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.

 

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.

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.

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.

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.

How Accurate are Projections of Energy Intensity?

A new short working paper about how accurate projections of future energy intensity are. It's an extension of comments I made at Energy Update 2016 here at the ANU.

Energy intensity is one of the four factors in the Kaya Identity, which is often used to understand changes in greenhouse gas emissions. It is one of the two most important factors together with the rate of economic growth. The 2014 IPCC Assessment Report shows that less than 5% of models included in the assessment project that energy intensity will decline slower than the historic rate under business as usual:*

Is this likely? In the paper, I evaluate the past performance of the projections implied by the World Energy Outlook (WEO) published annually (except in 1997) by the International Energy Agency (IEA). The following graph shows the average annual difference between the projected and actual rate of change in energy intensity in subsequent years** for each WEO since 1994:

Positive errors mean that energy intensity declined slower than projected in the following years while negative errors mean it declined faster. So, for example, the error of -0.4% for 2000 means that over the years 2001-2015, on average energy intensity declined by 0.4% a year faster than was projected in the 2000 WEO.

It turns out that these errors are strongly negatively correlated (r = -0.8) with the error in projecting the rate of economic growth, which IEA outsources. Csereklyei et al. (2016), similarly, find that reductions in energy intensity tend to only occur in countries with growing economies. If we divide and multiply the growth rate of energy intensity g(E/Y) by the growth rate of GDP g(Y) we get the following identity:

The first term on the right hand side can be seen as the elasticity of energy intensity with respect to GDP.*** The following graph plots the elasticity as projected and as subsequently realized for each WEO:

The two seem to have tracked each other quite well. But there is a complication. The 1994 to 96 WEOs only projected future energy use up to 2010. 2010 is the only recent year when global energy intensity actually increased. This end point reduces (in absolute value) the actual elasticities for these three WEOs. From 1998 on, the difference between the projected and actual rate of change in energy intensity is calculated up to 2015. But through the 2011 WEO, 2010 is one of the years in the projection period. From 2012, 2010 is no longer include in the projection period and there is a sharp step down in the actual elasticity over the projection period. I think that the elasticities for 2012-16 probably under-estimate the true long-run elasticities and that the relatively stable values from 1998-2011 are more representative of what the future elasticities will be over the full projection horizon to 2030 or 2040.

If that is the case, then the projected elasticity of -0.6 in the 2016 WEO probably over-estimates the the elasticity that will be realized in the long run. Why would this be the case?

Early WEOs largely modeled energy intensity trends based on historical trends. This is not the case for recent WEOs. Over time, the IEA has endogenized more variables in their model of the world energy system and included more and more explicit energy policies. It is likely that the model under-estimates the economy-wide rebound effect. It's also possible that energy efficiency policies are not implemented as effectively as expected.

As part of our ARC funded DP16 project, we hope to contribute to improving future projections of energy intensity by empirically estimating the economy-wide rebound effect.

* The light grey area indicates the projections between the 95th and 100th percentile of the range for the default scenario.

** The base year for each WEO is 2-3 years before the publication date. Therefore, we can already assess the 2015 and 2016 WEO's.

*** We can use the identity to decompose the projection errors:

Over time the contribution of errors in the projected growth rate has increased relative to the contribution from errors in the elasticity. But I think that if we revisit this experiment in 2030 we will find a larger contribution from errors in the elasticity for what are currently recent issues of the WEO.

P.S. 23 June 2017

The paper is now published in Climatic Change.

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.

World Energy Outlook 2016 and the Rebound Effect

I've been asked to make some brief comments on the 2016 World Energy Outlook just published by the IEA at the ANU Energy Change Institute's 2016 Energy Update. It's a huge report, but I'll focus on the global projections for energy use and GHG emissions. I think that the IEA are still over-optimistic about the potential for energy intensity improvements and underestimate the future contribution of non-fossil energy. Under the "Current Policies" scenario they expect fossil fuels to have 79% of total energy in 2040 vs. 81% today. The current rapid growth of renewables under current policies makes me skeptical about that. The decline in world energy intensity is also more rapid than in recent decades.

