Stochastic Trend

 

I have a new working paper coauthored with my master's student Suryadeepto Nag on the impact of rural electrification in India. Surya did his master's at IISER in Pune with me as his supervisor. He visited Canberra over the last Southern Summer. This paper is based on part of Surya's thesis.

The effect of providing households with access to electricity has been a popular research topic. It's still not clear how large the benefits of such interventions are. Is electricity access an investment that generates growth? Or is it more of a consumption good that growing economies can afford? Researchers have used traditional econometric methods on secondary data (observational studies) and also carried out field experiments, such as randomized controlled trials (RCTs), to try to answer this question.

Experimental methods have generally found smaller and less statistically significant results than observational studies have. Is this because experiments are more rigorous? Or because observational studies usually measure impacts over a longer period of time? It's likely that it takes time for people to make use of a new electricity connection. They will need to save and buy appliances. Effects on children's education will take an especially long time to come to fruition.

We carry out a meta-analysis of 16 studies previously reviewed by Bayer et al. (2020):

We assigned each positive impact (for example on income or on education) found in a study the score of 1 and each negative impact a -1 and then averaged over all the impacts. The graph shows this "positiveness of impact" compared to the time households had been connected to electricity. While observational studies found more positive impacts than experimental studies, there is also a positive correlation between duration of connection and positiveness of impact (and between duration of connection and being an observational study). Regression analysis shows that only duration of connection is statistically significant. 

But this small sample of studies can't be that conclusive, so we then carry out our own analysis to test whether impacts increase over time.

Using three waves of Indian household surveys from 1994-95, 2004-5, and 2011-12, we quantify the impacts of short-term (0-7 years) and long-term (7-17 years) electricity access on rural household well-being. These surveys tracked the same households over time. We don't know exactly when a household was connected, just whether it was already connected in 1994-95 or whether it got connected between the other surveys. We do know when villages were connected.

We use a difference in differences regression that is weighted using "inverse propensity scores". This is supposed to compensate for the fact that households are not actually connected randomly to the grid. If, for example, poor households are less likely to get connected, we overweight them in the sample. In our main analysis, we exclude households that were already connected in 1994-95 so that the control group only includes households that were not connected by 2011-12.*

We find that long-term electricity access increases per capita consumption and education, and reduces the time spent by women on fuel collection (compared to the control group). The effect of short-term connection is smaller and statistically insignificant. We find no significant effects on agricultural income, agricultural land holding, and kerosene consumption. 

Here is our main table of results:

The long-term impact on consumption is really very big – 18 percentage points more than the control group over a 7 year period. The effect on education is 0.4 of a year relative to the control group.

We did some robustness tests – using different weights and including the households connected before 1994-95 as "very long-term connections". The results roughly hold up, though the weighting isn't ideal in either case.

We think our results show that experimental studies really need longer term follow-ups before coming to conclusions.

* The recent research on differences in differences shows that many past studies used inappropriate control groups.

I have a new working paper up coauthored with former masters student Jack Gregory. The paper analyses data from two tribal area villages in Maharashtra State. Jack organized and supervised the survey when he worked for the NGO WOTR in India a few years ago. The surveyed was intended to provide data on greenhouse gas emissions and so had some shortcomings as an information source for understanding energy demand and the choice of fuel. Still, we thought it was worthwhile presenting this information to a wider audience.

We found that there were really big differences between the villages. In the village of Purushwadi income had a big effect on energy use, but there was little relationship in the nearby village of Kohane. Unfortunately, we don't have any explanation for this difference. In Purushwadi the relationship between energy use and income was also different than that found in national surveys.

The national surveys show fairly constant use of biomass across income groups and increased use of modern fuels in the upper half of the income distribution in rural India (Khandker et al., 2012):



The numbers refer to income deciles. Unfortunately, we don't have data on electricity use, though we do have data on electricity connections. Here is the data for per capita energy use that we do have by income quintile in each village (P = Purushwadi, K = Kohane):



It seems to me that research in this area is mostly about coming up with common patterns but understanding more about the differences between villages might be also of interest.

Besides this, our modelling shows modest support for the energy ladder or rather "energy stacking" hypothesis. Energy stacking implies that rural households continue to use traditional fuels but add more and more of the modern ones as their income rises. Also we find that using higher quality energy sources reduces energy use, ceteris paribus. We also find that household size, stove ownership, and season influence rural energy choices. However, the effects of improved stoves are small and not consistent across the villages. This fits with recent evidence for modest or even perverse impacts of improved stoves.

Here are some pictures to give you an idea of what is involved in measuring energy use in rural India:

(a) Measuring rice with a 5 kg basket scale; (b) Measuring a headload of branches with a 25 kg hanging scale; (c) Measuring kerosene with a 200 ml graduated cylinder

This is a new open-access journal published from the Crawford School and produced by Blackwell Wiley. First issue will be in early 2014 but the website is already open for submissions. I'm a member of the editorial board.

I've been at the workshop on Equity, Sustainability, and Climate Change organized by the Centre for Science, Technology and Society at the Tata Institute of Social Sciences in Delhi over the last two days. The meeting was attended by both academics, NGOs, and government officials including a speech by the environment minister. It has been interesting to hear different perspectives on the climate change issue than I usually hear from Australians, Europeans, Americans, and Chinese. Though mentioned by some Chinese, there is a much stronger emphasis on historical responsibility for emissions in the context of a "carbon space" or "carbon budget" model. Developed countries have used up much of the available space in the atmosphere to absorb carbon dioxide and the question is how can the developing countries develop with the little remaining available space in the next few decades if we are to stay within a 2C maximum warming. There is still debate about whether there should be another round of Kyoto commitments or whether the "bottom up" or "pledge and review" framework that emerged from Copenhagen can be accepted. It was pointed out that it was the BASIC countries (Brazil, South Africa, India, and China) that got together with the US at Copenhagen to introduce this regime, so they can hardly complain now. And many seem to accept that Kyoto is dead and at least China has to be in any new agreement in order to have the slightest chance of the getting the Americans on board. Mukul Sanwal stated that China looks like announcing a unilateral cap on per capita emissions, perhaps at the Durban meeting and that this will change the whole game. There was a lot of exasperation with the US and amazement that they could almost default on their debt obligations just because they can't agree with each other internally.

