Typo in Directed Technical Change and the British Industrial Revolution

I hate reading my papers after they're published as there is usually some mistake somewhere. Unfortunately, I have to read them to do more research. I just found a typo in our 2021 paper in JAERE. Equation (8) should look like this:

In the published paper, there is a missing Gamma in the second term. 

I also noticed a couple of issues in the text of "Energy quality" published in Ecological Economics in 2010. One is in the introduction and is debatable: "Fuel and energy quality is not neccessarily fixed". This should be or instead of and or are instead of is. But it really isn't important. Then on p1475 we have "How does these measures". Again, not important.

Of course, the error in JAERE is not very important as the third term above is correct in the published paper.

Energy and Development

The first of two book chapters for Elgar encyclopedias I recently wrote.

What is the Role of Energy in Economic Activity?

The economic system must operate within the constraints determined by the laws of physics and human knowledge of technology. Production, including household production, requires energy to carry out work to convert materials into desired products and to transport raw materials, goods, and people. The second law of thermodynamics implies that energy cannot be recycled and that there are limits to how much energy efficiency can be improved. Therefore, energy is an essential factor of production, and continuous supplies of energy are needed to maintain existing levels of economic activity as well as to grow and develop the economy (Stern, 1997). The first law of thermodynamics states that energy cannot be created and so energy (and matter) must be extracted from the environment. Also, energy must be invested in order to capture useful energy (Hall et al., 1986). Before the Industrial Revolution, economies depended on energy from agricultural crops and wood as well as a smaller amount of wind and waterpower, all of which are directly dependent on the sun (Kander et al., 2015). This is still largely the case in the rural areas of the least developed countries. While solar energy is abundant and inexhaustible, it is very diffuse compared to concentrated fossil fuels. This is why the shift to fossil fuels in the Industrial Revolution relaxed the constraints on energy supply and, therefore, on production and growth (Wrigley, 1988).

How Does Energy Use Change with Economic Development?

Figure 1 shows that energy use per capita increases with GDP per capita, so that richer countries tend to use more energy per person than poorer countries. The slope of the logarithmic regression line implies that a 1% increase in income per capita is associated with a 0.8% increase in energy use per capita. As a result, energy intensity – energy used per dollar of GDP – is on average lower in higher income countries. These relationships have been very stable over the last several decades (Csereklyei et al., 2016). Energy intensity in today’s middle-income countries is similar to that in today’s developed countries when they were at the same income level (van Benthem, 2015).

Figure 1. GDP and Energy Use per Capita 2018

Energy intensity has also converged across countries over time, so that countries that were more energy intensive in the 1970s tended to reduce their energy intensity by more than less energy intensive countries, and the least energy intensive countries often increased in energy intensity. Though data are limited to fewer and fewer countries as we go back further in time, these relationships also appear to hold over the last two centuries – energy use increased, energy intensity declined globally, and countries converged in energy intensity (Csereklyei et al., 2016). Though data is even more limited, it seems that the share of energy consumption expenditure and production costs also declines as countries develop (Csereklyei et al., 2016; Burke et al., 2018).

The mix of fuels used changes over the course of economic development. Figure 2 shows the average mix of energy sources in each of five groups of countries ordered by income per capita in 2018. In the lowest income countries in the sample (approximately below $5,000 per capita in 2017 purchasing power parity adjusted dollars), traditional use of biomass such as wood and agricultural waste dominates and oil use for transportation as well as electricity generation and other uses is the second most important energy source. As we move to richer countries, the relative role of biomass declines radically, and first oil and then natural gas and primary electricity increase in importance. Note that biomass use per capita in the richest quintile (above $40,000 per capita) is actually greater than in the lowest quintile, as total energy use increases with income. The ways in which this biomass is used will of course be quite different. Higher quality fuels are those that provide more economic value per joule of energy content by being converted more efficiently, being more flexible or convenient to use, and by producing less pollution. We would expect that lower income households would be more willing to tolerate the inconvenience and pollution caused by using lower quality fuels to produce energy services. So as household income increases, we would expect households to gradually ascend an “energy ladder” by consuming higher quality fuels and more total energy. Recent studies often find a more ambiguous picture where multiple fuels are used simultaneously as modern fuels are added to the use of traditional fuels (Gregory and Stern, 2014). 

Figure 2. Fuel Mix and Development 2018

 

In 2016, approximately one billion people remained without access to electricity at home (International Energy Agency, 2017). Around 85% of these people lived in rural areas. There has been rapid progress in electrification in recent years with both grid expansion and the spread of off-grid systems (Burke et al., 2018; Lee et al., 2020). Due to the complexity and costs of electricity-sector management and constrained and weak institutions, power supply is usually less reliable in developing countries than in developed countries (Figure 3) and electricity theft is also more common (Burke et al., 2018). Best and Burke (2017) found that countries with higher levels of government effectiveness have achieved greater progress in providing access to reliable electricity. Industry and other electricity consumers, therefore, often rely on self-generation of electricity, but this is a costly solution (Fingleton-Smith, 2020). 

