Australian economic models of greenhouse abatement

Environmental Science & Policy 1 (1998) 1±12

Australian economic models of greenhouse abatement Mark Diesendorf * Institute for Sustainable Futures, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007, Australia

Abstract A critical examination is o€ered of the assumptions underlying Australian computer models of the economic impact of greenhouse abatement in the energy sector, notably `top±down' models which estimate the impact on the whole economy instead of just the energy sector. Such an examination is needed because the Australian government and resource industries are using results obtained from these models as a basis for opposing international greenhouse targets. This examination aims to assist in demystifying the models for policy analysts, political scientists and environmental managers. It is argued that `top±down' models used in Australia have fundamental ¯aws. For instance, most assume, contrary to much empirical evidence, that markets for energy services are competitive and that there are no costless energy eciency options still to be implemented; they estimate costs but rarely bene®ts; they substitute dubiously derived parameters for speci®cations of technologies; and they often fail to perform sensitivity analyses. # 1998 Elsevier Science Ltd. All rights reserved. Keywords: Greenhouse response; Energy±economy models; `Top±down' models; Energy services; Sensitivity analysis; Ecient energy use

1. Introduction What are the costs and bene®ts of reducing greenhouse gas emissions in the energy sector? What models are currently being used, or could be used, to answer this question, and what are their respective assumptions and limitations? The answers to these questions are important for national greenhouse response strategies and in the regular deliberations of the Conference of the Parties (COP) to the Framework Convention on Climate Change. In July 1996 at the second COP the Australian Government aligned itself with the Organisation of Petroleum Exporting Countries (OPEC) by refusing to accept legally binding targets and timetables for reducing greenhouse gas emissions. The basis for this position was apparently some runs on an economic model of greenhouse response. Therefore this paper aims to evaluate the roles and limitations of the most prevalent type of computer model used in Australia to evaluate the economic im* Tel.: +61-2-9209-4350; Fax: +61-2-9209-4351; E-mail: [email protected] 1 The latter task is attempted to some extent by James (1996). 2 Earlier work with similar objectives is Kinrade (1992a). 1462-9011/98/$19.00 # 1998 Elsevier Science Ltd. All rights reserved. PII: S 1 4 6 2 - 9 0 1 1 ( 9 8 ) 0 0 0 0 2 - 1

plications of greenhouse gas abatement in the energy sector. This type of model examines the economic impact of these responses on the whole nation or on groups of nations and is known as a `top±down' model (de®ned in the next section). Similar models are used in other countries. The paper does not attempt to analyse the equations or the details of the economics in the models1. Instead it o€ers a nonmathematical explanation of the basic assumptions of `top±down' models for policy makers, political scientists, environmental managers, businesses and community groups which are concerned about the costs and bene®ts of greenhouse abatement in the energy sector2. To assist in bridging the gaps between the various disciplines interested in these models, a glossary is provided as an Appendix A. In focusing on greenhouse abatement, this paper shows that many important assumptions underlying the models are matters for debate by these wider interest groups, instead of `technical' matters for economic modellers whose skills involve speci®c mathematical and computational techniques. Hence the paper avoids jargon where possible, de®ning the economic and mathematical terms which are introduced in a manner which reveals their underlying assumptions.

2

M. Diesendorf / Environmental Science & Policy 1 (1998) 1±12

A model is just a simpli®ed representation of reality. Well-known examples are the medical model of achieving health, Newton's laws of motion, or any map or equation. A computer model could be de®ned as a model which, from a set of initial assumptions, provides a logical description of how a system performs, and hence can be represented on a computer. Computer models are potentially very useful for analysing complex systems with many variables and many interrelationships between these variables (Meadows and Robinson, 1985). Examples of complex systems are an ecosystem, the economy of a nation-state, or the interactions between economics and ecosystems in a particular resource industry. The assumptions and quantities in computer models must be speci®ed precisely and this can be valuable to modellers in clarifying their thinking, even before computer analysis takes place. A computer can manipulate much more information than the human mind and can keep track of many more interrelationships. If constructed correctly, computer models can process a very complicated set of assumptions to draw logical, error-free conclusions. Sometimes they can elucidate a counter-intuitive result. Computer models can easily test a wide variety of di€erent conditions and policies, thus providing a form of social experimentation that is much less costly and time-consuming than tests within the real social system (Meadows and Robinson, 1985). However, in practice many computer models fall short of this potential. This is often the case in economics, where questionable assumptions are buried in the currently dominant paradigm and where comparison with empirical observation is generally inadequate. In particular, it is pointed out in Section 3 that some assumptions are so important to the neoclassical economics paradigm that they are held even in cases where they con¯ict with clear empirical evidence. Unlike scienti®c models of the greenhouse e€ect3, economic models of greenhouse abatement tend to lack mechanisms for describing the relationships between di€erent variables and so, for simplicity, they tend often to assume that these relationships are very simple (e.g. linear) and that the unknown parameters can be determined empirically. In applied mathematics, this approach is known as `parametrising our ignorance'. It may lead to oversimpli®cation and the false belief that the mechanisms are understood.

3 There are of course uncertainties in some of the relationships in scienti®c models of the greenhouse e€ect, such as whether clouds will produce negative or positive feedbacks to global climate change. However, in these cases further research is being undertaken to resolve these uncertainties and determine the actual mechanisms.

