Beyond the environmental Kuznets curve: Diffusion of sulfur-emissions-abating technology

A group of students in Sweden sent me some questions about my paper:

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

The paper is fairly technical and so I thought it might be useful to post my responses here.

What would you say is the main question in the framework of dematerialization in your paper?

The focus of my paper is on the reduction in sulfur emissions. I don't think the paper is mainly about dematerialization as I understand it, in particular. Within my focus on sulfur emissions I am mainly interested in changes in sulfur emissions when we hold many other things that are going on constant - what economists like to call "ceteris paribus". Now those things I am holding constant are the shift of the economy from manufacturing to services or from agriculture to industry etc. and also the shift in the mix of fuels say from coal to natural gas or from oil to electricity. Those changes will affect sulfur emissions. All of these moves may involve "dematerialization". The effects of these variables are displayed in Table 2 "Frontier parameter estimates".

What is left after controlling for those factors are the trends in Figure 2 "Emissions technology trends". Over time there has been a reduction in sulfur emissions holding all the table 2 factors constant in most countries. This results in less sulfur being emitted into the environment as sulfur dioxide but doesn't probably result in dematerialization.

What is your reasoning on how to realize this dematerialization of sulphur emissions?


One of the leading methods of sulfur abatement is flue gas desulfurization (see picture above) which reacts the gas with limestone to form calcium sulfate. This requires mining limestone, building the machinery that removes the pollutant and then disposing of the waste. And desulfurization consumes energy. In some cases the waste has been used in the building industry and so there hasn't been a big increase in material and energy used. Other approaches are using low sulfur coal and "washing coal" to remove sulfur prior to burning.


What are your personal opinions on the outcome of the paper?

The most interesting result for me was how the countries ended up grouping into two groups by 2000 - a low pollution group of Germanic/Scandinavian countries and Japan and a high pollution group of Mediterranean and Anglo Saxon countries. I didn't really expect to find that quite so clearly. Recently I realised that this seems to be related to the idea of "legal origin". French and English legal origin countries have higher pollution, ceteris paribus, and German and Scandinavian legal origin countries lower pollution. Japan's legal system is based on the German system. I also found that countries with higher per capita income, higher population density, and higher potential pollution if nothing was done about it had lower pollution ceteris paribus.

Blue and Red States and Climate Change

Source: U.S. EPA, Climate Change Indicators in the United States, April 2010.

The map above shows that there has been more climate change on average in "blue states" - those that vote Democratic than in "red states" - those that vote Republican in the US. The colors are neatly reversed (here they are the traditional blue for conservative and red for left wing). Does this partly explain the divide in US politics on climate change? But there is the same divide in Australia and to some degree in Britain. So I don't know if this has anything to do with it.

The Environment and Directed Technical Change: Acemoglu et al.

Acemoglu, Aghion, Bursztyn, and Hemous put out an interesting NBER Working Paper last October. The abstract is below. They carry out a simulation which shows that a carbon tax alone is significantly inferior in terms of loss of consumption to a combination of a carbon tax and clean technology development subsidy. Results depend on the elasticity of substitution between dirty and clean inputs and the discount rate. If the elasticity of substitution is 10 then temperature never rises by more than 1.76C irrespective of the discount rate and a carbon tax only policy costs 0.92 to 1.55% of consumption relative to the optimal policy depending on the discount rate. But lower elasticities of substitution (5 or 3) make a carbon tax worse (2-4% consumption loss) and result in catastrophic climate change (7-8C) under higher discount rates (1 to 1.5% rate of time preference, latter is Nordhaus' choice).

It is likely that the interfuel elasticity of substitution is greater than unity. But, based on my research I think it is very unlikely to be as high as 5 or 10.

Based on this research more attention should be paid to combining innovation policy with a carbon tax. This is a position that is, I believe, advocated by Roger Pielke among others. But it also shows that there can be a huge difference between using discount rates as high as 0.015% rather than the 0.001% favored by Nicholas Stern in assessing climate policy.

There is a lot more besides this in the paper including the effects of delay and non-renewable resources and the problem of global policy coordination,

Abstract
This paper introduces endogenous and directed technical change in a growth model with environmental constraints and limited resources. A unique final good is produced by combining inputs from two sectors. One of these sectors uses "dirty" machines and thus creates environmental degradation. Research can be directed to improving the technology of machines in either sector. We characterize dynamic tax policies that achieve sustainable growth or maximize intertemporal welfare, as a function of the degree of substitutability between clean and dirty inputs, environmental and resource stocks, and cross-country technological spillovers. We show that: (i) in the case where the inputs are sufficiently substitutable, sustainable long-run growth can be achieved with temporary taxation of dirty innovation and production; (ii) optimal policy involves both "carbon taxes" and research subsidies, so that excessive use of carbon taxes is avoided; (iii) delay in intervention is costly: the sooner and the stronger is the policy response, the shorter is the slow growth transition phase; (iv) the use of an exhaustible resource in dirty input production helps the switch to clean innovation under laissez-faire when the two inputs are substitutes. Under reasonable parameter values (corresponding to those used in existing models with exogenous technology) and with sufficient substitutability between inputs, it is optimal to redirect technical change towards clean technologies immediately and optimal environmental regulation need not reduce long-run growth. We also show that in a two-country extension, even though optimal environmental policy involves global policy coordination, when the two inputs are sufficiently substitutable environmental regulation only in the North may be sufficient to avoid a global disaster.

How Should We Adjust Economic Institution Rankings for Size?

RePEc provides a ranking of top level economics institutions as well as the number of authors at each institution. This ranking has been criticized online because it ranks MIT below the World Bank, NYU, and Columbia. Everyone knows that that isn't true. But how could we come up with a better ranking? If bigger institutions are better up to a point then it won't help us to either have the original data and compute a ranking on the basis of the average score of authors at those institutions. Nor will it help to use modeling approaches for ranked data to adjust the ranks for a standardized institution size.

I think it is obvious that size matters. French and Blanchflower make Dartmouth a very good economics institution for its size. On this chart:



You can see that Dartmouth is on the "frontier". So are the Minnesota Federal Reserve, Princeton, Chicago, and Harvard. MIT, Tel Aviv and UC Berkeley are just behind the frontier. But does this mean that the former 5 should be considered the top 5 quality institutions? OTOH, clearly ANU, Oxford, and the World Bank are a long way behind the quality frontier despite their size.

Energy and Growth Survey: Conclusions

We conclude that the theoretical and empirical evidence indicates that energy use and output are tightly coupled with energy availability playing a key role in enabling growth. However, the greater availability of energy, technical progress, and the employment of higher quality fuels has allowed less energy to be used per unit output and has reduced the constraint that energy resources place on the output of the economy and economic growth. Even so, energy remains important.