 Figure 3. Electricity Reliability and Development 2017

 

Does Energy Use Drive Economic Growth?

Economic growth refers to the process that results in increasing GDP per capita over time while development refers to a broader range of indicators including health, education, and other dimensions of human welfare. However, GDP per capita is highly, although not perfectly, correlated with broader development measures (Jones and Klenow, 2016) and so it is worth considering what the role of energy is in economic growth.

Mainstream economic growth models largely ignore the role of energy in economic growth and focus on technological change as the long-run driver of growth. On the other hand, there is a resource economics literature that investigates whether limited energy or other resources could constrain growth. By contrast, many ecological economists believe that energy plays the central role in driving growth and point to the switch traditional energy sources to fossil fuels as the cause of the industrial revolution (Stern, 2011). 

To reconcile these opposing views, Stern and Kander (2012) modified Solow’s neoclassical growth model (Solow, 1956) by adding an energy input that has low substitutability with capital and labor. Their model also breaks down technological change into those innovations that directly increase the productivity of energy– energy-augmenting technical change and those that increase the productivity of labor – labor-augmenting technical change. In this model, when energy is superabundant the level of the capital stock and output are determined by the same functions of the same factors as in the Solow model. But when energy is relatively scarce, the size of the capital stock and the level of output depends on the level of energy supply and the level of energy-augmenting technology. Therefore, in the pre-industrial era and possibly when energy was scarce – and possibly in developing countries today – the level of output was determined by the supply of energy and the level of energy augmenting technology. Until the industrial revolution, output per capita was generally low and economic growth was not sustained (Maddison, 2001). After the industrial revolution, as energy became more and more abundant, the long-run behavior of the model economy becomes more and more like the Solow growth model. If this model is a reasonable representation of reality, then mainstream economists are not so wrong to ignore the role of energy in economic growth in developed economies where energy is abundant, but their models have limited applicability to both earlier historical periods and possibly to today’s developing countries. McCulloch and Zileviciute (2017) find that electricity is often cited as a binding constraint on growth in the World Bank’s enterprise surveys. Energy is expensive relative to wages in developing countries. The price of oil is set globally, and the share of electricity in costs or expenditures can be very high in middle income countries (Burke et al., 2018).

Electricity and Development

Access to energy and electricity, in particular, is a key priority for policymakers and donors in low-income countries. For example, the United Nations’ Sustainable Development Goal 7 targets universal access to modern energy by 2030. Electrification can allow poor households to have easy access to lighting for evening chores or studying and power for phone charging and for a range of new small business activities, both on and off the farm (Lee et al., 2020). Electricity access allows a reallocation of household time, especially for women, away from obtaining energy, for example by collecting firewood, and towards more productive activities. Electricity could also provide health benefits by allowing deeper wells, refrigeration, reduced exposure to smoke etc. (Toman and Jemelkova, 2003).

The micro-level effect of electrification is a growing area of empirical research (Lee et al., 2020). While micro studies typically suggest positive impacts of electrification on income and other development outcomes, more recent quasi-experimental approaches such as randomized controlled trials typically find a smaller impact for electrification than earlier studies did (Lee et al., 2020). Estimates of the effect of electricity infrastructure on economic growth are typically small. One of the best studies (Calderón et al., 2015) estimates the elasticity of GDP with respect to electricity generation capacity as 0.03 (Burke et al., 2018).

Lee et al. (2020) argue that providing poor households with access to electricity alone is not enough to improve economic and noneconomic outcomes in a meaningful way. Complementary inputs are needed, which will accumulate very slowly. Imagination and role models are also important in understanding how to exploit electricity to develop businesses (Fingleton-Smith, 2020). When electricity becomes available in rural areas of sub-Saharan Africa, it is often not used to power agricultural or other productive activities (Bernard, 2012). Institutions are also vital for attaining broad-based benefits from electricity in developing countries. Many developing countries have reformed their electricity sectors during the last few decades, mostly towards market liberalization and corporatization. These efforts have only been partially successful in promoting efficient pricing and greater electricity access (Jamasb et al., 2017). Studies assessing the economic effects of these reforms are scarce. The effects on economic growth seem positive, while the effects on poverty are mixed (Jamasb et al., 2017). In this context, technology transfer and development finance will be critical for increasing the use of electricity in developing countries (Madlener, 2009).