There were also several presentations on the costs of climate mitigation, lead off by my paper on alternative cost measures. We found that the alternative approaches came to the same conclusion - that even a $50 a tonne CO2 tax is very low and would prompt switching to renewable energy on a large scale or substantial abatement in the short-term.

I met a lot of new people. Several, such as , Sivan Kartha, were at the IPCC meeting in Korea but I didn't happen to meet them there.

We have added six new working papers to the series, so far in July and August:

How Many Jobs is 23,510, Really? Recasting the Mining Job Loss Debate,
Bruce Chapman and Kiatanantha Lounkaew, July 2011, CCEP Working Paper 1106

It is commonplace in Australian policy debate for groups presumed to be adversely affected by proposed policies to provide estimates of the undesirable consequences of change. A fashionable form relates to predictions of job losses for the group affected, usually accompanied by counter-claims made by the government of the day or other groups in favour of the policy. A highly public example of the above is the claim by the Minerals Council of Australia (MCA), based on work done in 2009 by Concept Economics (2009) that the then-planned Emissions Trading Scheme (ETS) would result in 23,510 fewer jobs in Australian mining than would otherwise be the case. Our research reports on findings using three different data series and methods to put into context the supposed jobs loss figure. Our results should not be taken to mean that economic policy reform is costless to all employees who might be affected by sectoral changes in the labour market, and there remain clear roles for government to minimise the personal costs for those so disadvantaged. As well, the details of this research cannot be translated into precise analyses of the employment effects of the carbon price policy being developed by the current government. But the essential points concerning the size and meaning of mining sector employment effects should not be in dispute; the alleged Òjobs lossesÓ aspect of the climate change policy debate is not in any sense important to the overall discourse.

Nordhaus, Stern, and Garnaut: The Changing Case for Climate Change Mitigation,
Stephen Howes, Frank Jotzo, and Paul Wyrwoll, July 2011, CCEP Working Paper 1107

Today the idea that climate change requires a gradual and moderate response no longer commands consensus support among economists. A more demanding approach is gaining ground. This paper traces the changes in economic thinking concerning the case for action on climate change, through an analysis of the work of three eminent economists: William Nordhaus, Nicholas Stern and Ross Garnaut. It shows how from Nordhaus to Stern to Garnaut the case for more urgent and radical mitigation has been strengthened as temperature targets have been lowered and business-as-usual emissions projections raised. It also shows that Stern and especially Nordhaus, who has been working on this subject the longest, have changed their own views in favour of more urgent and radical mitigation. Some disagreements remain between these three economists, and some other economists have more moderate views, but the old consensus has been shattered.

Challenges in Mitigating Indonesia's CO2 Emission: The Importance of Managing Fossil Fuel Combustion,
Budy P. Resosudarmo, Frank Jotzo, Arief A, Yusuf, and Ditya A. Nurdianto, August 2011, CCEP Working Paper 1108

Indonesia is among the largest 25 carbon dioxide emitting countries when considering only fossil fuels, and among the top three or five when emissions due to deforestation and land use change are included. Emission per capita from fossil fuels are still low in comparison with other countries, but have been growing fast, and are likely to overtake those from deforestation and land use change in the future. This paper argues the importance for Indonesia to start developing strategies to mitigate its emissions from fossil fuel combustion. It analyses the main drivers of the increase in emissions, identifies the options and challenges in reducing the future growth in emissions. Policy options are reviewed that would enable the Indonesian economy to keep on growing, but with a much lower carbon output.


Green Fiscal Policy and Climate Mitigation in Indonesia,
Budy P. Resosudarmo and Abdurohman, August 2011, CCEP Working Paper 1109

In common with other archipelagic countries, Indonesia is vulnerable to such impacts of climate change as prolonged droughts, increased frequency in extreme weather events, and heavy rainfall resulting in floods. These threats, coupled with the fact that Indonesia has been declared one of the three biggest greenhouse gases emitters, has induced the Indonesian government to place a high priority on climate change issues. In particular, the government considers its fiscal policy to be a key instrument in both mitigating against and adapting to climate change. This paper reviews Indonesia's implementation of green fiscal policies and discusses recent Indonesian fiscal policy responses to its commitment to reduce its emissions by 2020. In general, one can conclude that although progress has been made in the area of green fiscal policy in Indonesia, a more vigorous approach is needed to protect Indonesia's environment and to cope with the new challenges of controlling CO2 emission in the era of climate change.

Five Perspectives on an Emerging Market: Challenges with Clean Tech Private Equity,
Eric R. W. Knight, August 2011, CCEP Working Paper 1110

Private equity investment in technologies which deliver low carbon energy has grown as an area of both economic and social performance. This article offers a perspective on some of the challenges in the industry. It relies on case studies drawn from thirty five interviews with leading clean tech investment managers across Silicon Valley, New York and London. The findings suggest that despite the long-term growth opportunities, some investors have struggled to find attractive risk-reward premiums in early stage investments.

Where in the World is it Cheapest to Cut Carbon Emissions? Ranking Countries by Total and Marginal Cost of Abatement,
David I. Stern, John C. V. Pezzey, N. Ross Lambie, August 2011, CCEP Working Paper 1111

Countries with low marginal costs of abating carbon emissions may have high total costs, and vice versa, for a given climate mitigation policy. This may help to explain different countries' policy stances on climate mitigation. We hypothesize that, under a common percentage cut in emissions intensity relative to business as usual (BAU), countries with higher BAU emissions intensities have lower marginal abatement costs, but total costs relative to output will be similar across countries; and under a common carbon price, relative total costs are higher in emissions-intensive countries. Using the results of the 22nd Energy Modeling Forum, we estimate marginal abatement cost curves for the US, EU, China, and India, which we use to estimate marginal and total costs of abatement under a number of policy options currently under international debate. The results of this analysis provide support for our hypotheses.