Energy is important for growth because production is a function of capital, labor, and energy, not just the former two or just the latter as neoclassical growth models or biophysical production models taken literally would indicate. Both theory and time series results support these claims. Furthermore, the elasticity of substitution between energy and capital is likely to be low and energy is needed to produce the other inputs to production, is available in finite quantities on the Earth’s surface, and is non-recyclable.

However, the estimated output elasticity of energy and natural resources in general should be small in recent decades reflecting the market price determined output share. The current low price of energy reflects a low marginal productivity because of this heavy use. Resources have become increasingly abundant since the Industrial Revolution - evidence suggests that the energy cost share has declined continuously since then alongside the energy intensity of GDP.

Various factors have contributed to declining energy intensities but research is less clear on the relative importance of these variables. Different energy qualities have differing productivities. In particular, modeling the effect of electricity on output is important. Part of the reduction in energy intensities in developed economies may be due to the shift to higher quality fuels. Some research indicates that most of the historical reductions in energy intensity in developed economies and China have been due to technical change but other research finds a much larger role for structural change. Technological change tends to be offset to some degree by the rebound effect. Structural change towards more service-intensive economies tends to have less impact than is commonly thought because service industries in fact need energy intensive infrastructures. In fact energy-saving technical progress in manufacturing industry that reduces the apparent share of manufacturing in the economy may be more important.

As this survey shows, there is clearly much scope for further research to clarify the prospects for decoupling energy use and economic growth and for understanding the role of energy in growth.

Position at Lund University

Lund University are advertising a position in sustainable development and energy. You need to be less than 5 years post-PhD except in attenuating circumstances. You need to be able to teach in English. The emphasis is on the research side. My collaborator Astrid Kander is director of the platform for economic energy research at Lund and you can contact her for more information. I'm planning to visit Lund later this year.

Grattan Institute CPRS Report

The Grattan Institute has a report on the CPRS out today. The CPRS is in hibernation or maybe dead but the report argues as most of us did that the compensation proposed by the government was not economically justified. Here is their blurb:

"Like much of the world, Australia has debated putting a price on carbon emissions (a “carbon price”) with an emissions trading scheme or tax. A carbon price aims to induce structural change in the economy that will reduce emissions and consequently the risks of global warming.

The Australian debate has been dominated by concerns that Australia might lose industry and jobs offshore if it has a carbon price when competitor countries do not. If Australian production moves to countries with higher emissions, this would defeat the purpose of carbon pricing. To avoid this possibility, and protect industry from such an event, government plans to provide some industries with free carbon permits. The report is a detailed industry by industry analysis of the impact of carbon pricing.

We find that much of the protection proposed for the major emissions-intensive industries is unnecessary or poorly targeted. It would delay the structural adjustment required to move to a lower carbon economy.

Using industry data, the report finds that many of the recipient companies will be internationally competitive even if they receive no free permits. Many of the industries that would not be competitive would emit less carbon if they moved offshore, which is the purpose of carbon pricing. The proposed free permits will mute the incentives to reduce carbon emissions. They are also very expensive for other Australian taxpayers."

Shifts in the Composition of Output

This is the penultimate section of the paper that I'll post. Now I'm going on to rewriting the conclusions and then a massive edit. I've got 15,000 words and 179 references!

Shifts in the Composition of Output

Output mix typically changes over the course of economic development. In the earlier phases of development there is a shift away from agriculture towards heavy industry, while in the later stages of development there is a shift from the more resource intensive extractive and heavy industrial sectors towards services and lighter manufacturing. Different industries have different energy intensities. It is often argued that this will result in an increase in energy used per unit of output in the early stages of economic development and a reduction in energy used per unit output in the later stages of economic development (Panayotou, 1993).

However, there is reason to believe that the energy-saving effects of structural changes are overstated. When the indirect energy use embodied in manufactured products and services is taken into account, the US service and household sectors are more energy intensive than they first appear (Costanza, 1980). Service industries still need large energy and resource inputs. The service being sold may be intangible but the office towers, shopping malls, warehouses, rental apartment complexes etc. where the activity is conducted are very tangible and energy is used in their construction, operation and maintenance. Furthermore, consumers use large amounts of energy and resources in commuting to work, shop etc.

The effect of the Internet on the energy intensity of commerce has received increasing attention (Yi and Thomas, 2007). Obviously, individual technologies such as news websites vs. newspapers can greatly reduce emissions (e.g. Toffel and Horvath, 2004) but the effects on other activities could outweigh the gains. Romm et al. (1999) argue that the environmental costs of the greater dispersal of population engendered by telecommuting would not outweigh the reduction in commuting costs suggesting a strong energy-conserving Internet effect. But Matthews et al. (2002) and Williams and Tagami (2008) provide evidence that online book retailing use more energy than traditional retail while Herring and Roy (2002) show that electronic distance learning results in more energy use than traditional distance learning with printed material.

There may also be a tendency for consumers to use more energy directly over time as their consumption of the services appliances, housing, transport etc. increases. Judson et al. (1999) find that the consumer sector sees rising energy intensity over time, ceteris paribus, while the manufacturing sector sees decreasing energy intensity.

Furthermore, on a global scale there may be limits to the extent to which developing countries can replicate the structural shift that has occurred in the developed economies to the extent that this has occurred by outsourcing manufacturing overseas rather than simply from an expansion in service activities. However, the evidence shows that trade does not result in reductions in pollution in developed countries through the off-shoring of pollution intensive industries (Levinson, 2010, Aguayo and Gallagher, 2005; Kander and Lindmark, 2006). Additionally, if the service sector does require substantial material support, it is not clear whether the developed world can continue to shift in the direction of a growing service share of GDP indefinitely. In fact, as manufacturing prices have fallen relative to the prices of services (Baumol’s disease), even the relative decline of manufacturing in developed countries is exaggerated when the relative sizes of the sectors are computed in current prices (Kander, 2005).

Kander (2002) and Stern (2010) find a relatively small role for structural change in reducing energy intensity in Sweden (1800-2000) and the world (1971-2007), respectively. But, using a much finer disaggregation of industries, Sue Wing (2008) finds that structural change explained most of the decline in energy intensity in the United States (1958-2000), especially before 1980.