Burke et al. (2018) examined electrification success stories - countries that, from a low level of economic development, have now achieved near-universal electricity access as well as relatively high levels of electricity use. These countries are South Korea, China, Thailand, Vietnam, Egypt, and Paraguay. The first four are well-known development success stories too. Paraguay has abundant hydroelectricity and both Paraguay and Egypt have had relatively strong economic growth. Egypt has been less successful in providing a reliable electricity supply. The most successful countries in increasing access in Sub-Saharan Africa have been South Africa and Ghana, which both suffer from unreliable electricity, which constrains economic activity.

References

Bernard, T., 2012. Impact analysis of rural electrification projects in Sub-Saharan Africa. World Bank Research Observer 27(1): 33–51.

Best, R., and P. J. Burke, 2017. The importance of government effectiveness for transitions toward greater electrification in developing countries. Energies 10(9): 1247.

Burke P. J., D. I. Stern, and S. B. Bruns, 2018. The impact of electricity on economic development: a macroeconomic perspective. International Review of Environmental and Resource Economics 12(1): 85–127.

Calderón, C., E. Moral-Benito, and L. Servén, 2015. Is infrastructure capital productive? A dynamic heterogeneous approach. Journal of Applied Econometrics 30: 177–198.

Csereklyei Z., M. d. M. Rubio Varas, and D. I. Stern, 2016. Energy and economic growth: The stylized facts. Energy Journal 37(2): 223–255.

Fingleton-Smith, E., 2020. Blinded by the light: The need to nuance our expectations of how modern energy will increase productivity for the poor in Kenya. Energy Research & Social Science 70: 101731.

Gregory, J. and D. I. Stern, 2014. Fuel choices in rural Maharashtra. Biomass and Bioenergy 70: 302–314.

Hall, C. A. S., C. J. Cleveland, and R. K. Kaufmann, 1986. Energy and Resource Quality: The Ecology of the Economic Process. New York: Wiley Interscience.

International Energy Agency, 2017. Energy Access Outlook 2017: From Poverty to Prosperity. World Energy Outlook Special Report.

Jamasb, T., R. Nepal, and G. R. Timilsina, 2017. A quarter century effort yet to come of age: a survey of electricity sector reform in developing countries. Energy Journal 38(3): 195–234.

Jones, C. I., and P. J. Klenow. 2016. Beyond GDP? Welfare across countries and time. American Economic Review 106(9): 2426–2457.

Kander, A., P. Malanima, and P. Warde, 2014. Power to the People: Energy in Europe over the Last Five Centuries. Princeton University Press.

Lee, K., E. Miguel, and C. Wolfram, 2020. Does household electrification supercharge economic development? Journal of Economic Perspectives 34(1): 122–144.

Maddison, A., 2001. The World Economy: A Millennial Perspective. Paris: OECD.

Madlener, R., 2009. The economics of energy in developing countries. In: L. C. Hunt and J. Evans (eds.), International Handbook on the Economics of Energy, Edward Elgar.

McCulloch, N., and D. Zileviciute, 2017. Is electricity supply a binding constraint to economic growth in developing countries? EEG State-of-Knowledge Paper Series 1.3.

Solow, R. M., 1956. A contribution to the theory of economic growth. Quarterly Journal of Economics 70: 65–94.

Stern, D. I., 1997. Limits to substitution and irreversibility in production and consumption: a neoclassical interpretation of ecological economics. Ecological Economics 21: 197–215.

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

Stern, D. I., and A. Kander, 2012. The role of energy in the industrial revolution and modern economic growth. Energy Journal 33(3): 125–152.

Toman, M. A., and B. Jemelkova, 2003. Energy and economic development: An assessment of the state of knowledge. Energy Journal 24(4): 93–112.

van Benthem, A. A., 2015. Energy leapfrogging. Journal of the Association of Environmental and Resource Economists 2(1): 93–132.

Wrigley, E. A., 1988. Continuity, Chance, and Change: The Character of the Industrial Revolution in England. Cambridge: Cambridge University Press.

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.

 

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.

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.

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.

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.

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.

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.

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.

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.

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.

Energy Intensity, Growth, and Technical Change

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

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

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

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

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

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

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

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

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

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

Replicating Stern (1993)

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

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

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

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

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

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

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

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

Explaining Malthusian Sluggishness

I'm adding some more intuition to our paper on the Industrial Revolution. I have a sketch of the math but still need to work out the details. Here is the argument:

"In this section, we show that both Malthusian Sluggishness and Modern Economic Growth are characterized by a strong equilibrium bias of technical change (Acemoglu, 2009). This means that in both regimes the long-run demand curve for the resource is upward sloping – higher relative prices are associated with higher demand. Under Malthusian Sluggishness the price of wood is rising relative to coal and technical change is relatively wood-augmenting. At the same time, wood use is rising relative to coal use. Because when the elasticity of substitution is greater than one the market size effect dominates the price effect, technical change becomes increasingly wood-augmenting. As a result, the economy increasingly diverges from the region where an industrial revolution is possible or inevitable. Modern Economic Growth is the mirror image. The price of coal rises relative to the price of wood, but coal use increases relative to wood use and technical change is increasingly coal-augmenting."