Three main scenarios used throughout the report are summarized in the following Figure:

 

The "New Policies Scenario" includes policies from NDC's where the policy to implement the pledge appears to actually exist. The "450" Scenario is where policies that actually limit warming to 2 degrees C are implemented. Clearly, decarbonization is minimal under the current policies scenario and not that great under the new policies scenario. But the improvement in energy intensity is very large under all scenarios and does the vast majority of the work in reducing CO2 emissions. How plausible is this huge reduction in energy intensity? Here, I plot the historical global trend in energy intensity and the growth rates projected under the current and new policies scenarios:

The current policies scenario projects an increase in the rate of reduction in energy intensity relative to the 1990-2015 mean. This is possible, the rate of change might accelerate, but I am skeptical. Just looking at the data, we see that in the last few business cycles, energy intensity rose or fell slowly after recessions compared with later parts of boom periods. So, we seem likely to go through other cycles like these. Another issue is that the Chinese economy might have grown slower than the government admitted to in the recent couple of years. This would have exaggerated the global decline in energy intensity but probably not be a lot. The main reason, is that energy efficiency improvements do not translate one-for-one to reductions in energy intensity. The rebound effect, which we are researching in our ARC DP16 grant, means that improvements in energy efficiency lead to increases in the use of "energy services" - like heating, lighting, transport etc. which mean that energy use does not decrease as much as it would if all the efficiency improvement flowed through to energy consumption. At the micro-economic level this is simply because these energy services become cheaper as a result of the efficiency improvement. At the macro-level things are more complicated. I suspect that the IEA's model, which is driven by exogenous assumptions on things like the rate of economic growth, underestimates the economy-wide rebound effect.

Discovery Projects 2016

We - myself together with Stephan Bruns and Alessio Moneta - got an ARC Discovery Projects grant. Thanks also to Zsuzsanna Csereklyei who contributed to the development of the proposal and who we may hire as a post-doc using the grant - depending if she wants to come to Australia for the time we will be able to afford to fund with the money we received ($A273k - 65% of what we requested). This is my second ARC grant following the DP12 grant that we got a few years ago. The title of our project is: "Energy Efficiency Innovation, Diffusion and the Rebound Effect". We will be looking at the diffusion of energy efficiency innovations and trying to measure the economy-wide rebound effect empirically. This was our second attempt at applying for a grant on this topic. Last time around we were rated in the top 10% of unfunded proposals and so I thought it was worthwhile revising and resubmitting!

Other good news today is that my colleague, Paul Burke got a DECRA grant. I think this is our fourth DECRA at Crawford. Congratulations to Paul! Also Peter MacDonald and Robert Sparrow got a DP16 grant. Congratulations to Robert and Peter!

Donglan Zha

Donglan Zha is visiting Crawford School for the next year. She is an associate professor at Nanjing University of Aeronautics & Astronautics and works on energy economics including research on substitution possibilities and the rebound effect. Her office is in Constable's Cottage across the road from the main Crawford Building. Her ANU e-mail address is donglan.zha@anu.edu.au. Please welcome Donglan to the Crawford School, I'm sure she will be happy to meet with you.

Energy Leapfrogging (or Not)

Arthur van Benthem has a recent paper in the Journal of the Association of Environmental and Resource Economists titled "Energy Leapfrogging". The main thesis of the paper is that despite presumed improvements in the energy efficiency of individual technologies such as cars and refrigerators, energy intensity in developing countries today is similar to what it was in today's developed countries when they were at a similar income level. There is no "energy leapfrogging". This is also an implication of our paper "Energy and Economic Growth: the Stylized Facts". If there has been an almost constant log-linear relationship between energy use and GDP per capita then there is no energy leapfrogging.

van Benthem suggests that a major contributor to this is that the consumption bundle in developing countries today is much richer in energy services like personal transport than was the consumption bundle at a similar level of development in today's developed countries. Consumers have substituted towards these now cheaper energy services (what are they consuming less of though?).