References
Aguayo, F., and K. P. Gallagher (2005) Economic reform, energy, and development: the case of Mexican manufacturing, Energy Policy 33: 829–837.
Costanza, R. (1980). “Embodied energy and economic valuation.” Science 210: 1219-1224.
Herring, H. and R. Roy (2002). “Sustainable services, electronic education and the rebound effect.” Environmental Impact Assessment Review 22: 525-542.
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.
Kander, A. (2002). Economic Growth, Energy Consumption and CO2 Emissions in Sweden 1800-2000, Lund Studies in Economic History No. 19, Lund, Sweden.
Kander, A. (2005). Baumol's disease and dematerialization of the economy, Ecological Economics 55(1): 119-130.
Kander, A. and Lindmark, M. (2006). "Foreign trade and declining pollution in Sweden: a decomposition analysis of long-term structural and technological effects," Energy Policy 34(13): 1590-1599.
Levinson, A. (2010) Offshoring pollution: Is the United States increasingly importing polluting goods? Review of Environmental Economics and Policy 4(1): 63-83.
Matthews, H. S., E. Williams, T. Tagami, and C. T. Hendrickson (2002). “Energy Implications of Online Book Retailing in the United States and Japan.” Environmental Impact Assessment Review 22: 493-507.
Panayotou, T. (1993). Empirical Tests and Policy Analysis of Environmental Degradation at Different Stages of Economic Development. Working Paper WP238, Technology and Employment Programme, International Labour Office, Geneva.
Romm, J., A. Rosenfeld and S. Herrmann (1999). The Internet Economy and Global Warming: A Scenario of the Impact of E-Commerce on Energy and the Environment. The Center for Energy and Climate Solutions, The Global Environment and Technology Foundation, Arlington, VA.
Stern D. I. (2010) Modeling international trends in energy efficiency and carbon emissions, Environmental Economics Research Hub Research Report 54.
Sue Wing, I. (2008) Explaining the declining energy intensity of the U.S. economy, Resource and Energy Economics 30: 21–49.
Toffel, M. W. and A. Horvath (2004) Environmental Implications of Wireless Technologies: News Delivery and Business Meetings, Environ. Sci. Technol. 38(11): 2961–2970.
Williams, E. and T. Tagami (2008) Energy use in sales and distribution via e-commerce and conventional retail: A case study of the Japanese book sector. Journal of Industrial Ecology 6(2): 99 – 114.
Yi, L. and H. R. Thomas (2007) A review of research on the environmental impact of e-business and ICT, Environment International 33(6): 841-849.

Innovation and Energy Efficiency

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.
Popp, D. (2002). “Induced innovation and energy prices.” American Economic Review 92: 160-180.
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.

Do Liberal Arts Programs Work?

Can anyone point me to research on the effectiveness of the liberal arts model of education popular in US and Chinese universities compared to the more specialized approach prevalent in Europe and Australia? Which results in better educated citizens? This is particularly relevant with the advent of the "Melbourne Model". Is this a good compromise between the various models?

Econometrics and Economics

I went to two presentations at ANU recently by well-known American economists" Joshua Angrist and Frank Lichtenberg. Angrist presented work on the charter school in Lynn, MA. The presentation was clear and I thought quite impressive and convincing in showing a major impact of attending the school on students' performance, at least in mathematics. Lichtenberg showed the effect of diagnostic imaging and chemotherapy on cancer survival. I thought the results were fairly convincing that these procedures had large effects on reducing mortality.

I thought both these pieces of research were impressive but I wondered if they were economics. Angrist's work showed that charter schools increase education incomes but there was no policy evaluation or modeling of the decision to choose to apply to the charter school lottery etc. I asked Lichtenberg about what the results said about the cost effectiveness of research and development in the two areas. He hadn't done work on that. He did provide evidence of the monetary equivalent gain of the improved outcomes. I don't think it is a problem that this research isn't economics really. But the question then is: "If this is economics, is applying econometrics to climate data economics?" I think more economists would say no here and say that that kind of research doesn't belong in economics journals. Does the Angrist/Lichtenberg type of research belong in economics journals because it is about people (and uses econometrics)? So what is economics exactly?

Substitutability of Energy and Capital

There is a large empirical literature on the issue of whether capital and energy are substitutes or complements and on how substitutable they are (e.g. Berndt and Wood, 1979; Apostolakis, 1990; Thompson and Taylor, 1995; Frondel and Schmidt, 2002; Thompson, 1997; Stern, 2007; Koetse et al., 2008). Substitutability can be measured using the Hicks or direct elasticity of substitution, which measures how the ratio of two inputs changes in response to a change in the ratio of their prices while holding output constant. However, most of the empirical literature focuses on the concept of (price) substitutability versus (price) complementarity. Two inputs are said to be p-substitutes (p-complements) if the quantity of one increases (decreases) when the price of the other increases. Blackorby and Russell (1989) correctly state that “the elasticity of substitution concept, as originally conceived by Hicks, has nothing to do with the substitute/complement taxonomy” (885). That discrimination should be made according to the sign of the cross-price elasticity, which is necessarily the same as the sign of the Allen-Uzawa substitution elasticity.

Econometric studies have come to varying conclusions regarding whether capital and energy are complements or substitutes (Berndt and Wood, 1979; Apostolakis, 1990). Based on the differences between time series and cross-sectional results, Apostolakis (1990) concluded that capital and energy act more as substitutes in the long run and more as complements in the short run. Frondel and Schmidt (2002) revisit the studies reviewed by Apostolakis and additional data from Germany and find that evidence of complementarity only occurs in cases where the cost share of energy is small. When materials are included the cost shares of capital and energy are smaller and a finding of complementarity is more likely. More time series studies than cross-sectional studies have data on materials use. Obviously the cost of materials should be included if possible and econometric results that exclude this variable are likely to be biased. Similarly, Berndt and Wood (1979) found that econometric studies using the KLE specification (i.e. not including materials) and engineering studies indicate substitution, while cost functions with the KLEM specification indicate complementarity.

The Morishima elasticity of substitution (MES) is close to Hicks’ original idea of the elasticity of substitution. However, it is asymmetric – the elasticity takes different values, depending on whether the price of energy or capital increases. Koetse et al. (2008) conduct a meta-analysis of the MES and the cross-price elasticity (CPE) between capital and energy for an increase in the price of energy. Their base case finds that energy and capital are complements and that the MES is 0.216. The MES estimated using panel and cross-section data is greater (0.592 and 0.848, respectively) while the CPE is positive in cross-sectional data. It is likely that these larger values do reflect long-run elasticities and the lower values short-run elasticities (Stern, 2009). Koetse et al. (2008) found that exclusion of materials had no significant effect.