2. `Bottom±up' versus `top±down' models 2.1. `Bottom±up' models `Bottom±up' or `engineering' models of greenhouse abatement start with data on the cost and performance of speci®c technologies in the energy sector. Given scenarios for future growth in the demand for energy services (see Appendix A), these models focus on the least-cost provision of technologies to meet these scenarios. But, they do not attempt to examine the impact of changes in the energy sector on the rest of the economy. As a result of this approach and because most of them do not assume that the business-as-usual (BAU) scenario is necessarily economically optimal, `bottom± up' models generally ®nd that some reductions in greenhouse gas emissions can be achieved with net cost savings (see Section 3.2). However, provided the input data on technologies are accurate, they may overestimate the practical potential, because they neglect social, political and institutional constraints on the uptake of cost-e€ective technologies (Sutherland, 1991). The principal `bottom±up' model used in Australia is MENSA, developed from the International Energy Agency's MARKAL model by the Australian Bureau of Agricultural and Resource Economics (ABARE). Since MENSA is well documented and suitable for contrasting with `top±down' models, it is now considered in a little more detail. MENSA is a dynamic, multi-period linear programming model of Australia's energy sector. That is, given an exogenous (i.e. externally speci®ed) baseline scenario for the future demand for energy services over (say) the next 30 years, MENSA will calculate the minimum-cost mix of energy supply and demand-reducing technologies in its database to provide for that scenario in (say) 5-year timesteps. MENSA then repeats the calculation with an imposed constraint on greenhouse gas emissions and obtains a new minimumcost mix of technologies which has greater use of energy ecient, renewable energy and natural gas technologies. The MENSA modellers acknowledge that they cannot run a BAU scenario, because it is not an economic optimum. Hence their baseline, known as the `unconstrained optimum', is necessarily an arti®cially constructed scenario with lower current energy use and greenhouse gas emissions than the present pattern of energy use (Jones et al., 1991). MENSA is only as good as its database. Until recently this was better for energy supply technologies than for ecient energy use technologies. In particular, the thermal eciencies of some of its energy-using products, such as industrial boilers, were previously overestimated, thus leaving less room for low-cost and

M. Diesendorf / Environmental Science & Policy 1 (1998) 1±12

costless improvements in energy eciency (Sustainable Solutions P/L and Forster Electronic Services P/L, 1994). Furthermore, a recent set of runs, which uses improved energy eciency data in the residential sector, shows that improvements in the database o€er a greater role for ecient energy use. Indeed, under several scenarios residential energy use and greenhouse gas emissions could stabilise and then decline, with substantial cost savings (Naughten et al., 1994). Hence, it appears that, until now, MENSA has been underestimating the potential for costless reductions in greenhouse gas emissions and therefore has been including more of the currently expensive solar energy technologies than is necessary to meet greenhouse targets. Since the demand for energy services is ®xed outside the operations of MENSA, there is currently no provision for this demand to change with energy prices (Hamilton and Common, 1994). MENSA is a useful tool for examining changes within the energy sector resulting from meeting greenhouse targets. But, policy makers should keep in mind that MENSA only answers the question: ``If the social, political and institutional barriers to ecient energy use and renewable energy were removed, and if the demand for energy services continued to grow at a particular rate, what would it cost to meet greenhouse constraints, given the input data provided on technologies and costs?''

2.2. Top±down models Unlike `bottom±up' models, `top±down' models examine the aggregated behaviour of the whole economy under greenhouse response. They have no explicit representation of technologies, which are usually replaced by two types of parameters: (i) the rate of reduction of energy intensity (energy divided by GDP) which is not the result of price changes, otherwise known as the `autonomous end-use energy-intensity improvement' (AEEI), and (ii) `elasticities'. In the present context the latter represent changes in energy demand and various types of energy supply as a result of price changes. In economic modelling of greenhouse abatement, both types of parameter are assumed to be constants and are allegedly determined empirically. The validity of doing this is questioned in Sections 3.5 and 3.6. In both `top±down' and `bottom±up' models are generally considered to be the most important determinants of human behaviour. With a `top±down' approach, this results in focusing on energy price changes via carbon taxes (and, to a lesser degree,

3

tradeable emission permits) as the main instrument for reducing emissions, and on impacts on gross national product (GNP) or gross domestic product (GDP) as the principal measure of economic impact. Hence the bene®ts of low-cost policy changes tend to be ignored. `Top±down' models su€er particularly from the assumption that future institutional arrangements will be the same as in the past. Almost all `top±down' models assume that the markets for energy and energy services are competitive and hence that there are no `no regrets' options for reducing greenhouse gas emissions. `No regrets' options are de®ned as having nongreenhouse economic bene®ts greater than or equal to their economic costs. From an economics perspective, they are measures which should be implemented whether there is a greenhouse e€ect or not. They mostly involve unimplemented measures to use energy more eciently. Ignoring `no regrets' options entails that `top±down' models tend to overestimate the cost of greenhouse response and underestimate the bene®t (see Section 3.2).

2.3. Political implications The structures of `top±down' and `bottom±up' models, and the wide range of possible assumptions which can be made within these models, creates potential for their use as political tools. Since 1989 much media publicity against substantial greenhouse gas abatement measures in Australia has been associated with the public release of the results of economic modelling using `top±down' models. The model MEGABARE is the basis for the Australian government's international position which, together with the OPEC countries, is opposed to international greenhouse abatement targets and protocols. `Top±down' models used on their own in Australia tend to be funded either directly by resource industries which are large greenhouse gas emitters or by government agencies which are likely to be sympathetic to these industries. On the other hand, funding for bottom±up models, used either alone or in combination with `top±down' models, has come from a Department of the Environment, a Solar Energy Council and the Electrical Supply Association of Australia (ESAA) which represents a mixture of fossil fuel and other interests (see Table 1). Subsequent sections of this paper focus on some of the basic assumptions underlying the main `top±down' models used to study the costs of greenhouse abatement in Australia: ORANI, IMP, G-Cubed and MEGABARE.

US EPA, Aust. Mining Industry Fed. Govt McKibbin et al. (1994); McKibbin and Wilcoxen (1995) ESD Working Groups (1991, ch. 7), Jones et al. (1991); ESD Working Group Chairs (1992, ch. 5)

DFAT and ABARE (1995)

Brooker and O'Meagher (1991) London Economics (1992)

G-Cubed MENSA

top±down, time dependent, but not truly dynamic top±down, dynamic bottom±up, linear optimisation MEGABARE

top±down, comparative static top±down ORANI-F ERM + bits

ORANI-F NIEIR Demand + IMP NIEIR Demand + IMP Ð ORANI; WEDGE Ð

CRA Vic. Solar Energy Council Vic. Govt; Elec. Supply Assoc. of Aust. BCA Internal Members of Tasman Institute (notably greenhouse gas producers) Fed. Govt BHP, CRA, Shell Aust., Aust. Coal Assoc., AMIC, UMF Fed. Govt., Aust. Coal Assoc., BCA, etc Marks et al. (1989) NIEIR (1990) NIEIR (1991); NIEIR (1995 Burmot Australia P/L (1991) Industry Commission (1991) Moran and Chisolm (1991)

