Thursday, December 30, 2021

Annual Review 2021

I've been doing these annual reviews since 2011. They're mainly an exercise for me to see what I accomplished and what I didn't in the previous year. 

This was the first year since I have been back living in Canberra in 2007 that I spent the entire year in the Canberra region. In fact, it is the first year since 1991 that I didn't fly on a plane. It's not that unusual for me not to leave my country for a year. I didn't travel outside of Australia in 2019. This year, with a two year old and random snap lockdowns happening in the first part of the year, we were not in the mood to travel anywhere overnight even when it was possible. Then from mid-August came the second Canberra lockdown for 2 plus months (the first was during the first wave of the pandemic in March-May 2020). Luckily I was able to take Isaac (the two year old) to daycare throughout the lockdown, but we had to help homeschool Noah (5 years old). I was impressed how well the school organized things. Before Omicron came along, things had returned almost to normality in Canberra. We still needed to wear masks at the daycare and on the bus and needed to check in sometimes at stores etc. The university has been dragging its feet on the return to campus, but the faculty office areas in the Crawford Building have sometimes been even a little bit busy. In the last week of the year, I finally travelled out of Canberra with my family to go on holidays on the NSW South Coast.

While we've been away from Canberra the number of COVID-19 cases has been growing radically. Almost everyone here is vaccinated and Omicron seems less severe, so it's unclear what this will mean for university activity in 2022. They were planning a more or less complete return to on campus teaching, but who knows now...
In Semester 1 (from February to June), I again taught environmental economics and the masters research essay course. But this is the last time I will be teaching them. More about that in the 2022 predictions, below. We taught in hybrid mode. In the environmental economics class there was a joint online lecture for online and on campus students and then separate tutorials for the two groups. It turned out that very few people came to the in person tutorial. Often I had only one student. But this session was much better in my opinion than either of the online sessions. The masters research essay class had separate online and in-person classes.
I was awarded a Francqui Chair at the University of Hasselt in Belgium for the 2020-21 academic year. The main duty of the position was to give ten hours of lectures. Of course, I didn't actually travel to Belgium and so I gave five online lectures. You can see the videos and read some commentary on my blog.

I can't really think of anything notable to say about my research activity this year. It's mostly been a story of completing existing projects. We finally wrapped up our ARC DP12 project (yes, funding started in 2012 and we submitted the proposal in 2011) with the publication of our paper on the Industrial Revolution in JAERE.

I started working on several new ideas in the second half of the year but they don't seem to be going anywhere or have already been abandoned. The exception is our asymmetry paper, which we started thinking about right at the end of 2020 and now is under review.

We published five papers with a 2021 date:

Stern D. I., J. C. V. Pezzey, and Y. Lu (2021) Directed technical change and the British Industrial Revolution, Journal of the Association of Environmental and Resource Economists 8(6), 1079-1114.

Saunders H., J. Roy, I. Azevedo, D. Chakravarty, S. Dasgupta, S. de la rue du Can, A. Druckman, R. Fouquet, M. Grubb, B.-Q. Lin, R. Lowe, R. Madlener, D. McCoy, L. Mundaca, T. Oreszczyn, S. Sorrell, D. I. Stern, K. Tanaka, and T. Wei (2021) Energy efficiency: What has research delivered in the last 40 years? Annual Review of Environment and Resources 46, 135-165.

Dressel B. and D. I. Stern (2021) Research at public policy schools in the Asia-Pacific region ranked, Asia and the Pacific Policy Studies 8(1), 151-166.

Stern D. I. and R. S. J. Tol (2021) Depth and breadth relevance in citation metrics, Economic Inquiry 59(3), 961-977.

Bruns S. B., A. Moneta, and D. I. Stern (2021) Estimating the economy-wide rebound effect using empirically identified structural vector autoregressions, Energy Economics 97, 105158.

and one paper with a 2022 date:

Jafari M., D. I. Stern, and S. B. Bruns (2022) How large is the economy-wide rebound effect in middle income countries? Evidence from Iran, Ecological Economics 193, 107325.

We posted four new working papers:

How Much Does Physical Infrastructure Contribute to Economic Growth? An Empirical Analysis
December 2021. With Govinda Timilsina and Debasish Das.

Asymmetric Response of Carbon Emissions to Recessions and Expansions and Oil Market Shocks
October 2021. With Xueting Jiang.