I don't think I've mentioned on the blog that I am going to India next week to a workshop on "Equity, Sustainability, and Climate Change" organized by the Centre for Science, Technology and Society at the Tata Institute of Social Sciences. Though TISS is in Mumbai the workshop will be in Delhi at the India International Centre. The workshop focuses on balancing the need for sustainability and hence a limited global carbon emissions budget with the desire for equity in dividing the remaining allowed emissions among developing and developed countries. I have written a couple of papers* relevant to India's climate policy, hence my invitation to participate.

I haven't been to India before, so it will be my second new country this year, though I'm not planning on going anywhere but Delhi.

* We will post a new version of "Where is it Cheapest to Cut Carbon Emissions?" soon. This semester I have also been working with a student (Jack Gregory) on a paper on rural energy use in India and hopefully we'll turn that into a working paper fairly soon too.

There is a lot of misinformation floating around the blogosphere on the intentions behind Australia's recently announced climate change policy. A week ago, Stephen King listed a bunch of reasons for the policy, which does not include the true reason. And today there is a similar article on the Conversation from Paul Frijters. The real reason the government is acting is that following the Copenhagen conference in 2009 they pledged to cut emissions by 5% unconditionally. Of course, they are proposing a much more ambitious program than the opposition is because the Greens have forced them to do so. But the opposition also has a policy because they must do so to meet Australia's international obligations.

Frijter's article misinterprets what happened at Copenhagen, in my opinion, and as a result then criticizes the government's efforts to meet Australia's obligations. Though no comprehensive agreement was reached at Copenhagen, following the conference most countries in the world made pledges of what they would do to address climate change. Most developed countries including Australia made pledges to make absolute cuts in emissions. Australia promised a 5-25% reduction by 2020. The 5% reduction was unconditional on any action by other governments. So now the Australian government is trying to do what it pledged to do. The United States pledged a 17% reduction. The Obama administration is trying to address that (through EPA regulation) despite the obstruction of the Republican Party. China and India made pledges in terms of reductions in emissions intensity of GDP. India's goal was a 20-25% reduction and China's a 40-45% reduction. India's goal probably isn't much different to business as usual but China's is a real cut relative to BAU. Other developing economies made pledges to reduce emissions relative to what they would otherwise be under BAU. So it is completely wrong to assert that Australia is the only country taking action and that other countries including India will laugh at Australia.

Whether the government will meet its goal through this policy is another question. But the key thing is that after the initial 3 year period of a fixed carbon price there is planned to be a cap on carbon emissions. If the Liberals come to power under Abbott this will likely be dropped, I suppose, and even Labor may chicken out of it. But in theory this would have an impact even though not all emissions sources (but more than just electricity generation) are included.

Though modern ecological economics dates to the late 1980s, as a school of thought ecological economics has deep roots in thinkers who developed various forms of “biophysical economics”: Daly, Odum, Georgescu-Roegen etc. The book “Ecological Economics” by Juan Martinez-Alier documents this history [also Røpke, 1994].

The International Society for Ecological Economics (ISEE) was founded in 1988 following a meeting in Barcelona and discussions between ecologists and economists in the US and Europe – many in Sweden. In 2011 ISEE has 3049 members worldwide. There have been booms and busts in membership of ISEE over time. There are now local “chapters” of the international society in most regions of the world: Africa, Argentina and Uruguay, Australia-New Zealand, Brazil, Canada, Europe, India, Meso-America, Russia, USA. Their main role is to hold local conferences in the odd years. Europe is the largest chapter in terms of members, followed by the US and India.

The first president of the society was Bob Costanza, followed by Dick Norgaard, John Proops, Charles Perrings and Joan Martinez-Alier, Peter May, and Bina Agarwal.
The international society holds meetings every even year. These have been held in: Washington DC (1990), Stockholm (1992), San Jose, Costa Rica (1994), Boston MA (1996), Santiago de Chile (1998), Canberra (2000), Sousse, Tunisia (2002), Montreal (2004), New Delhi (2006), Nairobi (2008), Bremen-Oldenburg (2010).

The society’s journal, Ecological Economics, was founded in 1989 and has had three editors: Bob Costanza, Cutler Cleveland, and Rich Howarth. It is published by Elsevier. It is very successful and now receives hundreds of submissions each year, while publishing 273 articles in 2009. It is also increasingly cited - about matching the longer established JEEM – though the latter publishes fewer papers [Ma and Stern, 2006]. Edward Elgar and Island Press are probably the two largest publishers of ecological economics books. The journal Environmental Policy and Governance is now associated with ESEE.

Ecological economists have a stronger policy influence in regions outside the US and possibly have the greatest influence in Europe. Membership in the societies varies across regions. In the US most members are academics, students, and non-profits/NGOs. In Australia and New Zealand public service representation is strong.

There are numerous programs at universities all over the world, though not all are explicitly called ecological economics programs. RPI’s is perhaps the only one that is both titled “ecological economics” and is in an economics department.

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This is just a bunch of facts at the moment. Not sure what to do with this and how to incorporate it.

These are my first estimates of the marginal abatement cost curves for the four main regions based on the results of the EMF22 exercise. Here I have flipped the graph back 90 degrees again. This is private marginal cost for abating fossil and industrial emissions of CO2 using market exchange rates. The EU is the most expensive region for small cuts in emissions and India the cheapest. But for extreme cuts India is most expensive. These results will look different if we measure cost differently. Note also that this meta-analysis finds very high carbon prices for large cuts in emissions. The typical numbers thrown around of $20 per tonne only apply to very small cuts in emissions. But the cost of a given emissions reduction declines over time as technology progresses.