Kaufmann and Azary-Lee (1991) demonstrate the importance of accounting for the physical interdependency between manufactured and natural capital. They use a standard production function to account for the indirect energy used elsewhere in the economy to produce the capital substituted for fuel in the U.S. forest products sector. They found that from 1958 to 1984 the indirect energy costs of capital offset a significant fraction of the direct fuel savings. In some years, the indirect energy costs of capital are greater than the direct fuel savings. The results of Kaufmann and Azary-Lee’s analysis are consistent with the arguments made above that substitution possibilities are different at macro and micro levels.

It seems that, in conclusion, that the micro-level elasticity of substitution between capital and energy is less than unity, especially in the short-run. Capital and energy are likely p-complements in the short-run and p-substitutes in the long-run.

References
Apostolakis, B. E. (1990). “Energy-capital substitutability / complementarity: the dichotomy.” Energy Economics 12: 48-58.
Berndt, E. R. and D. O. Wood (1979). “Engineering and econometric interpretations of energy-capital complementarity.” American Economic Review 69: 342-354.
Blackorby, C. and R. R. Russell (1989). “Will the real elasticity of substitution please stand up? (A comparison of the Allen/Uzawa and Morishima elasticities).” American Economic Review 79: 882-888.
Frondel, M. and C. M Schmidt (2002). “The capital-energy controversy: An artifact of cost shares?” The Energy Journal 23(3): 53-79.
Kaufmann, R. K. and I. G. Azary-Lee (1991) “A biophysical analysis of substitution: Does substitution save energy in the U.S. forest products industry?” In D. P. Bradley and P. O. Nilsson (eds.) Ecological Economics: Implications for Forest Management and Practice, The Swedish University of Agricultural Sciences, Garpenberg, 111-123.
Koetse, M. J., H. L. F. de Groot, and R. J. G. M. Florax (2008) Capital-energy substitution and shifts in factor demand: A meta-analysis, Energy Economics 30: 2236–2251.
Stern D. I. (2009) Interfuel substitution: A meta-analysis, Environmental Economics Research Hub Research Report 33.
Thompson, H. (1997). “Substitution elasticities with many inputs.” Applied Mathematics Letters 10(3): 123-127.
Thompson, P. and T. G. Taylor (1995). “The capital-energy substitutability debate: A new look.” The Review of Economics and Statistics 77: 565-569.

The Ecological Economics Critique

This is a long section of the paper. Again if you have suggestions for additional references etc. please let me know. Either you'll get your paper cited or your help acknowledged in the final paper.

Introduction

Ecological economists derive their view of the role of energy in economic growth from the biophysical foundations of the economy discussed above. While mainstream growth theory focuses on institutional limits to growth (e.g. Solow, 1978, 1993, 1997), ecological economists tend instead to focus on the material basis of the economy (e.g. Georgescu-Roegen, 1971; Costanza, 1980; Cleveland et al., 1984; Hall et al., 1986, 2001, 2003; Murphy and Hall, 2010). This view is shared by some geographers (e.g. Smil, 1994) and economic historians (e.g. Wrigley, 1988; Allen, 2009) who believe that energy plays a crucial role in economic growth, as well as being an important factor in explaining the industrial revolution. Ecological economic criticism of mainstream growth theory focuses on limits to substitution between capital and resources and limits to technological progress as ways of mitigating the scarcity of resources. If these two processes are limited then limited resources or the degradation of ecosystem services may restrict growth.

A prominent tradition in ecological economics is represented by biophysical models where energy is considered to be a primary factor of production and the only such primary factor. In this view, all value is derived from the action of energy that is directed by capital and labor. Payments to capital and labor represent a rent appropriated by the owners of these inputs but stemming from the productivity of energy (Costanza, 1980; Hall et al., 1986; Gever et al., 1986; Kaufmann, 1987). The flow of energy in the economy is the service of the reservoirs of fossil fuels and the sun, which represent a primary input in our terminology. In some biophysical economic models (e.g. Gever et al., 1986) geological constraints fix the rate of energy extraction so that the flow rather than the stock can be considered a primary input. On the other hand, capital and labor are treated as flows of capital consumption and labor services rather than as stocks, in other words, they are considered as intermediate inputs that are created and maintained by the primary input of energy and flows of matter. The value of the flows is computed in terms of the embodied energy use associated with them. Prices of commodities should then be determined by embodied energy cost (Hannon, 1973b) – a normative energy theory of value - or are actually correlated with energy cost (Costanza, 1980) - a positive energy theory of value (Common, 1995). This theory – like the Marxian paradigm - must then explain how labor, capital etc. end up receiving part of the surplus. Energy surplus must be appropriated by the owners of labor, capital, and land with the actual distribution of the surplus depending on the relative bargaining power of the different social classes and foreign suppliers of fuel (Kaufmann, 1987). If we assume that there are constant returns to scale, the production process of the economy as a whole can be represented by a Leontief input-output model with a single primary factor of production (Hannon, 1973a; Stern, 1999).

Cleveland et al. (1984), Hall et al. (1986), Hall et al. (2003), and Ayres and Warr (2005) among others argue that either energy, properly accounted for, accounts for most apparent productivity growth, or that technological change is real but innovations increase productivity by allowing the use of more energy. Therefore, increased energy use is the main or only cause of economic growth.

It is difficult to argue for this pure energy model as matter and organization or information are obviously important as discussed above. For example, the quality of resources such as oil reservoirs is critical in determining the energy required to extract and process fuels and other intermediate resource flows, which increases as the quality of resources declines over time with depletion. Changing resource quality results in changes in the embodied energy of the intermediate inputs indicating that human directed energy must be substituted for the services provided autonomously by nature. Odum’s emergy approach (see Brown and Herendeen, 1996; see also the framework developed by Costanza, 1980) also includes embodied solar and geological energy in indicators of total embodied energy. Thus changing resource quality is represented by changes in the embodied energy of the primary resources themselves. But this approach seems too reductionist. Other services provided by nature such as nutrient recycling, the provision of clean air and water, pollination, the climate system, and so on should also then be accounted for. These ecosystem services provide the conditions that make economic production—and life itself—possible.

A more sophisticated approach with a variety of different types of factors of production was already formulated by Georgescu-Roegen (1971). The neo-Ricardian models developed by Perrings (1987) and O'Connor (1993) also allow any number of inputs while complying with thermodynamic and mass-balance constraints.

Resource Quality and Economic Output
EROI – energy return on investment – is the ratio of useful energy produced by a method of energy supply for the amount of energy invested in extracting that energy. Lower quality energy sources have lower EROIs. Biomass usually produces less useful energy relative to the human energies expended in growing, harvesting, and processing the crop than and oilfield produces relative to the energy expended in discovering, extracting, and processing the oil. Larger, shallower oilfields typically have higher EROIs than smaller, deeper fields, and the EROI of an oilfield declines over time as pressure falls due to the extraction of oil.