How Large is the Economy-Wide Rebound Effect in Middle Income Countries? Evidence from Iran
August 2021. With Mahboubeh Jafari and Stephan Bruns.

Do Energy Efficiency Improvements Reduce Energy Use? Empirical Evidence on the Economy-Wide Rebound Effect in Europe and the United States
May 2021. With Anne Berner, Stephan Bruns, and Alessio Moneta.

We have three papers under review at the moment (one – the Europe rebound one – is a resubmission). There are twelve other papers on my to do list, but they range from one we are actively trying to complete, to ones that I haven't really done anything on any time recently.

Google Scholar citations exceeded 21,000 with an h-index of 55. I wrote more blogposts this year. Fifteen in total compared to ten in 2020. Twitter followers rose from 1500 to more than 1650 over the year. I did 3 external assessments of people for promotion or tenure for universities in Australia, Hong Kong, and Germany. Fewer than last year. I only did 11 reviews for journals. I used to do around double this three or more years back. And I reviewed a bunch of papers for EAERE, a proposal for the ARC, as well as giving people feedback on their papers etc.

My PhD student Xueting Zhang completed her first research year. She has made a lot of progress, with three papers at various stages of completion. My other student Debasish Das continued his work on prepaid metering and a lot of other stuff, some of which you can check out on his Google Scholar profile.

Looking forward to 2022, a few things can be predicted: 

  • I will be teaching a new course (for me) in the second semester: Agricultural and Resource Economics. It is going to take a lot of work to prepare this course. 
  • As a result, I won't be teaching in the first semester. Officially, I will be on long service leave, which is how I got my teaching reduced to one course for the year. But I will need to work hard on both the course and research right from the start of the year. OK, I'm feeling like taking 4th January off :) The university has encouraged us to take long service leave to help the budget situation. Taking the leave releases money from the account where it has been set aside and they don't need to pay my salary from the recurrent budget.
  • I'm hoping we will get our paper on the rebound effect in Europe accepted very soon.
  • I probably will stay in Australia for this year too. Anyway, I haven't set up any international travel at this stage.

Tuesday, December 21, 2021

Estimating the Effect of Physical Infrastructure on Economic Growth

I have a new working paper coauthored with Govinda Timilsina of the World Bank and my PhD student Debasish Das. It is a panel data study of the effect of various forms of infrastructure on the level of GDP. 

Compared to existing studies, we use more recent data, include new types of infrastructure such as mobile phones, and provide separate estimates for developing and developed countries. We find larger effects than most previous studies. We also find that infrastructure has a larger effect in more recent years (1992-2017) than in earlier years (1970-1991), and the effects of infrastructure are higher in developing economies than in industrialized economies. The long-run effects seem to be much larger than the initial impact. We also tried to estimate the effect of infrastructure on the rate of economic growth. Controlling for the initial level of GDP per worker we found a null result. So, we can't say that having more infrastructure means a more rapid rate of economic growth.

Getting good quality data that is comparable across countries is really a problem in this area of research. Many types of infrastructure only have data available for a few years. The ones that have more panel-like data often suffer from differences in definition across countries – such as what is a road or a motorway – or unexplained jumps in individual countries. So, our results are subject to a lot of measurement error.

Our main analysis uses data on five types of infrastructure – roads, railways, electric generation capacity, fixed line telephones, and mobile telephones*:

Following some previous research, we aggregate the individual types of infrastructure using principal component analysis. We use two principal components. One factor seems to be related to transport infrastructure and the other to electricity and telecommunications. Still, we can recover estimates of the effect of each individual type of infrastructure.

Also following some previous research, we use the Pooled Mean Group estimator to estimate a dynamic panel regression model. This allows us to test for the weak exogeneity of the explanatory variables, allowing us to give the results a somewhat causal interpretation.

The table shows the percentage change in GDP per worker for a 1% change in each infrastructure type. Getting standard errors for these estimates would be rather tricky.** Interestingly, the PMG estimates are mostly much larger than the static fixed effects estimates. Static fixed effects can be expected to converge to a short-run estimate of the effects while PMG should be a better estimate of long-run effects. Fixed effects also tends to inflate the effects of measurement error

Maybe the most innovative thing in the paper is that we plot the impulse response functions of GDP with respect to a 1% increase in each of the two main types of infrastructure:

PC1 is electricity and communications and PC2 transport infrastructure.*** Long-run effects of infrastructure are much larger than the short-run effects. In the short run, transport infrastructure even has a negative impact.