The Impact of Electricity on Economic Development: A Macroeconomic Perspective

I have a new working paper out, coauthored with Paul Burke and Stephan Bruns. The paper is one of those commissioned for the first year of the Energy for Economic Growth program, which is funded by the UK Department for International Development and directed by Catherine Wolfram.  The paper was actually completed in January 2017, but there has been a lot of delay in getting approval for publication. The project and the paper focuses on the role of electricity in economic development in Africa and South Asia, the two regions of the world where electricity is least accessible.

Access to and consumption of electricity varies dramatically around the world. Access is lowest in South Sudan at 4.5% of households, while consumption ranges from 39 kWh per capita – this includes all uses of electricity not just household use – in Haiti to 53,000 kWh per capita in Iceland – driven by aluminum smelting. Consumption is 13,000 kWh per capita in the US. Electricity use and access are strongly correlated with economic development, as theory would suggest:

Access and consumption have increased strongly in many poorer countries in recent years – will this have beneficial effects on development? The specific questions that DFID asked us to answer were:

• How serious do electricity supply problems have to be in order to constitute a serious brake on economic growth?

• To what degree has electrification prolonged or accelerated economic growth?

• What can be learned from the development experience of countries that have invested successfully in electrification?

In principle, it should be easier to find evidence for causal effects using more disaggregated micro level data as some variables can more easily be considered exogenous, and randomized trials and other field experiments are possible. On the other hand, growth is an economy-wide, dynamic, and long-term process with effects that cannot usually be captured in micro studies. Therefore, macroeconomic analysis is also needed. Our paper is complemented by a paper covering the microeconomic aspects of these questions.

Despite large empirical literatures – such as that on testing for Granger casuality between electricity use and economic growth – and suggestive case evidence, we found few methodologically strong studies that establish causal effects for electricity use, access, infrastructure, or reliability on an economy-wide basis. The best such study that we found is a paper by Calderon et al. in the Journal of Applied Econometrics. But this paper actually tests the effects of an aggregate of different types of infrastructure on growth.

We propose that future research focuses on identifying the causal effects of electricity reliability, infrastructure, and access on economic growth; testing the replicability of the literature; and deepening our theoretical understanding of how lack of availability of electricity can be a constraint to growth.

Data and Code for "Energy and Economic Growth: The Stylized Facts" and an Erratum

Following a request for our estimation code we have now completed a full replication package for our 2016 Energy Journal paper and uploaded it to Figshare.

While we were putting this together we noticed some minor errors in the tables in the published paper. The reported standard errors of the coefficients of lnY/P in Tables 2 and 3 for the results without outliers are incorrect. We accidentally pasted the standard errors from Table 5 into Tables 2 and 3. The correct versions of Tables 2 and 3 should look like this:

The standard errors for unconditional convergence in Tables 4 and 6 are also incorrect. The reported standard errors are not robust and one was completely wrong. The tables should look like:

None of these errors results in the significance level in terms of 1%, 5% etc. changing.

Barcelona Talk

I'll be giving a presentation in the "distinguished speakers series" at ICAT, Autonomous University of Barcelona on 5th December. I just wrote the abstract:

The Role of Energy in the Industrial Revolution and Modern Economic Growth

Abstract: Ecological and mainstream economists have debated the importance of energy in economic growth. Ecological economists usually argue that energy plays a central role in growth, while mainstream economists usually downplay the importance of energy. Using the (mainstream) theory of directed technological change, I show how increasing scarcity of biomass could induce coal-using innovation in Britain, resulting in the acceleration in the rate of economic growth known as the Industrial Revolution. Paradoxically, industrialization would be delayed in countries with more abundant biomass resources. However, as energy has become increasingly abundant, the growth effect of additional energy use has declined. Furthermore, both directed technological change theory and empirical evidence show that innovation has increasingly focused on improving the productivity of labor rather than that of energy. This explains the focus of mainstream economic growth models on labor productivity enhancing innovation as the driver of economic growth.

The paper will draw on my 2012 paper with Astrid Kander – it shares the same title after all – my recent working paper with Jack Pezzey and Yingying Lu, and maybe my ongoing work with Akshay Shanker on understanding trends in energy intensity in the 20th and 21st Centuries. The talk is for an interdisciplinary audience, so that will be challenging, but I think I can do it :)