On the face of it, this suggests that there would be a very large rebound effect due to substitution towards energy services. This is on top of any indirect rebound effect due to increased energy productivity boosting income and thus energy demand as originally proposed by Harry Saunders.

On the other hand, there must be some shift away from energy services as income increases so that energy intensity is lower in richer countries. Anyway, this is pretty speculative but worth thinking about, I think.

Energy Prices, Growth, and the Channels in Between: Theory and Evidence

Lucas Bretschger has an interesting new paper in Resource and Energy Economics titled: "Energy prices, growth, and the channels in between: Theory and evidence". The paper argues that countries with higher energy use per capita grow slower in the long-run though reductions in energy use lower output in the short-run. The long-run effect is due to an induced increase in capital accumulation and because the model is similar to AK endogenous growth models, innovation. The paper is motivated by the stylized fact that in a sample of 37 countries, countries with higher per capita energy use grow slower. The sample includes mostly developed countries but also China and India.

This negative correlation is, however, easy to explain in terms of catch-up growth dynamics. As we show in our stylized facts paper, there is a strong positive correlation between the level of GDP per capita and the level of energy use per capita. Per capita income in countries such as China and India has risen faster than in the developed countries due to the fact that they are poorer and undergoing catch-up growth. This results in a spurious negative relationship between energy use per capita and the rate of economic growth.

This is not to say at all that Bretschger's theoretical model is wrong, but the motivation can easily be explained in another way. In fact, I'm very sympathetic to the idea that plentiful energy resources could slow the rate of economic growth as I discussed in my presentation at the AARES conference in Rotorua and my upcoming Arndt-Corden seminar on 17th March.

Bretschger also estimates an econometric model that is loosely related to his theoretical model (for example, using energy quantity rather than price due to a lack of internationally comparable data - a big problem for energy economics) that regresses the investment/output ratio on energy intensity and GDP (there are additional equations). It is not surprising that reduced energy intensity could encourage increased investment if it represents increased energy efficiency - this is one of the factors in macro-level rebound as in Harry Saunders (1992) model.

The bottom line is that the energy-output relationship is quite complicated and is probably not at all well captured by reduced form time series models. Bretschger is also making this point with his paper.

The Rebound Effect

Working on an article for New Palgrave. Here is a draft of the section on the rebound effect:

The Rebound Effect

Energy saving innovations reduce the cost of providing energy services such as heating, lighting, industrial power etc. This reduction in cost encourages consumers and firms to use more of the service. As a result energy consumption usually does not decline by as much as the increase in energy efficiency implies. This difference between the improvement in energy efficiency and the reduction in energy consumption is known as the rebound effect. Rebound effects can be defined for energy saving innovations in consumption and production. In both cases the increase in energy use due to increased use of the energy service where an efficiency improvement has happened is called the direct rebound effect. For consumer use of energy estimated rebound effects are usually small typically in the range of 10-30% (Greening et al., 2000; Sorrell et al., 2009). Roy (2000) argues that because high quality energy use is still small in households in India, demand is very elastic, and thus rebound effects in the household sector in India and other developing countries can be expected to be larger than in developed economies. In the case of energy efficiency improvements in industry the rebound effect at the firm level could be large as the form could greatly increase their sales as a result of reduced costs. However, under perfect competition for an industry supplying domestic demand it is much harder for the industry as a whole to expand output and so the direct rebound effect would be more limited. Rebound effects are likely to be larger for export industries that have more opportunity to expand production (Grepperud and Rasmussen, 2004; Allan et al., 2007; Linares and Labandeira, 2010).

As a result of the reduction in the cost of the energy service consumers will demand less of substitute goods and more of complementary goods. These include other energy services. Firms will make similar changes in their demands for inputs. There will also be additional repercussions throughout the economy – non-energy goods whose demand has increased require energy in their production; the fall in energy demand may lower the price of energy (Gillingham et al., 2013; Borenstein, 2015) increasing energy use again; and the efficiency improvement is a contribution to an increase in total factor productivity, which tends to increase capital accumulation and economic growth that results again in greater energy usage (Saunders, 1992). These additional effects are called indirect rebound effects, though the latter two may be treated separately as “macro-level rebound effects” (e.g. Howarth, 1997). Direct and indirect rebound effects together sum to the economy-wide rebound effect.