My paper with Frank Jotzo in Energy Policy argued that while India's goal of cutting emissions intensity by 25% between 2005 and 2020 was likely to be similar to the business as usual reduction in emissions, China's goal was much more ambitious. China aims to reduce emissions intensity by 40-45% over this time frame, while we estimated it would decline by 24% under business as usual.

By contrast, many commentators argued that China's goal was just business as usual. This was because China's strong policies to reduce energy and carbon intensity were already included in standard scenarios.

I am now revising my paper with Ross Lambie on where it is cheapest to cut carbon emissions. We will use the results of the 22nd Energy Modelling Forum (EMF22) in this revised version. So I was curious what the business as usual scenarios developed by the participating models said about China and India in the 2000-2020 period:



On average they predict a 25% reduction in emissions intensity in China from 2000 to 2010, increasing to a 27% reduction in 2010-2020. We estimated 1% and 15% reductions in these periods under BAU. There is no way that China will end up with a 25% reduction from 2000-2010. Emissions intensity rose from 2000 to 2005 and China is struggling to achieve its goal of reducing energy intensity by 20% from 2005 to 2010. Our estimates for India are pretty close to the EMF averages. The results also show a large variation in the scenarios. There is a lot of uncertainty about what is BAU.

China might achieve a 27% reduction in emissions intensity relative to 2010 by 2020 (the average given by the EMF22 models above). But it will be the result of policy action, not business as usual.

van den Bergh, J. C. J. M., Environment versus growth — A criticism of “degrowth” and a plea for “a-growth”, Ecological Economics.

van den Bergh makes a much more extensive version of the main argument I made in my review of Tim Jackson's book Prosperity without Growth. There aren't policy levers that can directly stop growth and it might not be what is needed to solve environmental problems anyway. It makes much more sense to implement direct policies on resource use, environmental quality etc. van den Bergh calls this "a-growth". Forget about growth per se as well as de-growth as policy targets and aim at achieving the things we actually want to achieve.

Henriques, S. T. and A. Kander, The modest environmental relief resulting from the transition to a service economy, Ecological Economics.

This paper expands Kander's previous study of dematerialization in Sweden to a group of 13 countries. That article showed that it was an illusion that a shift to the service sector had helped dematerialize the economy. Rather rapid productivity gains in the industrial sector had both reduced energy use and the share of manufacturing in GDP due to the fall in the price of manufactured goods relative to services. The trend to rising service prices relative to manufacturing prices due to productivity gains in manufacturing is known as Baumol's disease. They explain the new study in the abstract:

"A service transition is supposed to lead to the decline of energy intensity (energy/GDP). We argue that this interpretation is overly optimistic because the shift to a service economy is somewhat of an illusion in terms of real production. Several recent studies of structural effects on energy intensity have made the error of using sector shares in current prices, combined with GDP in constant prices, which is inconsistent and ignores the different behaviour of prices across sectors. We use the more correct method of sector shares in constant prices, and make an attempt to single out the effect from the real service transition by using two complementary methods: shift share analyses in current and constant prices, and Logarithmic Mean Divisia Index (LMDI) for 10 developed and 3 emerging economies. A service transition is rather modest in real terms. The major driver of the decline in energy intensity rests within the manufacturing sector. Meanwhile, the transition to a service sector had a small downward impact on energy intensity in 7 of the developed countries (and no impact in the others). For emerging economies like Brazil, Mexico and India, it is the residential sector that drives energy intensity down because of the declining share of this sector as the formal economy grows, and as a consequence of switching to more efficient fuels."

The Australian Research Council announced it's decisions on applications for Discovery and Linkage grants today. Congratulations to Frank Jotzo and Peter Wood on the success of their application. Also, congratulations to my colleague in the Arndt-Corden Department of Economics, Prof. Athukorala whose proposal with Peter Robertson: "Sustaining India's economic transformation: challenges, prospects and implications for Australia and the Pacific region" was also funded. Also of interest given the topics of this blog:

Jakob Madsen won an Australian Professorial Fellowship for "The great divergence, long-run growth and unified theories of economic growth."

David Pannell, John Rolfe, Michael Burton, and Jessica Meeuwig received funding for their Linkage project: "Do scientist and public preferences diverge? Analysing expert and public preferences for environmental and social outcomes for the Swan River."

Congratulations!

Our paper: How Ambitious are China and India's Emissions Intensity Targets? has been accepted for publication at Energy Policy.

This is the second part of a series on my new working paper on where it is cheapest to cut carbon emissions.

In the previous post I assumed that all countries shared the same marginal cost of abatement curve. In reality this is not the case and in order to rank countries by marginal cost of abatement or total costs of meeting a given policy we need to estimate a cost curve for each country. The Treasury Review assessed the costs of meeting 4 different policy scenarios at 2020 and 2050. This gave us 8 data points for each region or country.

The data we used to estimate the curves are GDP data rather than GNP data because changes in GNP include net receipts for the sale of emissions permits. Our data on the cuts in emissions are the actual domestic reductions in emissions in each region. We don't want, therefore, to use a measure of cost that includes the costs of offsets.

Also we subtracted the "terms of trade impact" component from the estimates of GDP losses and gains. Under a global climate policy countries experience GDP impacts that have nothing to do with their domestic mitigation efforts but are the effect of climate policy actions elsewhere. For example, OPEC countries are hard hit under global mitigation efforts because of the reduction in world demand for oil. On the other hand, oil importers such as India gain from the fall in the oil price. Again, because we wanted to limit costs to the costs of domestic mitigation we needed to remove these effects.

Obviously a lot of technological change is expected to take place over the next 50 years. This technological change would be expected to lower the emissions intensity of the economy even in the absence of climate policy. This lowers business as usual emissions compared to what they would be without technological change. We make the assumption, though, that the elasticity of GDP with respect to reducing emissions relative to business as usual does not change. It's a strong assumption, but we think it is the best we can do with the data we have available.