Model name Funders Author and year

Table 1 Australian economic models of greenhouse response

top±down, comparative static bottom±up + top±down bottom±up + top±down neither both top±down, comparative static welfare economic

M. Diesendorf / Environmental Science & Policy 1 (1998) 1±12

Model type

4

3. Summary of limitations of `top±down' models 3.1. One-sided cost±bene®t analysis Economic models attempt to calculate the cost of greenhouse abatement, but rarely weigh this against the bene®ts. The latter include both the direct economic bene®ts of implementing an ecological sustainable energy system Ð which may include net employment creation (Moeller, 1985; ACF and ACTU, 1994), growth in exports of new technologies (Blakers and Diesendorf, 1996), and reduced imports of fossil fuels Ð and the environmental and health bene®ts of reducing human-induced global climate change (European Commission, 1996) and local pollution. William Nordhaus, a leading US economic modeller, has claimed that the only signi®cant economic damage of global climate change, at least in the USA, is minor impact on the agricultural industry, which is in turn a very small part of the (US) economy (Nordhaus, 1990, 1991). His results follow from the assumption that a commercial discount rate can be applied to this environmental damage, a procedure which some environmental managers would see as a violation of the principle of intergenerational equity. His results have been applied globally on the assumption that the economic impacts in less developed countries are the same as in the USA. This does not take into account the fact that many less developed countries do not have the resources to respond e€ectively to the impacts of climate change. Recently a major set of studies of the global environmental and health costs of global climate change has been conducted by the European Commission (1996). It considers Ð as well as damage to agriculture Ð additional ®res, losses of forests, insurance losses, cases of asthma and allergy, problems for sanitation and fresh water, and deaths from heatwave, droughts ¯oods, starvation, malaria, schistosomiasis, cholera, etc. It ®nds that the estimated `external' costs of producing and using fossil fuels are generally greater than or roughly equal to the standard economic costs, while the `external' costs of using direct solar and wind energy are very much less than their standard economic costs. Under the assumptions of intergenerational equity (i.e. that future environmental and health costs are not discounted) and that the value of human lives in developing and developed countries are each equal to the standard US ®gure of $3 M per person, Soerensen (1995, 1997), one of the consultants to the European Commission, estimates the total global cost over the whole of the 21st century arising from CO2 doubling to be about US$1015. This corresponds to an average of US$1013 per annum, which is roughly one-half of

M. Diesendorf / Environmental Science & Policy 1 (1998) 1±12

global annual GDP. Speci®cally, for coal-®red electricity, the study ®nds that the `external' costs are dominated by the greenhouse e€ect and amount to about US$0.40/kWh. If this were to be included in the price of coal-®red electricity, a combination of windpower (which is already cost-e€ective in parts of the USA, northern Europe and Tasmania), solar thermal electricity and even photovoltaics could economically replace coal-®red power stations in Australia and many other countries as they reach the end of their operating lives (Diesendorf, 1994). 3.2. Neglect of cost-e€ective energy eciency With the exception of IMP, the Australian `top± down' models assume incorrectly that all cost-e€ective products and services for reducing greenhouse gas emissions are already being used. This is a consequence of the neoclassical economics assumption that the market for energy services is `competitive' and so the price is determined by a balance or `equilibrium' between supply and demand. Some neoclassical energy economists argue that, if these measures were really cost-e€ective, they would be implemented automatically by the market. The engineers who use `bottom± up' models are overlooking hidden transaction costs, they say. In response, engineers, scientists and other economists draw attention to a large body of empirical evidence from several countries that the market of energy ecient goods and services su€ers from severe market failure. There is no competitive market in this area, they say, and so there are many unimplemented coste€ective energy eciency measures available (Hirst and Brown, 1990; Grubb, 1990; Hinchy et al., 1991; Reddy, 1991; Lovins and Lovins, 1991; ESD Working Groups, 1991, ch. 7; ESD Working Group Chairs, 1992, ch. 5; Auditor-General, 1992; Blok et al., 1993; Geller and Nadel, 1994; Koomey and Sanstad, 1994; Sanstad and Howarth, 1994; Diesendorf, 1996). Some of the alleged high transaction costs could be removed at negligible cost by means of administrative decisions or legislation, and so it is not fruitful to treat them as large hidden costs. Disregarding `no regrets' measures entails that the costs of greenhouse abatement are arti®cially boosted, because models install dearer energy supply technologies instead of low-, zero-, or negative-cost demandreducing technologies. In addition, the employment creation bene®t of investment or expenditure of savings from ecient energy use technology is ignored. In this regard a few economists have claimed that, even if unimplemented cost-e€ective technologies for ecient energy use existed, any money saved would be spent in such a way that it cancelled the original energy savings (Brookes, 1990). This is sometimes

5

called the `rebound e€ect'. However, that the rebound must be very small is entailed by the following observations: . For many energy services, demand may be approaching saturation. . Expenditure on energy is only 5% of GDP (ESD Working Groups, 1991, p. 3). Actually, the important thing is how much of an extra dollar received is spent on energy, and this is likely to be less than 5%, because of the ongoing decline in energy intensity of the economy (see Section 3.5). Furthermore, by de®nition there could be no `rebound' if each energy consumer implemented a package of energy eciency measures which had zero net costs. Such packages could be o€ered to consumers by utilities operating as energy service companies. Consultants to the ESD Energy Use Working Group undertook a comprehensive analysis of the costs of energy savings that could be made in many nontransport energy use areas, comprising about half of Australia's energy use. With `moderate' intervention by government to remove barriers which are nontechnical and noneconomic to reduce CO2 emissions by 55 Mt, their ®ndings sum to total potential economic savings over a 15 year period of approximately A$7.2 billion (net present value assuming 8% real discount rate). A `high' level of intervention would entail a more rapid phaseout of existing stocks of energy-inecient products, and hence a higher level of costs. Nevertheless, it was found that savings in this case would still result in net economic bene®ts of approximately A$3.3 billion over the same period (Kinrade, 1992b; Diesendorf and Kinrade, 1992). Approximately, A$1 = US$0.75. These potential economic savings, less a very small `rebound', are excluded automatically from the general equilibrium models, Orani, G-Cubed and MEGABARE. IMP does not exclude the savings, but diminishes them greatly by assuming that no policy measures would be implemented to make ®nance available for ecient energy use at the same low interest rate as for energy supply technologies. 3.3. Assumption of `constant returns to scale' This is the assumption that, if all the inputs to the manufacture of particular goods are increased by a certain factor, then the outputs are increased by the same factor. Although this is a convenient mathematical assumption for economic modellers, it severely disadvantages new technologies, notably renewable energy sources. The dramatic reductions in the costs of wind and solar power through the 1980s were the result of a combination of technology improvement, of increasing scale of production and of technology scale. Speci®cally, the economic optimal capacity of a wind

