECSs. The multicointegrating estimators were still better than the other estimators. But there wasn't any systematic variation in the degree of underestimation that would allow us to use a meta-analysis to derive an adjusted estimate of the ECS.

This is still OK. But then I read and re-read more research on under-estimation of the ECS from historical observations. The recent consensus is that estimates from recent historical data will inevitably under-estimate the ECS because feedbacks change from the early stages after an increase in forcing to the latter stages as a new equilibrium is reached. The effective climate sensitivity is lower at first and greater later.

OK, even if we have to give up on estimating the long-run ECS, my estimates are estimates of the historical sensitivity. Aren't they? The problem is that I used the long-run ECS to derive the forcing from the energy balance equation. So, the forcing I derived is wrong. It is too low. I could go back to using the forcing I used previously, I guess. But now I don't believe the meta-analysis of that data is meaningful. So, I have a bunch of estimates using the wrong forcing with no way to further analyse them.

I also revisited the Monte Carlo analysis. By the way I had an on-and-off again coauthor through this research. He helped me a lot with understanding how to analyse the data. But he didn't like my overly bullish conclusions on the submitted paper and so withdrew his name from it. But he was maybe going to get back on the revised submission. He thought that the existing analysis which used an MVAR to produce the simulated data was maybe biased unfairly in favour of the MVAR. So, I came up with a new data-generating process. Instead of starting with a forcing series I would start with the heat content series. From that I would derive temperature, which needs to be in equilibrium with heat content and then using the energy balance equation derive the forcing. To model the heat content I fitted a unit root autoregressive model (stochastic trend) to the heat content reported from the Community GCM with the addition of a volcanic forcing explanatory variable. The stochastic trend represents anthropogenic forcing. The Community GCM is one of the 15 GCMs I was using and it has temperature and heat content series that look a lot like the observations. I then fitted a stationary autoregressive model for temperature with the addition of the heat content as an explanatory variable. The simulated model used normally distributed shocks with the same variance as these fitted models and volcanic shocks.

As an aside, the volcanic shocks were produced by the model:
where rangamma(0.05) are random numbers drawn from a standard gamma distribution with shape parameter 0.05. This is supposed to produce the stratospheric sulfur radiative forcing, which decays over a few years following an eruption. Here is an example realisation:

The dotted line is historical volcanic forcing and the solid line a simulated forcing. My coauthor said it looked "awesome".

So, again, I produced two sets of 2000 datasets. One with a sample size of 165 and one with a sample size of 1000. Now, even in the smaller sample, all four estimators I was testing produced essentially identical and unbiased results! I ran this yesterday. So, our Monte Carlo result disappears. I can't see anything unreasonable about this data generating process, which produces completely different results to the one in the submitted paper. So, I don't see anything to justify one over the other. So, this was the point where I gave up on this project.

My coauthor, who is based in Europe, is on vacation. Maybe he'll see a way to save it when he comes back, but I am sceptical.

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.