These are the resulting 2005 marginal costs that we come up with:



The left side of the table ranks countries by the marginal cost of abating a ton of carbon (not carbon dioxide) where marginal cost is measured in PPP (Purchasing Power Parity) adjusted dollars. To get figures in terms of carbon dioxide multiply by 12/44.

Japan and Canada actually have GDP gains. This shows that despite the adjustments made these numbers are still not, of course, the effect of a unilateral domestic policy but of a coordinated global mitigation effort. Apparently, investment increases in Japan under climate mitigation increasing its GDP. Abatement is fairly cheap in the US and EU and expensive in most developing and transition economies. There isn't any correlation between emissions intensity and marginal cost. Countries appear to be on different cost curves.

In terms of marginal domestic loss of GDP climate policy is expensive in developing countries and cheap in the developed world.

The right hand side of the table uses actual market exchange rates instead. Now it is cheap to abate in China and OPEC as well as in the US and EU.

In a last minute substitution I am going to be presenting instead of Regina Betz at the CERF National Conference at Old Parliament House on Tuesday at 1pm. Title will be "Modelling Global Energy Efficiency Trends". I'll try to cover both implications for Australian energy efficiency and Chinese and Indian emissions intensity targets in the 15 minutes allowed.

Changes in the energy/GDP ratio that are not related to changes in the relative price of energy are called changes in the autonomous energy efficiency index (AEEI, Kaufmann, 2004). These could be due to any of the determinants of the relationship between energy and output listed at the beginning of this section and not just technological change. Even A in (3) is just general TFP and, therefore, includes the effects of technological change on augmenting other inputs as well as energy. There are two related ways of measuring the level of technology that control for the other factors that we consider in this section of this paper. The first, distance function, approach asks: “What is the minimum energy requirement to produce a given level of output holding all other inputs constant?” The level of energy efficiency in period t relative to period 0, Bt , is given by:
(4)




where y is the vector of outputs and x the vector of non-energy inputs with subscripts indicating the periods and Ei() is a function indicating the minimum energy required in period i in order to achieve the given outputs given the level of inputs. Equation (4) can also be used to measure the relative level of energy efficiency of two countries. The functions in (4) can be estimated econometrically (e.g. Stern, 2010) or non-parametrically.

An alternative approach is an index of energy augmenting technical change. This involves a reformulation of the production function (3):

(5)



so that each input is multiplied by its own technology factor Ai that converts crude units of the input into “effective units”. AE is the index of energy augmenting technical change, which holds the use of all other inputs and their augmentation indices constant. In some but not all situations, AE = B.

Estimates of the trend in AEEI, energy efficiency, or the energy augmentation index are mixed. This is likely because the direction of change has not been constant and varies across different sectors of the economy and strong correlations between the state of technology and the levels of other inputs result in biased and inconsistent results (Stern, 2010). Jorgensen and Wilcoxen (1993) estimated that autonomous energy efficiency is declining. Berndt et al. (1993) use a model with linear time trends to estimate augmentation trends labor, electricity, fuels, machines, and structures in US manufacturing industry between 1965 and 1987. The rates of augmentation are -1.2%, 11.8%, -3.4%, 4.4%, and 8.7% respectively per annum. Patterns for Canada and France were entirely different. Stern (2010) uses a method intended to address the issue of biased estimation. He finds that energy efficiency (4) improved from 1971 to 2007 in most developed economies, former communist countries including China, and in India. But there was no improvement or a reduction in energy efficiency in many. Globally, such technological change resulted in 40% growth in energy use over the period than would otherwise have been the case.

Judson et al. (1999) estimate separate EKC relations for energy consumption in each of a number of energy-consuming sectors for a large panel of data. They estimate time effects that show rising energy consumption over time in the household and other sector but flat to declining time effects in industry and construction. This suggests that technical innovations tend to introduce more energy using appliances to households and energy saving techniques to industry (Stern, 2002).

When there is endogenous technological change, changes in prices may induce technological changes. As a result, an increase in energy prices does tend to accelerate the development of energy saving technologies, while periods of falling energy prices may result in energy-using technological change. There can also be an effect on the general rate of TFP growth (Berndt, 1990). Jorgenson (1984) found that technical change was biased and tended to be energy using. If this is the case, lower energy prices tend to accelerate TFP growth and vice versa. More recent results may contradict this conclusion (e.g. Judson et al., 1999). Newell et al. (1999) provide some information on the degree to which energy price increases induce improvements in the energy efficiency of consumer products. They decompose the changes in cost and energy efficiency of various energy using appliances using the concept of a transformation frontier of possible cost and efficiency combinations. For room air conditioners, large reductions in cost holding efficiency and cooling capacity occurred from 1960 to 1980 in the US. Also the cost of high efficiency air conditioners relative to inefficient ones was reduced. From 1980 to 1990 the former trend ended but the mix of air conditioners offered from those that were feasible to manufacture shifted sharply in favor of higher efficiency. Only about one quarter of the gain in energy efficiency since 1973 was induced by higher energy prices. Another quarter was found to be due to raised government standards and labeling. For gas water heaters the induced improvements were close to one half of the total, although much less cost reducing technical change occurred. Popp (2002) similarly finds that increased energy prices have a significant though quantitatively small effect on the rate of patenting in the energy sector.

Recent research investigates the factors that affect the adoption of energy efficiency policies or energy efficiency technology (Matisoff, 2008; Fredriksson et al., 2004; Gillingham et al., 2009; Wei et al., 2009; Stern, 2010). Differences across countries and states, over time, and among individuals can be due to differences in endowments and preferences but also due to market failures. Gillingham et al. (2009) provide a classification of various market and behavioral failures that affect energy efficiency. Market failures include environmental externalities, information problems, liquidity constraints in capital markets, and failures of innovation markets. Fredriksson et al. (2004) find that the greater the corruptibility of policy-makers the less stringent is energy policy and that the greater lobby group coordination costs are the more stringent energy policy is.