6

M. Diesendorf / Environmental Science & Policy 1 (1998) 1±12

generator increased from 55 kW in the early 1980s to around 600 kW in the mid 1990s, while the cost per kW declined by a factor of 3±4 in real terms (Blakers and Diesendorf, 1996), thus violating the assumption of constant returns to scale. 3.4. Choice of baseline scenario In economic models of greenhouse abatement, both `top±down' and `bottom±up', the cost is taken to be the di€erence between the value of some indicator such as GDP in a baseline or reference scenario and that in a target scenario which has lower emissions. Sensitivity analyses performed overseas show that the results depend sensitively on the choice of baseline scenario. Between di€erent models this choice may vary widely (Grubb et al., 1993). If it is assumed that the BAU scenario involves rapid growth in emissions, then the task of (say) stabilising emissions at 1990 level by year 2000 becomes more expensive than if the BAU scenario involves lower growth. Clearly, a sensitivity analysis of the choice of baseline scenario is required, but this is rarely performed in practice in Australia. Instead, many `top±down' modellers are tied to the positivist, scientistic assumption that their choice of BAU scenario is the economic optimum. 3.5. Assumption that future energy intensity declines at a low, constant rate Although `top±down' models do not deal with technologies explicitly, they attempt to take account of future improvements in the eciencies of energy generation and energy use by assuming that, in the absence of price changes, future energy intensity declines at a constant rate, usually chosen to be in the range 0.5± 1.0% p.a. This rate is known as the `autonomous enduse energy-intensity improvement' (AEEI) and its value determines to a large degree the baseline energy demand projection. But, in reality, changes in energy intensity re¯ect a combination of factors: improvements in energy eciency technologies resulting in lower energy bills, the e€ects of changes in government policies on the uptake of these technologies, shifts from manufacturing to service industries, and the saturation of demand for energy-intensive products. The aggregation of all these e€ects into a single constant parameter is too crude both for understanding past changes in energy intensity and for policy making to in¯uence future changes. Governments can and do introduce new regulations, funding for the development and commercialisation of new technologies, institutional changes, national targets, new tax rates and information services. Therefore the choice of a single unchanging (and unchangeable) parameter is an example of `parametris-

ing our ignorance' done badly. It would be better to use a `bottom±up' model, containing speci®c projections for future cost reductions by speci®c technologies, and marry it to a `top±down' model which permits separate investigation of the e€ects of policy, structural and social changes. 3.6. Use of elasticities Similar objections apply to elasticities, the principal parameters which are used to substitute for information on energy supply and energy use technologies and their adoption in the market-place (see Section 2.2). For each fuel (or electricity), the price elasticity of energy demand is de®ned to be the change in energy demand resulting from a change in its price. `Top± down' models also use the price elasticity of substitution between energy and other factors of production, such as labour and capital. Taking into account the large changes in energy demand and in energy supply technologies which will be needed to meet greenhouse targets, there is little Australian data which can be used to derive these elasticities. Some models attempt to derive elasticities from the large oil-price shocks caused by OPEC in the 1970s, but this is a poor model for the impact of a carbon tax imposed in small annual increases from an initially low level (Ekins, 1994). In attempting to derive the price elasticity of energy demand, `top±down' modellers also generally assume unrealistically that it is independent of time, is symmetric with respect to price increases and falls, and that it is not a€ected by the nonprice changes which may be taking place at the same time. In practice, these assumptions are inconsistent with data from preand post-OPEC price rises which show that elasticities are not constants but change with time; with engineering evidence that eciency gains are not all lost when energy prices fall; and with known regulatory and institutional changes made by governments of several OECD countries to encourage ecient energy use and the development of renewable energy sources during the years following the OPEC increases in oil prices in the 1970s (Grubb et al., 1993). Hence, the uses and values of elasticities used in economic models of greenhouse abatement are questionable to say the least. The results of the models depend sensitively upon these choices of the values of elasticities. The wide variety of choices in di€erent `top±down' models suggests that at least some economic modellers may be aware of the limitations of elasticities. 3.7. Inappropriate indicators of welfare `Top±down' models assess the impact of greenhouse abatement primarily by its e€ect on aggregate indi-

