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.