Biophysical economists argue that the more energy that is required to extract energy the less energy is available for other uses and the poorer an economy will be. In pre-industrial societies most workers were engaged in growing food and collecting fuel. Only a small fraction of society could use the small energy surplus generated to produce other products and services. In other words, as energy needs to be used with other inputs, most of societies’ factors of production were directed to energy extraction. In this view, the increase in EROI allowed by the switch from biomass to fossil fuels enabled the industrial revolution and the period of modern economic growth that followed it (Hall et al., 1986).

Declining EROI would threaten not just growth but also the level of output of the economy and, therefore, sustainability. Murphy and Hall (2010) document EROI for many energy sources, arguing that it is declining over time. Wind and direct solar energy have more favorable EROIs than biomass fuels but worse than most fossil fuels. However, unlike fossil fuels, the EROI of these energy sources tends to improve over time with innovation (Kubiszewski et al., 2010). But current usage is very small and Murphy and Hall argue that there is no prospect of them replacing a large part of fossil fuel usage in the near future.

Declining EROI could be mitigated by substituting other inputs for energy or by improving the efficiency with which energy is used. However, biophysical economics argues that both these processes have limits.


Limits to Substitution
There is more than one type of substitution between inputs and, therefore, there is more than one reason why substitution may be limited. There can be substitution within a category of similar production inputs – for example between different fuels - and between different categories of inputs – for example between energy and machines. There is also a distinction to be made between substitution at the micro level - for example in a single engineering process or in a single firm – and at the macro level – in the economy as a whole. Finally, some types of substitution that are possible in a single country are not possible globally.

Solow (1997) argues that within category substitution, and in particular the substitution of renewable for nonrenewable resources, is most important and seems to assume that new substitutes will always be found. It is possible that the elasticity of substitution for within category types of substitution exceeds unity. The long run pattern of energy use in industrial economies has been dominated by the substitutions from wood and waterpower to coal, oil, natural gas and primary electricity (Hall et al., 1986; Smil, 1991). In large part the industrial revolution was enabled by the use of fossil fuels that freed economic activity from reliance on low power and variable but renewable solar energy. When fossil fuels are economically exhausted the next stage of energy development may see a return to solar energy, albeit captured in a more sophisticated way, rather than a move to a new substitute. Meta-analysis of existing studies of interfuel substitution (Stern, 2009) suggests that the long-run elasticity of substitution between coal and natural gas is greater than unity and that that between oil and electricity is less than unity with the other interfuel elasticities insignificantly different from unity. But the values of the elasticities are very sensitive to the estimator used and more research is needed.

Ecological economists emphasize the importance of limits to the between category type of substitution, and in particular, the substitution of manufactured capital for resources including energy (Costanza and Daly, 1992). A number of arguments for limited substitutability have been put forward, with the main ones that are relevant to the energy case described below. The terms “substitute” and “complement” have been used very loosely in this literature as elsewhere in economics (Stern, 2007). Instead, we can classify inputs as good or poor substitutes as measured by the Hicks or Direct Elasticity of Substitution. This elasticity reflects movement along an isoquant of the production function holding all other inputs constant. It can take values from zero (Leontief function) to infinity (linear production). Good and poor substitutes have elasticities of substitution of greater than and less than unity, respectively. A meta-analysis of the existing empirical literature finds that the elasticity of substitution between capital and energy is less than unity (Koetse et al., 2008).

Thermodynamic limits to substitution. Thermodynamic limits to substitution are easily identified for individual processes by an energy-materials analysis that defines the fundamental limitations of transforming materials into different thermodynamic states and on the use of energy to achieve that transformation (Ruth, 1993; Islam, 1985). It might be argued that standard production functions can account for mass balance and thermodynamic constraints if the elasticity of substitution between capital and resources is less than or equal to unity so that resources are essential. The Cobb-Douglas production function has the essentiality condition. Given positive non-energy inputs, output is only zero when the energy input is zero, and strictly positive otherwise. This at least accounts for the fact that some amount of energy and materials are required to produce goods and services. But when the elasticity of substitution is unity this “essential” amount can be infinitesimal if sufficient manufactured capital is applied. Therefore, this condition does not satisfy thermodynamic considerations throughout the domain of the function. Thermodynamic limits can be approximated by a production function with an elasticity of substitution significantly below unity.

Material cause and efficient cause. Georgescu-Roegen’s (1976) fund-flow model describes production as a transformation process in which a flow of materials, energy, and information – the material cause - is transformed by two agents of transformation, human labor and manufactured capital – the efficient cause that effect the transformation. Thus, Daly (1991) argues that adding to the stock of pulp mills (efficient cause) does not produce an increase in pulp unless there also is the wood fiber (material cause) to feed them. From this perspective, capital should be a poor substitute for energy and other resources.

Mainstream economists think about this question differently. First, they argue that though additional capital cannot conjure wood fibers out of thin air, more capital and “smarter” capital can be used with each amount of wood fibers to produce more sophisticated and valuable products, and that this is the relevant substitution between capital and resources. Thermodynamic limits only apply to production of specific physical products. There is then no limit to the potential value of product created through sophisticated manipulation using larger amounts of capital (van den Bergh, 1999).


Physical interdependence and macroeconomic and global limits to substitution. The construction, operation, and maintenance of tools, machines, and factories require a flow of materials and energy. Similarly, the humans that direct manufactured capital consume energy and materials in the form of food, water, and other subsistence needs. Thus, producing more of the “substitute” for energy - manufactured capital - requires more of the thing that it is supposed to substitute for. This again limits potential substitutability.

Ecological economists argue that production functions used in growth models do not account for this interdependence, and thus assume a degree of substitutability that does not exist (Georgescu-Roegen, 1979; Cleveland et al., 1984; Ayres and Nair, 1984; Kaufmann, 1992; Daly, 1997, Stern, 1997). But we must distinguish between micro-and macro-applications of production functions. Substitution seems to be fundamentally more constrained at the macro-level of analysis than at the micro-level (Stern, 1997). For example, home insulation directly substitutes for heating fuel within the household sector. But that insulation requires fuel to manufacture, so for the economy as a whole the net substitution of insulation for fuel is less than that indicated by an analysis of the household sector in isolation from the rest of the economy. Put another way, the aggregate of potential energy savings at the macroeconomic level is less than the sum of the savings one would calculate by adding the savings from sectoral-level analyses that do not account for the indirect costs.

