M. Diesendorf / Environmental Science & Policy 1 (1998) 1±12

cators such as GDP, GNP or GNE. The MEGABARE model explicitly confuses GNE with welfare (DFAT and ABARE, 1995). But these are poor indicators of welfare (Daly and Cobb, 1989; Dodds, 1997), since some reductions in their values are bene®cial, e.g. in the greenhouse context, when they involve less expenditure on sea-walls, pesticides and medical and hospital bills from asthma, bronchitis and motor accidents. Hence, it is inappropriate for GDP and similar traditional macro-economic indicators, which have not been adjusted for the `external' environmental and health costs, to be used as measures of welfare. Furthermore, these indicators do not re¯ect the distribution of impacts by social class or income level. It is likely that the greatest human su€ering resulting from global climate change will be felt in developing countries, which will be least able to prevent and cope with increases in tropical diseases, decreased food production, and deaths connected with migration caused by additional ¯oods or droughts (Soerensen, 1997, Table 9.3). To be credible with policy makers and environmental managers, economic models should dispense with such crude indicators, and instead distinguish speci®c types of social (including distributional e€ects), economic (including employment) and environmental impacts. 3.8. Restricted range of instruments of greenhouse abatement Because of their emphasis on `competitive' markets and prices, `top±down' models focus on a carbon tax and, sometimes, tradeable emission quotas, ignoring the important roles of regulations, institutional change, education, information and training (Diesendorf, 1997). 3.9. Sensitivity to how a carbon levy is spent Many `top±down' modellers ignore the fact that the economic and environmental impacts of a carbon levy depends on how it is spent (Ekins, 1994; Hamilton et al., 1997). Di€erent impacts on GDP, employment and greenhouse gas emissions will be obtained in general if the tax is spent on: . reducing income tax and payroll tax, . stimulating investment, especially in ecient energy use and renewable energy, . reducing budget de®cits or . some combination of the above. The ocial report on MEGABARE (DFAT and ABARE, 1995) contains no sensitivity analysis in this regard.

7

3.10. Inappropriate terminology linked with opaque assumptions Using GDP, GNP or GNE as measures of welfare (Section 3.7) provides one example. Another, related example is the MEGABARE report's use of the term `equity' for relative impact of abatement on GNE in various countries (DFAT and ABARE, 1995). MEGABARE obtains the result that Australia would have a greater `burden' (i.e. reduction in GNE growth) in meeting an international greenhouse target, and then describes this misleadingly as `inequity'. This use of language is simplistic, ignoring the fact that Australia is less ecient (in a technical sense as well as in terms of energy intensity) in using energy than most other OECD countries (Kinrade, 1992c), and hence has much greater potential for low or negative cost greenhouse abatement. Thus, MEGABARE's peculiar use of `equity' follows from its assumption, held in common with almost all top± down models, that there are no `no regrets' measures (see Section 3.2). 3.11. Presenting input assumptions as outputs of the modelling Without justi®cation, the MEGABARE report makes the implicit political assumption that action by a few selected rich countries to reduce emissions will not in¯uence the large emitters among the developing countries, notably China and India, to reduce theirs. This assumption is made despite the fact that, at international greenhouse fora, both China and India have stated that the rich countries are responsible for most of the emissions to date and so the rich countries must ®rst demonstrate their credibility by reducing emissions before China and India will join in. With this implicit assumption and by focussing on a selected group of rich countries whose CO2 emissions amount to less than half global emissions, MEGABARE then `®nds' that setting a stabilisation target in these countries alone `is ine€ective' in stabilising global emissions. The result follows immediately from the assumptions Ð it is unnecessary to perform the computer analysis. But MEGABARE presents it as if it were the outcome of analysis by the model. 4. Conclusion: what is a good model? All the seriously ¯awed assumptions discussed in Section 3 are made by MEGABARE; most are made by the other general equilibrium models, Orani/ Monash and G-Cubed; and a few are made by IMP. Yet, even within the limited framework of general equilibrium models, several of these shortcomings

8

M. Diesendorf / Environmental Science & Policy 1 (1998) 1±12

could be readily recti®ed, namely the assumption of constant returns to scale (Section 3.3), the assumption that GDP = welfare (Section 3.7), the sensitivity to di€erent ways of spending a carbon levy (Section 3.9), inappropriate terminology linked to opaque assumptions (Section 3.10), and presentation of inputs as if they were outputs of the model (Section 3.11). In addition, at least a sensitivity analysis could be performed for the choice of baseline scenario (Section 3.4), although this in itself would not provide a better empirical basis for choosing a particular baseline scenario. It should also be easy for modellers to make their economic and political assumptions more transparent. However, other required improvements are much more dicult within the general equilibrium structure: namely, doing away with poorly based parameters, such as AEEI (Section 3.5) and elasticities (Section 3.6), incorporating `no regrets' options under conditions of market failure (Section 3.2) and allowing nonprice policy options to be handled (Section 3.8). Integrating economic and environmental models and taking account of the economic and noneconomic bene®ts of reducing greenhouse gas emissions (Section 3.1) may be beyond the framework of neoclassical economics, perhaps requiring the broader approaches of the new ®eld of ecological economics. Based on the recommendations of Meadows and Robinson (1985) and the shortcomings of existing computer models of greenhouse abatement identi®ed here, the following seven general properties of `good' computer models are identi®ed. In relation to each property, speci®c comments are o€ered on the four main Australian `top±down' models discussed in this paper. (i) Ability to compare key assumptions and results with observation. Where assumptions are contradicted by observations, the assumptions should be changed: All of the four main `top±down' models used in Australia appear to have shortcomings in this regard. For example, modellers need to take account of market failure and `no regrets' options, instead of assuming, in the face of all the evidence to the contrary, that they do not exist. In this regard IMP appears to be structured in a more ¯exible and realistic manner than the three general equilibrium models. But, in practice, it has been run with particular choices of assumptions which make greenhouse abatement appear costly (see Section 3.2). In general, economic models should recognise the reality of market failure and make much wider provision for the inclusion of nonprice policy instruments, such as regulation, institutional change and information. (ii) Where empirical tests are impossible, sensitivity analysis is needed to test e€ects of assumptions: For none of the four main `top±down' models has there been published extensive sensitivity analyses of their