* Note that the graphs show the country means of these variables, while we actually use the deviations from those means over time in each country

** We only estimate the GDP-infrastructure relationship, but I think we would need time series models for each of the explanatory variables in order to sample from those models' residuals in a bootstrapping procedure. Bootstrapping is needed because we first carry out the principal components analysis and then estimate the PMG model in a second stage. These elasticities are combinations of the parameters from those two models.

*** We could get a confidence interval for these impulse response functions if we assume that the explanatory variables in the PMG model are deterministic as this analysis assumes...

Thursday, October 21, 2021

The Environmental Kuznets Curve: 2021 Edition

The second encyclopedia chapter. First one is here.


The environmental Kuznets curve (EKC) is a hypothesized relationship between various indicators of environmental degradation and countries’ gross domestic product (GDP) per capita. In the early stages of a country’s economic development, environmental impacts and pollution increase, but beyond some level of GDP per capita (which will vary for different environmental impacts) economic growth leads to environmental improvement. This implies that environmental impacts or emissions per capita are an inverted U-shaped function of GDP per capita, whose parameters can be statistically estimated. Figure 1 shows a very early example of an EKC. A large number of studies have estimated such curves for a wide variety of environmental impacts ranging from threatened species to nitrogen fertilizers, though atmospheric pollutants such as sulfur dioxide and carbon dioxide have been investigated most. Panayotou (1993) was the first to call this relationship the EKC, where Kuznets refers to the similar relationship between income inequality and economic development proposed by Nobel Laureate Simon Kuznets and known as the Kuznets curve. The EKC can be seen as an empirical version of the interpretation of sustainable development as the idea that development is not necessarily damaging to the environment and, also that poverty reduction is essential to protect the environment (World Commission on Environment and Development, 1987).

Figure 1. An Environmental Kuznets Curve 

The EKC has been the dominant approach among economists to modeling ambient pollution concentrations and aggregate emissions since Grossman and Krueger (1991) introduced it in an analysis of the potential environmental effects of the North American Free Trade Agreement. The EKC also featured prominently in the 1992 World Development Report published by the World Bank and has since become very popular in policy and academic circles and is even found in introductory economics textbooks. 


Despite this, the EKC was criticized almost from its beginning on empirical and policy grounds, and debate continues. It is undoubtedly true that some dimensions of environmental quality have improved in developed countries at the same time that they have become richer. City air and rivers in these countries have become cleaner since the mid-20th Century and, in some countries, forests have expanded. Emissions of some pollutants such as sulfur dioxide have clearly declined in most developed countries in recent decades. But there is more mixed evidence for other pollutants such as carbon dioxide. Carbon emissions have fallen in the last 40 years in some developed countries such as the United Kingdom or Sweden, while they have increased in others such as Australia or Japan. There is also evidence that emerging countries take action to reduce severe pollution. For example, Japan cut sulfur dioxide emissions in the early 1970s following a rapid increase in pollution when its income was still below that of the developed countries (Stern, 2005) and China has also acted to reduce sulfur emissions in recent years. As further studies were conducted and better data accumulated, many of the econometric studies that supported the EKC were found to be statistically fragile. 

Initially, many understood the EKC to imply that the best way for developing countries to improve their environment was to get rich (e.g. Beckermann, 1992). This alarmed others (e.g. Arrow et al., 1995), as while this might address some issues like deforestation or local air pollution, it would likely exacerbate other environmental problems such as climate change. Even if there is an EKC for per capita impacts, environmental impacts would increase for a very long time if the majority of the population is on the rising part of the curve and/or the population is also growing (Stern et al., 1996). 


The existence of an EKC can be explained either in terms of deep determinants such as technology and preferences or in terms of scale, composition, and technique effects, also known as “proximate factors”. Scale refers to the effect of an increase in the size of the economy, holding the other effects constant, and should increase environmental impacts. The composition and technique effects must outweigh this scale effect for pollution or other environmental impacts to fall in a growing economy. The composition effect refers to the economy’s mix of different industries and products, which differ in pollution intensities. Finally, the technique effect refers to the remaining change in pollution intensity. This will include contributions from changes in the input mix, for example substituting natural gas for coal; changes in productivity that result in less use, ceteris paribus, of polluting inputs per unit of output; and pollution control technologies that result in less pollutant being emitted per unit of polluting input. 