In the figure, the curve E = f(M) is a neoclassical isoquant for a constant level of output, where E is energy, and M materials, including the material embodied in capital. The indirect energy costs of materials are represented by g(M). For simplicity, the diagram unrealistically assumes that no materials are required in the extraction or capture of energy. Addition of direct and indirect energy costs results in the "net" isoquant E = h(M). Generalizing for material costs to energy extraction suggests that there are eventually decreasing returns to all factors at the macro level and therefore the socially efficient region of the aggregate production function does not include areas with extreme factor ratios. This idea may be supported by a meta-analysis of the capital-energy elasticity of substitution that shows a lower elasticity for more aggregated sectors than for less aggregated sectors (Koetse et al., 2008).

Additionally, at the global level, countries such as Kuwait, Nauru, or Norway can deplete their natural resources and invest in manufactured capital offshore through the financial markets. But this route to substituting manufactured capital for natural capital is clearly not possible for the world as a whole.

Limits to Technological Change
But, as discussed above, if substitution possibilities are limited, sustainability may be possible if technological change is resource augmenting and unlimited in scope. This argument would be more convincing if technological change were really something different from substitution. This is not really the case. The neoclassical approach assumes that an infinite number of efficient techniques coexist at any one point in time. Substitution occurs among these techniques. Changes in technology occur when new more efficient techniques are developed. However, these new techniques really represent the substitution of knowledge for the other factors of production. The knowledge is embodied in improved capital goods and more skilled workers and managers, all of which require energy, materials, and ecosystem services to produce and maintain. Thus, however sophisticated the workers and machinery become, there are still thermodynamic restrictions on the extent to which energy and material flows can be reduced.

The difference between knowledge and other forms of capital is that knowledge is non-rival in use – in other words the same idea can be used simultaneously in different locations and production processes without any reduction in the productivity of the knowledge in the different locations and processes. This means that there are constant returns to the application of knowledge in production while other inputs experience diminishing returns. But knowledge must be used in conjunction with the other inputs such as energy. The productivity of knowledge is still determined by the available quantities of those inputs.

References
Allen, R. C. (2009) The British Industrial Revolution in Global Perspective, Cambridge University Press.
Ayres, R. and I. Nair (1984). “Thermodynamics and economics.” Physics Today 35: 62-71.
Ayres, R. U. and B. Warr (2005) Accounting for growth: the role of physical work, Structural Change and Economic Dynamics 16: 181-209.
Brown, M. T. and R. A. Herendeen (1996). “Embodied energy analysis and emergy analysis: a comparative view.” Ecological Economics 19: 219-236.
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.
Common, M. S. (1995). Sustainability and Policy: Limits to Economics. Melbourne: Cambridge University Press.
Costanza, R. (1980). “Embodied energy and economic valuation.” Science 210: 1219-1224.
Costanza, R. and Daly, H. E. (1992). “Natural capital and sustainable development.” Conservation Biology, 6: 37-46.
Daly, H. E. (1991). “Elements of an environmental macroeconomics.” In: R. Costanza (ed.), Ecological Economics New York: Oxford University Press. pp. 32-46.
Daly, H. E. (1997). “Georgescu-Roegen versus Solow/Stiglitz.” Ecological Economics 22: 261-266.
Georgescu-Roegen N. (1971) The Entropy Law and the Economic Process, Harvard University Press, Cambridge MA.
Georgescu-Roegen, N. (1976). Energy and Economic Myths. New York: Pergamon.
Georgescu-Roegen, N. (1979). “Energy and matter in mankind's technological circuit.” Journal of Business Administration 10: 107-127.
Gever, J., R. K. Kaufmann, D. Skole, and C. Vörösmarty (1986). Beyond Oil: The Threat to Food and Fuel in the Coming Decades. Cambridge, MA: Ballinger.
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.
Hall, C. A. S., D. Lindenberger, R. Kümmel, T. Kroeger, and W. Eichhorn (2001). “The need to reintegrate the natural sciences and economics.” BioScience 51: 663-673.
Hall, C. A. S., P. Tharakan, J. Hallock, C. Cleveland, and M. Jefferson (2003). “Hydrocarbons and the evolution of human culture.” Nature 426: 318-322.
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Kaufmann, R. K. (1987). “Biophysical and Marxist economics: learning from each other.” Ecological Modelling 38: 91-105.
Kaufmann, R. K. (1992). “A biophysical analysis of the energy/real GDP ratio: implications for substitution and technical change.” Ecological Economics 6: 35-56.
Koetse, M. J., H. L. F. de Groot, and R. J. G. M. Florax (2008) Capital-energy substitution and shifts in factor demand: A meta-analysis, Energy Economics 30: 2236–2251.
Kubiszewski, I., C. J. Cleveland, and P. K. Endres. 2010. Meta-analysis of net energy return for wind power systems. Renewable Energy 35: 218–225.
Murphy D. J. and C. A. S. Hall (2010) Year in review – EROI or energy return on (energy) invested, Annals of the New York Academy of Sciences 1185: 102-118.
O'Connor, M. P. (1993). “Entropic irreversibility and uncontrolled technological change in the economy and environment.” Journal of Evolutionary Economics 34: 285-315.
Perrings, C. A. (1987). Economy and Environment: A Theoretical Essay on the Interdependence of Economic and Environmental Systems. Cambridge: Cambridge University Press.
Ruth, M. (1993). Integrating Economics, Ecology, and Thermodynamics. Dordecht: Kluwer Academic.
Smil, V. (1991). General Energetics Energy in the Biosphere and Civilization. John Wiley, New York.
Smil, V. (1994) Energy In World History, Westview Press.
Solow, R. M. (1978). “Resources and economic growth.” American Economist 22: 5-11.
Solow, R. M. (1993). “An almost practical step toward sustainability.” Resources Policy 19: 162-172.
Solow, R. M. (1997). “Reply: Georgescu-Roegen versus Solow/Stiglitz.” Ecological Economics 22: 267-268.
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. (1999). “Is energy cost an accurate indicator of natural resource quality?” Ecological Economics 31: 381-394.
Stern, D. I. (2007) The elasticity of substitution, the capital-energy controversy, and sustainability, in: J. D. Erickson and J. M. Gowdy (eds.) Frontiers In Ecological Economic Theory And Application, Edward Elgar, Cheltenham, 331-352.
van den Bergh, J. C.J. M. (1999). “Materials, capital, direct/indirect substitution, and mass balance production functions.” Land Economics 75 (4): 547-561.
Wrigley, E. A. (1988) Continuity, Chance, and Change: The Character of the Industrial Revolution in England, Cambridge University Press, Cambridge.