greenhouse abatement results to their respective choices of baseline scenarios or to the way a carbon levy is spent. Under pressure from the ESD Working Groups, the `bottom±up' model MENSA was run to test the e€ect of di€erent baselines (Jones et al., 1991), but this was not mentioned in subsequent publications (e.g. Jones et al., 1994). (iii) Where empirical tests are impossible and the results are highly sensitive to assumptions about particular parameters, it may be better to change the model: The huge variations in choices of elasticities and AEEI in various models con®rms what is apparent on theoretical grounds: namely that these parameters cannot be determined empirically. Yet the results of `top±down' modelling are highly sensitive to these choices. Therefore the use of AEEI and elasticities for parametrising economists' ignorance of technologies is invalid. It would be better to introduce real data on speci®c technologies through a `bottom±up' model, link it to a `top±down' model to calculate the impact of changes in the energy sector on the rest of the economy, dispense with elasticities and introduce in their place a range of exogenously determined policy options for facilitating technology uptake or substitution. This is already done by IMP, but its assumptions on technology uptake need to be made more ¯exible and documentation should be improved. Although MENSA has been linked with ORANI, elasticities have been retained, so this is not a satisfactory combination. (iv) Detailed documentation of the model's objectives, assumptions, methods (including ¯ow diagrams and equations), terminology and limitations. The model should be constructed and documented to facilitate independent validation of the model and its component parts: Where the models make the assumptions discussed in Section 3), they fail to justify them. In terms of listing economic and mathematical assumptions not discussed in Section 3), G-Cubed and ORANI appear to be better documented than MEGABARE and IMP. But, all the models are documented for economic modellers rather than for policy makers who are not economists. (v) Balanced, open presentation of the results to policy makers in context: By failing to acknowledge many of the questionable assumptions discussed in Section 3), the four models' reports do not report fairly on the results of studies of the costs and bene®ts of greenhouse response strategies. (vi) Automatic checking of the ranges of validity of assumptions and special techniques (such as statistical analysis), with warning messages included in the output when the limits have validity have been approached: This is generally rare in economic models. (vii) Adequate, justi®able input data: MEGABARE, ORANI and G-Cubed use elasticities and MEGABARE and ORANI use AEEI. The empirical basis for using these parameters is dubious. Like other

M. Diesendorf / Environmental Science & Policy 1 (1998) 1±12

`top±down' models, the three Australian models ``describe, by design, a behaviourally, institutionally and (to a large degree) technologically ®xed world'' (Wilson and Swisher, 1993) (my insertion in brackets). Finally, it must be recognised that economic models of greenhouse abatement only o€er one window on a very complicated, multi-faceted problem. In building policy, there are also roles for physical input±output models (Hamilton, 1995), models which attempt to integrate economics and environment, back-of-the-

9

envelope calculations, political science analyses, common-sense and community consultation processes. Acknowledgements I thank Clive Hamilton, Joel N. Swisher and an anonymous referee for valuable comments. However, responsibility for views expressed and any errors is the author's.

Appendix A. Glossary for noneconomists4

AEEI bottom±up model (of greenhouse response) business-as-usual (BAU) scenario competitive market computer model discount rate

dynamic economic growth ecient energy use elasticity energy intensity (of an economy) energy service energy service company

4

`Autonomous end-use energy-intensity improvement' is a parameter which de®nes the rate at which energy intensity (q.v.) would change in the absence of price changes. A computer model of all or part of the energy sector which is based directly on data about the costs and performances of speci®c energy technologies and services. In the context of energy planning, a projected trend in energy use which places no environmental constraints on future economic activity or technology choice. A market in which none of the buyers or sellers can in¯uence prices. A market that is not competitive is said to su€er `market failure' (q.v.). A model which, from a set of initial assumptions, provides a logical description of how a system performs, and hence can be represented on a computer. An interest rate which is used to discount (i.e. reduce the value of) income or expenditure in the future (see `net present value') due in part to a preference for consumption now rather than later. It is often expressed in `real' terms, i.e. adjusted to exclude the e€ects of in¯ation. Fully time-dependent Expansion of economic activity expressed in monetary terms, usually measured by an increase in GDP or GNP. Using less energy to provide the same energy services, e.g. by insulating one's home, or using ¯uorescent lights instead of incandescent, or replacing a fuel wasting car with a fuel ecient car. A measure of the responsiveness of one variable when another variable changes (see `price elasticity of demand'.) Annual, national energy use divided by GDP. Task or service which involves energy as an input, e.g. home heating, oce lighting, transportation and cold food. The focus is on the service rather than the quantity and type of energy supplied. A business selling energy services rather than just particular forms of energy.

See also the more extensive glossary in Diesendorf and Hamilton (1997).

10

equilibrium

ESD exogenous externality (or external cost)

GDP

GNE GNP

kW kWh market

market failure

model Mt neoclassical economics net present value no regrets options

M. Diesendorf / Environmental Science & Policy 1 (1998) 1±12

In economics, this is the state of a competitive market, or a whole economy of competitive markets, in which supply = demand, and is characterised by a particular set of prices for the goods and/or services in the market. General equilibrium refers to a situation where all markets in an economy are simultaneously in equilibrium, while partial equilibrium refers to a single sector, in this case the energy sector. Ecologically sustainable development External (to the model) Something which a€ects a buyer or seller's utility or pro®t which is not included in the prices of goods and services exchanged in the market of interest: e.g. the environmental and health costs of pollution. The total monetary value of `®nal' goods and services produced in a country per year. `Final' excludes `intermediate' goods and services which are used as inputs into the production of other goods and services. Although used by neoclassical economists as a measure of national economic welfare, GDP does not include the value of unpaid work and changes in values of built or natural assets. Total expenditure by residents of a country on ®nal goods and services; i.e. GNE = GDP + value of imports ÿ value of exports The total monetary value of wages, rent, interest and pro®ts (including depreciation and indirect taxes) of a nation's residents, regardless of where they are employed. Unlike GDP it includes returns on overseas investments and excludes repatriated dividends and interest. Like GDP it excludes the value of unpaid work and changes in values of built or natural assets. Kilowatt, a unit of electrical power (rate of ¯ow of electrical energy), which is roughly equal to the instantaneous output of a single-bar electric radiator. Kilowatt hour, a unit of electrical energy, which corresponds roughly to the total energy output of a single-bar electric radiator over one hour. A process of competing bids and o€ers for a particular set of goods and/or services, in which buyers and sellers may be individuals or ®rms. Another de®nition is: an institution which organises the exchange of control of commodities, where the nature of the control is de®ned by the property rights attached to the commodity. A concept of neoclassical economics describing a situation in which the conditions of perfect competition do not apply in a market. Market failure can arise because: some buyers and/or sellers in¯uence prices; or there are externalities (q.v.); or the good or service in question has characteristics of a public good; or buyers have insucient information about prices, the attributes or availability of the relevant good; or there are institutional barriers to unfettered market operation. Most economists try to classify the latter as externalities or as `transaction costs', or simply ignore them. A simpli®ed representation of reality. It may be expressed in terms of mathematical equations or formal logic or informally. Megatonnes (million tonnes) The dominant economic paradigm that emphasises individual consumer choices in markets and `rational' pursuit of self-interest. The value now of a stream of net income over future years, arrived at by discounting (q.v.) future income. Greenhouse abatement options which have nongreenhouse economic bene®ts greater than or equal to their economic costs.