Over the course of economic development, the mix of energy sources and economic outputs tends to evolve in predictable ways. Economies start out mostly agricultural and the share of industry in economic activity first rises and then falls as the share of agriculture declines and the share of services increases. We might expect the impacts associated with agriculture, such as deforestation, to decline, and naively expect the impacts associated with industry, such as pollution, would first rise and then fall. However, the absolute size of industry rarely does decline, and it is improvement in productivity in industry, a shift to cleaner energy sources, such as natural gas and hydro-electricity, and pollution control that eventually reduce some industrial emissions. On the other hand, offshoring of pollution probably plays only a small role in cutting emissions in developed economies (Kander et al., 2015). 

Static theoretical economic models of deep determinants, that do not try to also model the economic growth process, can be summarized in terms of two parameters: The elasticity of substitution between dirty and clean inputs, which summarizes how difficult it is to cut pollution; and the elasticity of the marginal utility of consumption with respect to consumption, which summarizes how hard it is to increase consumer well-being with more consumption (Pasten and Figeroa, 2012). It is usually assumed that these consumer preferences are translated into policy action. Pollution is then more likely to increase as the economy expands, the harder it is to substitute other inputs for polluting ones and the easier it is to increase consumer well-being with more consumption. If these parameters are constant, then either pollution rises or falls with economic growth. Only if they change over time will pollution first rise and then fall. The various theoretical models can be classified as ones where the EKC is driven by changes in the elasticity of substitution as the economy grows or models where the EKC is primarily driven by changes in the elasticity of marginal utility. 

Dynamic models that model the economic growth process alongside changes in pollution are harder to classify. The Green Solow Model developed by Brock and Taylor (2010) explains changes in pollution as a result of the competing effects of economic growth and a constant rate of improvement in pollution control. Fast growing middle-income countries, such as China, then having rising pollution, and slower growing developed economies, falling pollution. An alternative model developed by Ordás Criado et al. (2011) also suggests that pollution rises faster in faster growing economies but that there is also convergence so that countries with higher levels of pollution are more likely to reduce pollution faster than countries with low levels of pollution. 

Recent Empirical Research 

Recent empirical research builds on these dynamic models to paint a subtler picture than early EKC studies did (Stern, 2017). We can distinguish between the impact of economic growth on the environment and the effect of the level of GDP per capita, irrespective of whether an economy is growing or not, on reducing environmental impacts. We can also distinguish between the effects of economic growth and the simple passage of time. Economic growth usually increases environmental impacts, but the size of this effect varies across impacts and the impact of growth often declines as countries get richer. However, richer countries are often likely to make more rapid progress in reducing environmental impacts. In econometric terms, the time effect – the change in emissions if economic growth is zero – may be higher in richer countries. Rapid growth in middle-income countries, such as China or India, is more likely to overwhelm the time effect in those countries as suggested by Brock and Taylor (2010).

Finally, there is often convergence among countries, so that those that have relatively high levels of impacts reduce them faster or increase them slower than countries with low levels of impacts. These combined effects explain more of the variation in pollution emissions or concentrations than either the classic EKC model or models that assume that either only convergence or growth effects alone are important. Therefore, while being rich means a country might do more to clean up its environment, getting rich is likely to be environmentally damaging. 


Arrow, K., Bolin, B., Costanza, R., Dasgupta, P., Folke, C., Holling, C. S., Jansson, B.-O., Levin, S., Mailer, K.-G., Perrings, C., Pimental, D., 1995. Economic growth, carrying capacity, and the environment. Science 268, 520–521. 

Beckermann, W., 1992. Economic growth and the environment: whose growth? Whose environment? World Development 20, 481–496. 

Brock, W. A.,Taylor, M. S., 2010. The green Solow model. Journal of Economic Growth 15, 127–153. 

Grossman, G. M., Krueger, A. B., 1991. Environmental impacts of a North American Free Trade Agreement. NBER Working Papers 3914. 

Kander, A., Jiborn, M., Moran, D. D., Wiedmann T. O., 2015. National greenhouse-gas accounting for effective climate policy on international trade. Nature Climate Change 5, 431–435. 

Ordás Criado, C., Valente, S., Stengos, T., 2011. Growth and pollution convergence: Theory and evidence. Journal of Environmental Economics and Management 62, 199–214. 

Panayotou, T., 1993. Empirical tests and policy analysis of environmental degradation at different stages of economic development. Working Paper, Technology and Employment Programme, International Labour Office, Geneva, WP238. 