Neoclassical Growth Models with Resources and Technical Change

Another installment. I'm more uncertain about whether I'm getting the story right here. So comments are even more welcome. I've had none so far :(


Growth Models with Resources and Technical Change

In addition to substitution of capital for resources, technological change might permit growth or at least constant consumption in the face of a finite resource base. Stiglitz (1974a) showed that in a Cobb Douglas framework with exogenous technical progress that consumption will grow over time if the rate of technological change divided by the discount rate is greater than the output elasticity of resources.

Growing total factor productivity obviously makes sustainability technically easier to achieve and sustainability may be possible even with an elasticity of substitution of less than one. Once again, technical feasibility does not guarantee sustainability. Depending on preferences for current versus future consumption, current depletion may as a result be faster (Smulders, 2005). This result is related to the Khazzoom-Brookes postulate or rebound effect discussed below. As noted above, due to externalities in knowledge production there may be too little innovation in an endogenous growth world. As a result, depletion of a non-renewable resource is nonoptimal, but this rate could be either too fast or too slow.

Recent work (e.g. Aghion and Howitt, 1998; Barbier, 1999; Scholz and Ziemes, 1999; Groth and Schou, 2002; Grimaud and Roug, 2003; Di Maria and Valente, 2008) exploits endogenous growth theories to analyze capital–non-renewable resource economies. Initial work by Aghion and Howitt (1998) finds that an AK type model with essential nonrenewable resources cannot allow for unbounded growth in consumption while a Schumpetarian type model can. A Schumpetarian model with a renewable resource that affects utility directly can allow unlimited growth, but only under unlikely assumptions. But if the renewable resources do not affect utility, continued growth would be easier than in the non-renewable case. Smulders (1999) provides a survey of earlier endogenous growth work and Smulders and de Nooij (2003) and Di Maria and Valente (2008) provide references to the more recent literature. An aim of much of this literature is to determine whether, and under what circumstances, technical progress is effective in ensuring sustained consumption (Bretschger, 2005). A general finding is that the rate of resource augmenting progress must be strictly positive and at least equal to the discount rate to obtain non-declining consumption in the long run (Di Maria and Valente, 2008).

Tahvonen and Salo (2001) develop a model economy with both renewable and non-renewable energy resources that is both very general and more realistic than the earlier neoclassical literature (e.g. Solow, 1974). The models have extraction costs for fossil fuels and production costs for renewable energy resources, which also rise as cheaper sources are exploited first. The model can incorporate no technological change, exogenous technical change, and learning by doing, a form of endogenous technical change. They assume that technical knowledge in extraction increases proportionally to extraction and that technical knowledge in final production is proportional to the capital stock. The optimal development of such an economy appears to mimic history much more effectively than other neoclassical models based on the Solow-Stiglitz capital-non-renewable resource model. The economy passes through pre-industrial, industrial, and post-industrial eras as the use of fossil fuels first rises and then falls and capital is accumulated. The price of nonrenewables first falls and then rises.

References
Aghion, P. and P. Howitt (1998). Endogenous Growth Theory. : Cambridge, MA: MIT Press.
Barbier, E.B. (1999), ‘Endogenous growth and natural resource scarcity’, Environmental and Resource Economics 14: 51–74.
Bretschger, L. (2005), ‘Economics of technological change and the natural environment: how effective are innovations as a remedy for resource scarcity?’ Ecological Economics 54: 148–163.
di Maria, C. and S. Valente (2008) Hicks meets Hotelling: the direction of technical change in capital–resource economies, Environment and Development Economics 13: 691–717.
Grimaud, A. and L. Roug (2003), ‘Non-renewable resources and growth with vertical innovations: optimum, equilibrium and economic policy’, Journal of Environmental Economics and Management 45: 433–453.
Groth, C. and P. Schou (2002), ‘Can non-renewable resources alleviate the knife-edge character of endogenous growth?’ Oxford Economic Papers 54: 386–411.
Scholz, C. and G. Ziemes (1999), ‘Exhaustible resources, monopolistic competition, and endogenous growth’, Environmental and Resource Economics 13: 169–185.
Smulders, S. (1999). “Endogenous growth theory and the environment.” in J. C. J. M. van den Bergh (ed.), Handbook of Environmental and Resource Economics, Edward Elgar, Cheltenham, 89-108.
Smulders, S. (2005). “Endogenous technical change, natural resources and growth.” In: R. Ayres, D. Simpson, and M. Toman (eds.), Scarcity and Growth in the New Millennium. Washington, DC: Resources for the Future.
Smulders, S. and M. de Nooij (2003). “The impact of energy conservation on technology and economic growth.” Resource and Energy Economics, 25: 59–79.
Solow, R. M. (1974). “Intergenerational equity and exhaustible resources.” Review of Economic Studies, Symposium on the Economics of Exhaustible Resources: 29-46.
Stiglitz, J. E. (1974a). “Growth with exhaustible natural resources: efficient and optimal growth paths.” Review of Economic Studies, Symposium on the Economics of Exhaustible Resources: 123-138.
Tahvonen, O. and S. Salo (2001). “Economic growth and transitions between renewable and nonrenewable energy resources.” European Economic Review 45: 1379-1398.

Energy as a Factor of Production

This section is almost unchanged from the 2004 version. I haven't seen anything recent that adds much to what's here. Have you?


Energy as a Factor of Production
The potentially critical role of energy in economic production and growth is dictated by basic physical principles. The laws of thermodynamics and the conservation of matter describe the immutable constraints within which the economic system must operate (Ayres and Kneese, 1969; Boulding, 1966). The first law of thermodynamics (the conservation law) implies the mass-balance principle (Ayres and Kneese, 1969). In order to obtain a given material output, greater or equal quantities of matter must be used as inputs with the residual a pollutant or waste product. Therefore, there are minimal material input requirements for any production process producing material outputs. The second law of thermodynamics (the efficiency law) implies that a minimum quantity of energy is required to carry out the transformation of matter. Carrying out transformations in finite time requires more energy than these minima (Baumgärtner, 2004). All production involves the transformation or movement of matter in some way. Some matter must be moved or transformed though particular elements and chemicals may be substitutable. Therefore, there must be limits to the substitution of other factors of production for energy. All economic processes must, therefore, require energy, so that energy is always an essential factor of production (Stern, 1997). In practice, recycling cannot be 100% complete due to the enormous energy costs of collecting very diffuse wastes and, therefore, pollution reduction also becomes increasingly costly. As long as sufficient energy is available this does not as proposed by Georgescu-Roegen (1971) pose an ultimate limit to economic production (Biancardi, et al., 1993). But it does imply environmental disruption, and as resource concentration and quality fall from their historically high levels increasing energy costs in obtaining resources (Hall et al., 1986).