Estimates of the economy-wide rebound effect are few in number (e.g. Turner, 2009; Barker et al., 2009; Turner and Hanley, 2011) and vary widely (Stern, 2011; Saunders, 2013; Turner 2013). At the economy-wide level “backfire”, where energy use increases as a result of an efficiency improvement, or even “super-conservation” where the rebound is negative are both theoretically possible (Saunders, 2008; Turner, 2009). It is usually assumed that the indirect rebound is positive and that the economy-wide rebound will be larger in the long run than in the short run (Saunders, 2008). Turner (2013) argues, instead, that because the energy used to produce a dollar’s worth of energy is higher than the embodied energy in most other goods, the effect of consumers shifting spending to goods other than energy will mean that the indirect rebound could be negative and the economy-wide rebound may also be negative in the long run. Borenstein (2015) presents further arguments for negative rebounds.

All evidence on the size of the economy-wide rebound effect to date depends on theory-driven models, which have limited empirical validation. Turner (2009) finds that, depending on the assumed values of the parameters in a simulation model, the rebound effect for the UK can range from negative to more than 100%. Barker et al. (2009) provide the only estimate of the global rebound effect, estimating the rebound from a set of IEA recommended energy efficiency policies at 50%.

References

Allan, G., Hanley, N., McGregor, P., Swales, K., Turner, K. 2007. The impact of increased efficiency in the industrial use of energy: A computable general equilibrium analysis for the United Kingdom. Energy Economics 29: 779–798.

Barker, T., Dagoumas, A. and Rubin, J. 2009. The macroeconomic rebound effect and the world economy. Energy Efficiency 2: 411-427.

Borenstein, S. 2015. A microeconomic framework for evaluating energy efficiency rebound and some implications. Energy Journal 36(1): 1-21.

Gillingham, K., Kotchen, M. J., Rapson, D. S. and Wagner, G. 2013. The rebound effect is overplayed. Nature 493: 475-476.

Greening, L. A., Greene, D. L. and Difiglio, C. 2000.Energy efficiency and consumption - the rebound effect - a survey. Energy Policy 28: 389-401.

Grepperud, S. and Rasmussen, I. 2004. A general equilibrium assessment of rebound effects. Energy Economics 26: 261-282.

Howarth, R. B. 1997. Energy efficiency and economic growth. Contemporary Economic Policy 25: 1-9.

Linares, P. and Labandeira, X. 2010. Energy efficiency: Economics and policy. Journal of Economic Surveys 24(3): 583-592.

Roy, J. 2000. The rebound effect: some empirical evidence from India. Energy Policy 28: 433-438.

Saunders, H. D. 1992. The Khazzoom-Brookes postulate and neoclassical growth. Energy Journal 13(4): 131-148.

Saunders, H. D. 2008. Fuel conserving (and using) production functions. Energy Economics 30: 2184–2235.

Saunders, H. D. 2013. Historical evidence for energy efficiency rebound in 30 US sectors and a toolkit for rebound analysts. Technological Forecasting & Social Change 80 (2013) 1317-1330.

Sorrell, S., Dimitropoulos, J., Sommerville, M. 2009. Empirical estimates of the direct rebound effect: A review. Energy Policy 37: 1356–1371.

Stern, D. I. 2011. The role of energy in economic growth. Annals of the New York Academy of Sciences 1219: 26-51.

Turner, K. 2009. Negative rebound and disinvestment effects in response to an improvement in energy efficiency in the UK economy. Energy Economics 31: 648-666.

Turner, K. 2013. “Rebound” effects from increased energy efficiency: a time to pause and reflect. Energy Journal 34(4): 25-43.

Turner, K. and Hanley, N. 2011. Energy efficiency, rebound effects and the Environmental Kuznets Curve. Energy Economics 33: 722-741.