Pasten, R., Figueroa, E., 2012. The environmental Kuznets curve: A survey of the theoretical literature. International Review of Environmental and Resource Economics 6, 195–224. 

Stern, D. I., 2005. Beyond the environmental Kuznets curve: Diffusion of sulfur-emissions-abating technology. Journal of Environment and Development 14(1), 101–124. 

Stern, D. I., 2017. The environmental Kuznets curve after 25 years. Journal of Bioeconomics 19, 7–28.

Stern, D. I., Common, M. S., Barbier, E. B., 1996. Economic growth and environmental degradation: the environmental Kuznets curve and sustainable development. World Development 24, 1151–1160. 

World Commission on Environment and Development, 1987. Our Common Future. Oxford: Oxford University Press.  

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.


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.

Wednesday, October 20, 2021

Our COVID-19 Paper

Publishing papers on COVID-19 is very popular: 

and we couldn't resist joining the bandwagon. Late last year, Xueting Jiang, my PhD student, and I did a quick literature survey to identify a gap. Though there was a lot of research on how pollution emissions evolved over the course of the pandemic and recession, there was little putting that into the historical context of past recessions. Last year, I worked with Kate Martin, a masters student, on the relationship between carbon emissions and economic activity over the business cycle. We decided to extend that analysis. 

Our new paper uses U.S. monthly data from January 1973 to December 2020. We look at how the relationship between carbon emissions and GDP varies between recessions and expansions, but we also look at individual recessions and how emissions from different sectors vary over the business cycle. 

Like Sheldon and others, we find that, in general, the emissions-GDP elasticity is greater in recessions than in expansions, but we find that this is largely because of sharp falls in emissions associated with negative oil market shocks. The 1973-5, 1980, and 1990-1 recessions were associated with negative oil supply shocks. In 2020, there was instead a negative oil demand shock due to the pandemic. These recessions have emissions-GDP elasticities that are significantly larger than the elasticity in expansions. The elasticities in the 1981-2, 2001, and 2008-9 recessions are no larger than in expansions.

The graph shows NBER recessions in light blue stripes and nominal and real oil prices. The big spike in oil prices in 2008 came at the end of an extended increase associated with rising demand for oil. Of course, supply was constrained during this period but there wasn't a sudden supply crisis. In 1981-82 the price of oil was already falling when the recession started and it is usually regarded as having been caused by the Federal Reserve under Paul Volcker dramatically raising interest rates.

When we regress the growth of sectoral carbon emissions on the growth of national GDP, we find that the asymmetry is present in the industrial and particularly in the transport sector, which are the two largest users of oil in the US economy, using 28% and 66% of the total, respectively.

When we control for oil use, the asymmetries disappear. 

So, though the cause of the COVID-19 recession was unusual the carbon emissions outcome was similar to past recessions associated with oil crises. More importantly, we learned something new about what happens to emissions in recessions, at least in the US.

Tuesday, October 12, 2021

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


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

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


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

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

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

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

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

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

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


Friday, August 6, 2021

Data for "Interfuel Substitution: A Meta-Analysis"

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

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

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

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

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

Column A: Identification number for each paper.

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

Column M: Year paper was published.

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

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

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

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

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

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

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

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

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

Columns AP to AZ are the specification of the model:

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

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

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

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

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

Column AW: Functional form of the model.

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

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

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

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

Columns BQ to CF: Reported translog cost function parameters.

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

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

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

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

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

Thursday, June 3, 2021

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

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

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

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

This new paper extends this research in two ways:

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

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

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

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

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

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

Wednesday, May 12, 2021

Fifth Francqui Lecture: Econometric Modeling of Climate Change

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

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

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

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

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

Public Policy Schools in the Asia-Pacific Ranked

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

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

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

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

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

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

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

Wednesday, April 28, 2021

Fourth Franqui Lecture: Energy and the Industrial Revolution

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


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

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

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

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

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

Monday, April 5, 2021

Third Francqui Lecture: The Rebound Effect

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

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

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

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

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

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

The "Conclusion" discusses Fullerton and Ta.

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

Wednesday, March 24, 2021

Second Francqui Lecture: Energy and Economic Growth and Development

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

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

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

Friday, March 12, 2021

Inaugural Francqui Lecture: Economic Growth and the Environment

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


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

Monday, February 8, 2021

Energy and Economic Growth: Updated Animation

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


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

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