Matisoff (2008) finds that the most significant variable affecting the adoption of energy efficiency programs across U.S. states is citizen ideology. A broad band of states from Florida to Idaho has not adopted any policies. The initial level of criteria air pollutants was also a significant determinant of the number of programs adopted and the adoption of a renewable portfolio standard. Wei et al. (2009) compute an energy efficiency index based on the data envelopment analysis approach to examine energy efficiency in China. Using 1997–2006 panel data for 29 provinces, they find that energy efficiency is negatively associated with the secondary industry share in GDP, the state-owned economic share in GDP and the government expenditure share in GDP, and is positively associated with the technical level and non-coal share in energy consumption.

Stern (2010) uses a stochastic production frontier to model trends in energy efficiency (4) over time in a panel of 85 countries. He finds that energy efficiency rises with increasing general total factor productivity but is also higher in countries with more undervalued exchange rates in PPP terms. Higher fossil fuel reserves are associated with lower energy efficiency. Energy efficiency converges over time across countries and technological change was the most important factor mitigating the global increase in energy use and carbon emissions due to economic growth.

References
Berndt, E. R. (1990). “Energy use, technical progress and productivity growth: a survey of economic issues.” The Journal of Productivity Analysis 2: 67-83.
Berndt, E. R., C. Kolstad, and J-K. Lee (1993). “Measuring the energy efficiency and productivity impacts of embodied technical change.” Energy Journal 14: 33-55.
Fredriksson, P. G., H. R. J. Vollebergh, and E. Dijkgraaf (2004) Corruption and energy efficiency in OECD countries: theory and evidence, Journal of Environmental Economics and Management 47: 207–231.
Gillingham, K., R. G. Newell, and K. Palmer (2009) Energy efficiency economics and policy, Annual Review of Resource Economics 1: 597-620.
Jorgenson, D.W. and P. J. Wilcoxen (1993). “Reducing US carbon emissions: an econometric general equilibrium assessment.” Resource and Energy Economics 15: 7-25.
Jorgenson, D.W. (1984). “The role of energy in productivity growth.” Energy Journal 5(3): 11-26.
Judson, R. A., R. Schmalensee, and T. M. Stoker (1999). “Economic development and the structure of demand for commercial energy.” The Energy Journal 20(2): 29-57.
Kaufmann, R. K. (2004). “The mechanisms for autonomous energy efficiency increases: A cointegration analysis of the US energy/GDP ratio.” Energy Journal 25(1): 63-86.
Matisoff, D. C. (2008) The adoption of state climate change policies and renewable portfolio standards: regional diffusion or internal determinants? Review of Policy Research 25(6): 527-546.
Newell, R. G., A. B. Jaffe, and R. N. Stavins (1999). “The induced innovation hypothesis and energy-saving technological change.” Quarterly Journal of Economics 114: 941-975.
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Stern, D. I. (2002). “Explaining changes in global sulfur emissions: an econometric decomposition approach.” Ecological Economics 42: 201-220.
Stern D. I. (2010) Modeling international trends in energy efficiency and carbon emissions, Environmental Economics Research Hub Research Report 54.
Wei, C., J. Ni, and M. Shen (2009) Empirical analysis of provincial energy efficiency in China, China & World Economy 17(5): 88-103.

The series continues:

Energy Quality and Shifts in Composition of Energy Input

In the course of economic development countries’ fuel mix tends to evolve as they move up the “energy ladder” (Hosier, 2004). Burke (2010) documents a similar progression for the power sources used in electricity generation. In the least developed economies and in today’s developed economies before the industrial revolution the use of biomass and animate prime movers dominates. The evolution of energy mix over the course of economic development and over history in the technologically leading countries depends on each country’s endowments of fossil energy and potential for renewables such as hydro-electricity but some regularities apply. The share of electricity in total energy use tends to rise. Low-income countries tend to generate electricity from hydropower and oil, while high-income countries have more diverse power sources including nuclear power. Direct use of coal tends to rise and then fall over time and income. Natural gas use has increased significantly in recent decades mostly in more developed economies. Finally electricity generated from solar- and wind-power and only now beginning to take off in more developed economies. The figure below illustrates this pattern for the United States.

Composition of US Primary Energy Input 1850-2008



















Energy quality is the relative economic usefulness per heat equivalent unit of different fuels and electricity. Fuels have a number of physical attributes that will affect their relative qualities, including energy density (heat units per mass unit); power density (rate of heat units produced per unit are per unit time); ease of distribution; the need for a transfer medium; controllability (the ability to direct the position, direction and intensity of energy use); amenability to storage; safety, and environmental impacts (Berndt, 1978; Schurr, 1982; Zarnikau, 1996; Cleveland et al, 2000). Some fuels can be used for a larger number of activities and/or for more valuable activities. For example coal cannot be used to directly power a computer while electricity can. Some fuels, in particular electricity, can transform the workplace entirely and change work processes, thus contributing to productivity gains (Enflo et al., 2009).

Stern (in press) discusses alternative ways of measuring energy quality. The most relevant approach to understanding the impact of relatively small changes in the composition of the energy input on economic output is the marginal product of the fuel, which is the marginal increase in the quantity of a good or service produced by the use of one additional heat unit of fuel. The marginal product of a fuel is determined in part by the complex set of attributes described above that are unique to each fuel. It also varies according to what activities it is used in, how much and what form of capital, labor, and materials it is used in conjunction with, and how much energy is used in each application. More abundant fuels will be applied more widely and on the margin in less productive applications (Kaufmann, 1994). Therefore, energy qualities measured in this way are not fixed over time. However, it is generally believed that electricity is the highest quality type of energy followed by natural gas, oil, coal, and wood and biofuels in descending order of quality. This is supported by the typical prices of these fuels per unit of energy, which should be proportional to its marginal product. Under the assumption of optimizing behavior marginal products should be approximated by prices, which are usually readily available. Other indicators of energy quality must be estimated.