First Invited Paper Published in Energies Special Issue

I am the editor of a special issue of the open access journal Energies. The special issue is on energy transitions and economic change. I'm happy to announce that the first paper that I invited for the special issue has now already been published. It is by Steve Sorrell and is on the rebound effect.
The first contributed paper has also been published. Others are still in the peer review process and a few we also already rejected.

The model for these special issues is that the editor invites a number of people to contribute papers and there is also a general call for contributions. The journal is publishing papers as they are ready but you can still submit papers up to the 15th July deadline.

Special Issue of Energies: Call for Papers

Energies is an open access journal on all topics relating to energy. I have agreed to be the guest editor of a special issue on energy transitions and economic change. The journal is indexed in both the Web of Science and Scopus with an impact factor of 1.844 (5 year IF = 2.087) and a SNIP of 1.296 (SJR = 0.543). I am looking for contributions on all topics related to energy transitions past, present, and future (the theme of my current funded research project) and related economic changes. I'm looking for a broad interpretation of energy transition to include not just changes in energy carriers used but also in the scale of energy use. Some of the topics that could be covered are:

• economics of new renewable energy technologies
• energy efficiency and the rebound effect
• energy ladder in developing countries
• historical energy transitions (biomass to coal, coal to oil etc.)
• role of energy in economic growth
• energy and climate change
• energy security
• peak oil
• economics of unconventional fossil fuels

 But this is just to give you an idea of the type of papers we are looking for. The deadline for submissions is 15 July 2014 but early submissions that pass the refereeing process before that date will be published before then.

I look forward to some interesting submissions and will update the blog with progress.

World Energy Outlook 2012 and the Rebound Effect

I have been reading the 2012 World Energy Outlook from the IEA.  There is a special focus section of three chapters on the role that energy efficiency improvements could play in reducing greenhouse gas emissions. The report is generally very conservative on estimated uptake of alternative energy and, therefore, efficiency will be needed if there is to be any chance of staying within a 2C trajectory.

There is, however, only one mention of the rebound effect in this whole section in Box 10.2 on p316. Somehow they come up with an estimated rebound effect of only 9%. This is almost certainly an underestimate of the rebound effect. Typical estimates for direct rebound in consumer applications are around 30%, while on the production side and at the macro-level rebound effects can be much larger than this. The report does correctly note that:

"A significant portion of this could avoided by appropriate pricing policy"

A cap on carbon emissions will induce energy efficiency improvements as part of the solution. Though there will still be a rebound effect it can't result in the emissions reduction goal not being met. However, an efficiency policy without a carbon cap is likely to yield disappointing results in my opinion. With a carbon tax, rebound means that the carbon tax would have to be higher than it would be if there was no rebound, I think.

Adele Morris

Adele Morris heads the energy and climate change program at the Brookings Institution. She presented at ANU today on clean energy technology. This covered a lot of the same ground as my lectures in the CRWF 8000 course last semester and again later this semester so the video of her presentation will hopefully be useful for my students when it is available. She also mentioned a paper she coauthored with Warwick McKibbin and Peter Wilcoxen published in the Energy Journal late last year titled: Subsidizing Household Capital: How Does Energy Efficiency Policy Compare to a Carbon Tax?. The paper compares a tax credit for energy efficient household technology with an economy-wide carbon tax. The money raised by the carbon tax is similar to the tax-expenditure of the tax credit. But the effects on emissions are dramatically different. From the abstract:

"This study uses a general equilibrium model to compare environmental and economic outcomes of two policies: (1) a tax credit of 10 percent of the price of household capital that is 20 percent more energy efficient than its unsubsidized counterpart, assuming half of new household investment qualifies for the credit; and (2) a tax starting at $30 ($2007) per metric ton of CO2 rising five percent annually. By 2040, the carbon tax and tax credit reduce emissions by about 60 percent and 1.5 percent, respectively. ... Both policies have similar impacts on the federal budget, but of opposite signs."