M. Diesendorf / Environmental Science & Policy 1 (1998) 1±12

OECD

OPEC parameter (math.) price elasticity of demand (for energy)

thermal (energy) eciency top±down model (of greenhouse response)

variable

11

Organisation for Economic Cooperation and Development, the group of mainly rich countries which comprises Australia, Canada, Iceland, Japan, Mexico, New Zealand, Turkey, USA and almost all the countries of western Europe. Organisation of Petroleum Exporting Countries, which includes Saudi Arabia, Kuwait, Iraq, Iran and Venezuela. Something which acts as a substitute for something else. A parameter describing the percentage change in the quantity of energy demand divided by the percentage change in energy price. For example, if a 20% increase in petrol price leads to a 10% decrease in the quantity demanded, then the price elasticity of demand is ÿ10/ 20 = ÿ 0.5. Useful energy output divided by energy input, usually expressed as a percentage. (Applied to energy conversion processes that involve the burning of fuels.) A computer-based economic model which examines the aggregated behaviour of the whole economy under greenhouse response. Such models generally represent existing technologies and technological change crudely. A quantity whose value changes with time or with changes in another variable upon which it depends.

References Auditor-General, 1992. Eciency audit: energy management of Commonwealth buildings. Audit Report No. 47. AGPS, Canberra. Australian Conservation Foundation and Australian Council of Trade Unions, 1994. Green Jobs in Industry: Research Report. ACF/ ACTU, Melbourne. Blakers, A., Diesendorf, M., 1996. A scenario for the expansion of solar and wind generated electricity in Australia. Australian Journal of Environmental Management 3, 11±25. Blok, K., Worrell, E., Cuelenaere, R., Turkenburg, W., 1993. The coste€ectiveness of CO2 emission reduction achieved by energy conservation. Energy Policy 21, 656±667. Brooker, R., O'Meagher, B., 1991. Economic modelling, ecologically sustainable development and the greenhouse e€ect. In: Ecologically Sustainable Development Working Groups, Economic Modelling. ESD Modelling Steering Group, Canberra. Brookes, L.G., 1990. The greenhouse e€ect: the fallacies in the energy eciency solution. Energy Policy 18, 199±201. Burmot Australia P/L, Centre for Applied Economic Research and Energetics P/L, 1991. Energy prospects: realistic prospects for improved energy eciency and the application of renewable energy sources, vol. 1. Business Council of Australia, Melbourne. Daly, H.E., Cobb, J.B., 1989. For the common good: redirecting the economy towards community, the environment and a sustainable future. Beacon Press, Boston. Department of Foreign A€airs and Trade and ABARE, 1995. Global climate change: economic dimensions of a cooperative international policy response beyond 2000. Commonwealth of Australia, Canberra. Diesendorf, M., 1994. Wind, biomass and other renewables. In: Dovers, S. (Ed.), Sustainable energy systems (pp 150±169). Cambridge University Press, Cambridge. Diesendorf, M., 1996. How can a `competitive' market for electricity be made compatible with the reduction of greenhouse gas emissions?. Ecological Economics 17, 33±48. Diesendorf, M., 1997. Greenhouse response in the energy sector. In: Diesendorf, M., Hamilton, C. (Eds.), Human Ecology, Human Economy: Ideas for an Ecologically Sustainable Future, ch. 8. Allen and Unwin, Sydney.

Diesendorf, M., Kinrade, P., 1992. Integrated greenhouse policies for energy and transport post-Rio. Institution of Engineers Australia seminar on Australia's greenhouse policy, Canberra, 26 June 1992. Australian Conservation Foundation, Melbourne. Diesendorf, M., Hamilton, C. (Eds.), 1997. Human Ecology, Human Economy: Ideas for an Ecologically Sustainable Future. Allen and Unwin, Sydney. Dodds, S., 1997. Economic growth and human well-being. In: Diesendorf, M., Hamilton, C. (Eds.), Human Ecology, Human Economy: Ideas for an Ecologically Sustainable Future, ch. 4. Allen and Unwin, Sydney. Ecologically Sustainable Development Working Group Chairs, 1992. Greenhouse Report. AGPS, Canberra. Ecologically Sustainable Development Working Groups, 1991. Final Report Ð Energy Use. AGPS, Canberra. Ekins, P., 1994. The impact of carbon taxation on the UK economy. Energy Policy 22, 571±579. European Commission (1996). ExternE: externalities of energy, vols. 1±6. EC DG12, Science Research and Development. Oce for the Ocial Publ. of the Commission of European Communities, Luxembourg. Geller, H., Nadel, S., 1994. Market transformation strategies to promote end-use eciency. Annual Review of Energy and the Environment 19, 301±346. Grubb, M.J., 1990. Energy eciency and economic fallacies. Energy Policy 18, 783±785. Grubb, M., Edmonds, J., ten Brink, P., Morrison, P., 1993. The costs of limiting fossil-fuel CO2 emissions. Annual Review of Energy and Environment 18, 397±478. Hamilton, C., 1995. Input±output models for analysis of greenhouse policy. In: Anon. (Ed.), The Role of Economic Analysis in Assessing Greenhouse Impacts and Responses. Department of Environment, Sport and Territories, Canberra, pp. 18±24. Hamilton, C., Common, M., 1994. Environment-economic modelling: current state of play and future directions. Consultancy to Department of the Environment, Sport and Territories. DEST, Canberra. Hamilton, C., Hundloe, T., Quiggin, J., 1997. Ecological Tax Reform in Australia. Australia Institute, Canberra. Hinchy, M.D., Naughten, B.R., Donaldson, P.K., 1991. The issue of