Two key concepts in the economics of production that are often confused in the debate on the role of energy in the economy are reproducibility and the distinction between primary and intermediate inputs. Some inputs to production are non-reproducible, while others can be manufactured, at a cost, within the economic production system and are said to be reproducible. Capital, labor, and in the longer term even natural resources, are reproducible factors of production, while energy and matter are nonreproducible factors of production. Energy vectors - fuels – and raw materials like minerals are in theory reproducible factors (Stern, 1999) though with the exception of agriculture and forestry they are usually harvested from nature and represent the accumulated work of the planet’s biogeochemical cycles, which in turn are powered by energy from the Sun and the Earth’s internal heat.

As neither energy nor matter are reproducible they must be captured from the environment with implied environmental disruption. This is especially relevant for energy as due to the entropy law, exergy – useful energy – cannot be recycled or reproduced. While some forms of energy capture are possibly more hazardous to human health or damaging to environmental quality, all methods – nuclear, fossil fuels, hydropower, windpower, biomass etc. - are environmentally disruptive. Solar energy is very diffuse compared to the concentrated stocks of fossil fuels and plants and animals are very inefficient converters of that energy into energy and work useful to people. This is why the shift to fossil fuels through the industrial revolution released the constraints on production and growth that existed previously (Wrigley, 2003).

Some aspects of organized matter - that is information - might also be considered to be non-reproducible inputs. Several analysts (e.g. Spreng, 1993; Chen, 1994; Stern, 1994; Ruth, 1995) argue that information is a fundamentally nonreproducible factor of production in the same way as energy, and that economics must pay as much consideration to information and its accumulation as knowledge as it pays to energy. Energy is necessary to extract information from the environment while active use of energy cannot be made without information and possibly accumulated knowledge. Obviously, energy can provide uncontrolled heating, lighting etc. without any activity on the part of economic agents. But even non-intelligent organisms need to use information to make controlled use of energy. For example, when plants use some sunlight for photosynthesis rather than just heating and lighting their leaves they are using the information in their genetic code to produce chlorophyll, construct chloroplasts, and generate sugar. Unlike energy, information and knowledge cannot be easily quantified. However, the fact that they must be incorporated into machines, workers, and materials in order to be made useful provides a biophysical justification for treating capital, labor etc. as factors of production. Though capital and labor are easier to measure than information and knowledge, their measurement is, still, very imperfect compared to that of energy (Stern, 1999).

Primary factors of production are inputs that exist at the beginning of the period under consideration and are not directly used up in production (though they can be degraded or accumulated from period to period), while intermediate inputs are those created during the production period under consideration and are used up entirely in production. Mainstream economists usually think of capital, labor, and land as the primary factors of production, while goods such fuels and materials are intermediate inputs. The prices paid for all the different inputs are seen as eventually being payments to the owners of the primary inputs for the services provided directly or embodied in the produced intermediate inputs (Stern, 1999).

This approach has led to a focus in mainstream growth theory on the primary inputs, and in particular, capital and labor. Once the centerpiece of the classical economic model, land, meant here to include all natural resource inputs to production, gradually diminished in importance in economic theory as its value share of GDP fell steadily in the 20th century (e.g. Schultz, 1951) and today is usually subsumed as a subcategory of capital. Energy and other resources are attributed a lesser and somewhat indirect role in the mainstream theory of production and growth. The primary energy inputs are stock resources such as oil deposits while the flow of energy available to the economy in any period is endogenous, though restricted by biophysical constraints such as the pressure in oil reservoirs and economic constraints such as the amount of installed extraction, refining, and generating capacity, and the possible speeds and efficiencies with which these processes can proceed (Stern, 1999). But these are not given an explicit role in the standard macroeconomic growth theories that focus on labor and capital. Therefore, understanding the role of energy in the mainstream theory of growth is not so straightforward and the role of energy as a driver of economic growth and production is downplayed.

References
Ayres, R.U., and A.V. Kneese (1969). “Production, consumption and externalities.” American Economic Review 59: 282-97.
Baumgärtner, S. (2004) Thermodynamic Models, In: J. Proops and P. Safonov (eds), Modelling in Ecological Economics, Cheltenham: Edward Elgar, 102–129.
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Boulding K. (1966) The Economics of the Coming Spaceship Earth, in H. Jarett (ed.), Environmental Quality in a Growing Economy, Johns Hopkins University Press, Baltimore MD.
Chen, X. (1994). “Substitution of information for energy: conceptual background, realities and limits.” Energy Policy 22: 15-28.
Georgescu-Roegen N. (1971) The Entropy Law and the Economic Process, Harvard University Press, Cambridge MA.
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.
Ruth, M. (1995). “Information, order and knowledge in economic and ecological systems: implications for material and energy use.” Ecological Economics 13: 99-114.
Schultz, T. W. (1951) “A framework for land economics – the long view.” Journal of Farm Economics 33: 204-215.
Spreng, D. (1993). “Possibilities for substitution between energy, time and information.” Energy Policy, 21: 13-23.
Stern, D. I. (1994). Natural Resources as Factors of Production: Three Empirical Studies. Ph.D. Dissertation, Department of Geography, Boston University, Boston MA.
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. (1999). “Is energy cost an accurate indicator of natural resource quality?” Ecological Economics 31: 381-394.
Wrigley, E. A. (2003). The First Industrial Revolution, Paper presented at the Colloquium on Energy, Economic Growth and Pollution, King’s College, Cambridge 24th-26th October.

Energy Mix and Energy Intensity

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
Berndt, E. R. (1978). “Aggregate energy, efficiency, and productivity measurement.” Annual Review of Energy 3: 225-273.
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.
Enflo, K., A. Kander, and L. Schön (2009) Electrification and energy productivity, Ecological Economics 68: 2808–2817.
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.
Kaufmann, R. K. (1992). “A biophysical analysis of the energy/real GDP ratio: implications for substitution and technical change.” Ecological Economics 6: 35-56.
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.
Stern D. I. (2010) Modeling international trends in energy efficiency and carbon emissions, Environmental Economics Research Hub Research Report 54.
Stern, D. I. (in press) Energy quality, Ecological Economics.
US Congress, Office of Technology Assessment (1990). Energy Use and the U.S. Economy. OTA-BP-E-57, U.S. Government Printing Office, Washington DC.
Zarnikau, J. (1996) Reexamination of the causal relationship between energy consumption and gross national product, Journal of Energy and Development 21: 229-239.

The Rebound Effect

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.