The dramatic difference can be put down to the rebound effect. This shows how ineffective energy efficiency policy could be as I have explained before

Why "Jevons Paradox" is an Argument for Stronger Action on Climate Change not Weaker

The 19th century economist William Jevons suggested that improvements in the efficiency of energy use devices could increase energy use rather than reduce it. The more efficient machines etc. would reduce the cost of production thereby increasing the amount demanded and sold and, therefore, the energy used to produce the products. The rebound effect is a modern statement of this idea: "Efficiency improvements will reduce energy use by less than the efficiency improvement". In the case of consumers, the rationale is that efficiency improvements are equivalent to reductions in the price of "energy services" such as heating, lighting, air-conditioning etc. The law of demand tells us that this will increase the demand for these services and, therefore, for the energy used to produce the services. The size of this rebound effect is an empirical question.

There have been some articles in the popular media about Jevons' paradox recently. The Economist argued that we would be better off without lighting efficiency improvements because they will result in increased energy use and hence pollution. Roger Pielke argues that Jevons paradox tells us that we both need to increase energy efficiency and energy supply in the future. On the other hand, the Climate Progress blog tries to debunk the Jevons' paradox while admitting that the rebound effect is real.

Instead, I argue that the rebound/Jevons' effect tells us that the results of direct action on climate change are likely to be disappointing. Efficiency improvements would need to be bigger than the desired savings in energy use. But, by contrast, a cap on carbon use would eliminate the "carbon rebound effect" due to efficiency improvements. We really do need improvements in energy efficiency as part of the solution to climate change and they make us better off. But a carbon price can be part of a more effective policy.

Most Popular Posts of 2010

In case you missed one of my more popular posts from 2010, I'm kicking off 2011 with a list of the top ten hits. Actually, I saw a bunch of other much bigger bloggers doing this and was curious what my most popular posts were:

1. iamscientist. People are obviously very interested in learning more about this science social networking site.

2. 2009 Journal Citation Report Released. And people want to know which journals are highly ranked. Quite a few people asked me in e-mail to send the whole report. That's not possible.

3. Energy Mix and Energy Intensity. Part of my serialization of this paper.

4. The Ecological Economics Critique. Another part of my serialization of this paper.

5. ERA Ranked Journal List is Out. As well as the ranking by ISI people were interested in the Australian Research Council's ranking.

6. World Trade Report 2010.

7. The Rebound Effect. Yet another part of my serialization of this paper.

8. researchgate. Another social networking site.

9. New Controversy on Malaria and Climate Change. The paper on this topic is under review. I haven't put out a working paper version as it's not really an economics paper and working papers aren't the done thing in biology.

10. Job Talk Abstract. Everyone wants to know how to do a job talk. Unfortunately, they won't get much info at this post! This post is more useful.

Energy Efficiency Report Part II

Reading through the report they seem to come to similar conclusions to me on Australia's track record on energy efficiency. The main goal is a 30% reduction in Australia's energy intensity by 2020. This implies an annual reduction of 2.6% per annum. Since 1980 energy intensity has declined by 1.3% per annum so the target is fairly ambitious in seeking to double this historical rate.

The centrepiece policy recommendation is to broaden existing energy efficiency schemes that currently exist in NSW, Victoria, and South Australia to a national energy certificate scheme. Credits would be generated by energy efficiency increasing investments that could then be sold to energy suppliers who would be obligated to improve the energy efficiency of their customer base. An interesting feature of this proposal is that it reduces the "split incentives" faced by renters and landlords. Often, landlords are reluctant to improve energy efficiency because they won't gain the benefits of energy cost savings while renters are ill-informed about the energy cost parameters of alternative rental properties and so don't make choices of where to live and how much rent to pay on that basis. In very tight rental markets there is often little choice anyway on where you can live*. Under the certificate scheme the landlord could sell the credit and the renters gain from the cost savings. Under an energy tax the incentives remain as asymmetric as they are now.

A problem with such schemes is that they seem to ignore the rebound effect. However, rebound effects are usually much less than 100%.

* See search markets