12

M. Diesendorf / Environmental Science & Policy 1 (1998) 1±12

domestic energy market failure. Technical Paper 91.5. Australian Bureau of Agricultural and Resource Economics, Canberra. Hirst, E., Brown, M., 1990. Closing the eciency gap: barriers to the ecient use of energy. Resources, Conservation and Recycling 3, 267±281. Industry Commission, 1991. Costs and Bene®ts of Reducing Greenhouse Gas Emissions, vols. I and II. AGPS, Canberra. James, D., 1996. Comparison of economic-environment models. Report to Department of the Environment, Sport and Territories, Canberra. Jones, B., Naughten, B., Zhao-Yang, P., Watts, S., 1991. Costs of reducing carbon dioxide emissions from the Australian energy sector. In: Ecologically Sustainable Development Working Groups, Economic Modelling. ESD Modelling Steering Group, Canberra. Jones, B.P., Peng, Z.-Y., Naughten, B., 1994. Reducing Australian energy sector greenhouse gas emissions. Energy Policy 22, 270±286. Kinrade, P., 1992a. Critique of an assessment of the achievability of an Australian commitment to stabilize energy related CO2 emissions undertaken for the Business Council of Australia by ACIL Australia Pty Ltd. ACF/WWF Ecologically Sustainable Development Policy Unit, Melbourne. Kinrade, P., 1992b. Implications for the Australian Economy of Meeting Carbon Dioxide Emissions Targets: A Review of Recent Carbon Tax Studies. ACF/WWF Ecologically Sustainable Development Policy Unit, Melbourne. Kinrade, P., 1992c. Energy Eciency of the Australian Economy: A Sectoral Analysis. ACF/WWF Ecologically Sustainable Development Policy Unit, Melbourne. Koomey, J.G., Sanstad, A.H., 1994. Technical evidence for assessing the performance of markets a€ecting energy eciency. Energy Policy 22, 826±832. London Economics, 1992. The impact of global warming control policies on Australian industry, vols. I and II. London Economics, London. Lovins, A.B., Lovins, L.H., 1991. Least-cost climatic stabilisation. Annual Review of Energy and the Environment 16, 433±531. McKibbin, W., Pearce, D., Stoeckel, A., 1994. Economic e€ects of reducing carbon dioxide emissions. Prepared on behalf of Centre for International Economics for Australian Mining Industry Council. McKibbin, W. and Wilcoxen, P., 1995. Environmental Policy and International Trade. Australian National University, Canberra and Brookings Institution, Washington DC, preprint. Marks, R., Swan, P., McLennan, P., Schodde, R., Dixon, P., Johnson, D., 1989. The feasibility and implications for Australia of the adoption of the Toronto proposal for carbon dioxide emissions. Report to CRA Ltd., Melbourne. Meadows, D.H. and Robinson, J.M. (1985). The electronic oracle: computer models and social decisions. Wiley, Chichester. Moeller, T., 1985. Windpower support is good business for the state. Windpower Monthly 1 (10), 91±94. Moran, A., Chisolm, A., 1991. Greenhouse gas abatement: its costs and practicalities. Tasman Institute, Melbourne. Naughten, B., Pakravan, P., Dlugosz, J., Dickson, A., 1994. Reductions in greenhouse gas emissions from the Australian energy

system: a report on modelling experiments using ABARE's MENSA model. ABARE, Canberra. NIEIR, 1990. Ecient renewable energy scenario study for Victoria. Report to Victorian Solar Energy Council, 3 vols. NIEIR, Melbourne. NIEIR, 1991. Ecient energy scenarios to 2020: the economic impact of greenhouse gas reductions. Australian Commission for the Future, Melbourne. NIEIR, 1995. Measuring the economic impact of reducing greenhouse gas emissions. Report to Electricity Supply Association of Australia, 2 vols. ESAA, Sydney. Nordhaus, W.D., 1990. Economic approaches to greenhouse warming. Yale University, New Haven. Nordhaus, W.D., 1991. To slow or not to slow: the economics of the greenhouse e€ect. The Economic Journal 101, 920±937. Reddy, A.K.N., 1991. Barriers to improvements in energy eciency. Energy Policy 19, 953±961. Sanstad, A.H., Howarth, R.B., 1994. `Normal' markets, market imperfections and energy eciency. Energy Policy 22, 811±818. Soerensen, B., 1995. Aggregation and life-cycle extension of externality studies. Final report of the Roskilde University contribution to the European Commission JOULE project `ExternE': externalities of fuel cycles, JOU-CT92-0264. Roskilde University, Denmark. Soerensen, B., 1997. Impacts of energy use. In: Diesendorf, M., Hamilton, C. (Eds.), Human Ecology, Human Economy: Ideas for an Ecologically Sustainable Future. Allen and Unwin, Sydney. Sustainable Solutions P/L and Forster Electronic Services P/L, 1994. Energy use and greenhouse gas emissions scenarios: three sectoral studies. Consultancy to Department of the Environment, Sport and Territories. AGPS, Canberra. Sutherland, R., 1991. Market barriers to energy-eciency investments. The Energy Journal 12 (3), 15±34. Wilson, D., Swisher, J., 1993. Exploring the gap: top±down versus bottom±up analyses of the cost of mitigating global warming. Energy Policy 21, 249±263.

Mark Diesendorf is Professor of Environmental Science and Director of the Institute for Sustainable Futures at the University of Technology, Sydney. He has a BSc (Hons) in Physics from the University of Sydney and a Ph.D. in Applied Mathematics from the University of New South Wales. His current research interests are energy, transport and greenhouse response, and processes for achieving ecological, economic and social sustainability. He is a Board Member of the Sustainable Energy industries Council of Australia Inc., a member of the Australian Cooperative Research Centre on Renewable Energy, a former President of the Australasian Wind Energy Association and has served on the Committee of the Australian and New Zealand Solar Energy Society. He is co-editor and principal author of the transdisciplinary book, `Human Ecology, Human Economy: Ideas for an Ecologically Sustainable Future', Sydney: Allen & Unwin, 1997. In all he has authored or co-authored three books and monographs, has edited ®ve books, and has published over 70 research papers and book chapters, 23 conference papers and over 50 popular articles.