Surprisingly, relatively few studies evaluate the role of the change in energy mix on energy intensity. Schurr and Netschert (1960) were among the first to recognize the economic importance of energy quality in understanding trends in energy and output. Noting that the composition of energy use has changed significantly over time, Schurr and Netschert argued that the general shift to higher quality fuels reduces the amount of energy required to produce a dollar’s worth of GDP. Berndt (1990) also noted the key role played by the shifting composition of energy use towards higher quality energy inputs.

Cleveland et al. (1984), Kaufmann (1992, 2004) and OTA (US Congress, 1990) presented analyses that explain much of the decline in the US energy/GDP in terms of structural shifts in the economy and shifts from lower to higher quality fuels. Kaufmann (2004) estimates a vector autoregressive model of the energy/GDP ratio, household energy expenditures, energy mix variables, and energy price variables for the US. He finds that shifting away from coal use and in particular shifting towards the use of oil reduces energy intensity. This shift away from coal more than explains the decline energy intensity over the entire 1929-99 time period. If decoupling is mainly due to the shift to higher quality fuels then there appear to be limits to that substitution. In particular, exhaustion of low-cost oil supplies could mean that economies have to revert to lower quality fuels such as coal (Kaufmann, 1992).

U.S. GDP and Primary Energy Use and Quality Adjusted Final Energy





















Notes: GDP is in constant dollars i.e. adjusted for inflation. Primary energy use is the sum of primary energy BTUs. Quality adjusted final energy use is a Divisia index of the principal final energy use categories – oil, natural gas, coal, primary electricity, wood and other biofuels. The different fuels are weighted according to their average prices. Sources: US Energy Information Administration and Bureau of Economic Analysis.


The figure above includes a quality-adjusted index of final energy use that accounts for differences in the productivity of different fuels by weighting them by their prices (see Stern, 2000). There is less evidence of decoupling of energy use and GDP in these data than indicated by the primary energy series especially up till 1973. The studies cited above and Stern (1993, 2000) used earlier GDP data that showed significantly less economic growth in the U.S.A. between 1960 and 1994 than more recent updated data. Using this data there was little decoupling of GDP from quality adjusted energy use even after 1973. This change in the GDP data indicates that structural change and technological change must also contribute to lowering the energy/GDP ratio in the last three decades assuming that prices reflect the relative marginal products of the fuels.

Other studies find, however, a much larger role for technological change than for changes in the composition of energy in the reductions in energy intensity seen around the world. For example, Ma and Stern (2008) find that interfuel substitution has negligible effects on the decline in energy intensity in China between 1994 and 2003. Technological change reduced energy intensity by more than the actual reduction in energy intensity due to the intensity increasing effects of structural change. Stern (2010) finds that between 1971 and 2007, changes in fuel mix within individual countries increased world energy use by 4%, while global energy intensity declined by 40%. Shifts in the distribution of economic activity towards countries with lower quality energy mixes such as China and India contributed further to increasing energy intensity globally.

References
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Berndt, E. R. (1990). “Energy use, technical progress and productivity growth: a survey of economic issues.” The Journal of Productivity Analysis 2: 67-83.
Burke, P. J. (2010) Income, resources, and electricity mix, Energy Economics 32: 616–626
Cleveland, C. J., R. Costanza, C. A. S. Hall, and R. K. Kaufmann (1984). “Energy and the U.S. economy: A biophysical perspective.” Science 225: 890-897.
Cleveland, C. J., R. K. Kaufmann, and D. I. Stern (2000). “Aggregation and the role of energy in the economy.” Ecological Economics 32: 301-318.
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Hosier, R. H. (2004). Energy ladder in developing countries. Encyclopedia of Energy, Elsevier, 2: 423–435.
Kander, A. (2002). Economic Growth, Energy Consumption and CO2 Emissions in Sweden 1800-2000, Lund Studies in Economic History No. 19, Lund, Sweden.
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Kaufmann, R.K. 1994. “The relation between marginal product and price in US energy markets: Implications for climate change policy.” Energy Economics 16(2):145-158.
Kaufmann, R. K. (2004). “The mechanisms for autonomous energy efficiency increases: A cointegration analysis of the US energy/GDP ratio.” Energy Journal 25(1): 63-86.
Ma C. and D. I. Stern (2008) China’s changing energy intensity trend: a decomposition analysis, Energy Economics 30(3): 1037-1053.
Schurr, S., (1982), “Energy efficiency and productive efficiency: some thoughts based on american experience” Energy Journal 3(3): 3-14.
Schurr, S. and B. Netschert (1960). Energy and the American Economy, 1850-1975. Baltimore: Johns Hopkins University Press.
Stern, D. I. (1993). “Energy use and economic growth in the USA, A multivariate approach.” Energy Economics 15: 137-150.
Stern, D. I. (2000). “A multivariate cointegration analysis of the role of energy in the U.S. macroeconomy.” Energy Economics 22: 267-283.
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The latest episode. Comments please.

The Rebound Effect

The Khazzoom-Brookes Postulate (Brookes, 1990; Khazzoom, 1980, Berkhout et al., 2000), or "rebound effect," argues that energy saving innovations induce an increase in energy consumption that offsets the technology derived saving. Rebound effects can be defined for energy saving innovations in consumption and production. A consumer consumes energy services that are produced using energy itself and an appliance whose energy use requirements are reduced by the innovation. Five rebound effects can be defined:

1. A substitution effect towards greater consumption of the now cheaper energy service and therefore of energy (Khazzoom, 1980).

2. A direct income effect, which can be positive or negative depending on whether the energy service is a normal or inferior good (Lovins, 1988). Lovins (1988) argued that energy services were inferior goods in developed economies and, therefore, the negative income effect would outweigh the positive substitution effect.

3. Income effects on the consumption of other energy services by the consumer. The money saved on the cheaper energy service can be spent on other energy services. Other energy services may be substitutes for or complements with the energy service that is now cheaper and, therefore, the effects are complicated (Berkhout et al., 2000). Most empirical rebound studies are microeconomic studies that only include these first three effects.

4. Increased real income also increases demand for all goods in the economy and, therefore, for the energy required to produce them. Berkhout et al., (2000) call this a structural effect.

5. There also may be economy-wide changes such as adjustments in capital stocks that result in further increased long-run demand response for energy that Howarth (1997) terms a "macro-economic feedback". These are the long-run consequences of structural change.

The production case is very similar, except that the income effect is replaced by an output effect. Consumers are constrained by a fixed nominal income but producers’ costs are not similarly constrained. Therefore, output effects can be large. For example, Darwin (1992) found that for wood-saving technological change in the US Pacific Northwest output effects were sufficiently large as to increase the consumption of raw logs.

Brookes (1990) suggested that, due to long-run growth effects, the rebound effect could be larger than the initial saving resulting in higher, not lower, energy consumption, sometimes termed “backfire”. In partial equilibrium, absolute value of the demand elasticity for energy should be an upper limit on the size of the rebound effect (Sorell and Dimitropoulos, 2008). Using a macro model with fixed energy prices, Saunders (1992) showed that this required that the elasticity of substitution between energy and other inputs is greater than unity, which in my opinion is unlikely. Howarth (1997), however, argues persuasively that even if the elasticity of substitution is one or greater that, when a distinction is made between energy services and energy use, the macro-level energy rebound effect for a production innovation is less than the initial innovation induced reduction in energy use, so improvements in energy efficiency do, in fact, reduce total energy demand.

Extensive empirical studies have been conducted for both production and consumption primarily at the micro-economic level. In an extensive survey of empirical estimates of the rebound effect through the mid-1990s, Greening et al. (2000) find that micro-level rebound effects for consumption are typically in the range of 10-30% and may typically be even smaller for industry. In subsequent studies, Bentzen (2004) finds a 24% rebound in U.S. manufacturing, Haas and Biermayr (2000) estimate a 20-30% rebound effect in Austrian space heating, and Berkhout et al. (2000) find rebound effects of 15-27% for the Netherlands. Roy (2000) argues that because high quality energy use is still small in households in India, demand is very elastic, and thus rebound effects in the household sector in India and other developing countries can be expected to be larger than in developed economies. Sorrell et al. (2009) review the literature on the micro-level or partial equilibrium rebound effect, which they term the “direct rebound effect”, for personal transport, household heating, and other household services, also finding that the effect appears to be less than 30%.

Grepperud and Rasmussen (2004) use a general equilibrium model for the Norwegian economy with econometrically estimated parameters. For an increase in the growth rate of the augmentation index of electricity, they find rebound effects greater than 100% in manufacturing industries where there are good substitution possibilities between electricity and other inputs, electricity dominates energy consumption, and the industries face perfectly elastic export demand which allows output to expand substantially. Schipper and Grubb (2000) survey energy-output changes for broad end-use categories in industrial nations and find sector-level rebounds of 5-15%. Within what they describe as a “limited theoretical framework”, they speculate that there are small macro-level rebound effects. Allan et al. (2007) also use a CGE model and find a short-term rebound effect of 55% and long-run effect of 30% for an increase in energy efficiency in production in the UK. However, these results are sensitive to the assumed structure of the labour market, key production elasticities, the time period under consideration and the mechanism through which increased government revenues are recycled back to the economy.

References
Allan, G., N. Hanley, P. McGregor, K. Swales, K. Turner (2007) The impact of increased efficiency in the industrial use of energy: A computable general equilibrium analysis for the United Kingdom, Energy Economics 29: 779–798.
Bentzen, J. (2004). “Estimating the rebound effect in US manufacturing energy consumption.” Energy Economics 26(1): 123-134.
Berkhout, P. H. G., J. C. Muskens, and J. W. Velthuijsen (2000) “Defining the rebound effect.” Energy Policy 28: 425-432.
Brookes, L. (1990). “The greenhouse effect: the fallacies in the energy efficiency solution.” Energy Policy 18: 199-201.
Darwin, R. F. (1992). “Natural resources and the Marshallian effects of input-reducing technological changes. Journal of Environmental Economics and Environmental Management 23: 201-215.
Greening, L. A., D. L. Greene, and C. Difiglio, (2000).”Energy efficiency and consumption - the rebound effect - a survey.” Energy Policy 28: 389-401.
Grepperud, S. and I. Rasmussen (2004). “A general equilibrium assessment of rebound effects.” Energy Economics 26: 261-282.
Haas, R. and P. Biermayr (2000). “The rebound effect for space heating - Empirical evidence from Austria.” Energy Policy 28: 403-410.
Howarth, R. B. (1997). “Energy efficiency and economic growth.” Contemporary Economic Policy 25: 1-9.
Khazzoom, D. J. (1980). “Economic implications of mandated efficiency standards for household appliances.” Energy Journal, 1(4): 21-39.
Lovins, A. B. (1988) Energy saving from more efficient applicances: another view, Energy Journal 10: 157-166.
Saunders, H. D. (1992). “The Khazzoom-Brookes postulate and neoclassical growth.” Energy Journal 13(4): 131-148.
Schipper, L. and M., Grubb (2000). "On the rebound? Feedback between energy intensities and energy uses in IEA countries." Energy Policy 28(6-7): 367-388.
Sorrell, S. and J. Dimitropoulos (2008) The rebound effect: Microeconomic definitions, limitations and extensions. Ecological Economics 65: 636–649.
Sorrell, S., J. Dimitropoulos, M. Sommerville (2009) Empirical estimates of the direct rebound effect: A review. Energy Policy 37: 1356–1371.