This section consolidates the explanations for the different findings in both the macro studies reviewed in Chapter 8 and the sectoral studies in this chapter. It extends and complements the methodological discussion in the SAR (Hourcade et al., 1996, pp. 282-92), particularly in the role of assumptions leading to differing results.

In assessing the economy-wide effects of mitigation, considerable use has been made of top-down models (macroeconomic, general equilibrium, and energy-engineering), while specific sectoral studies use both top-down and engineering-economic bottom-up models. Critical differences in the results come from the type of model used, and its basic assumptions. Repetto and Austin (1997), in a meta-analysis of model results on the costs of mitigation for the USA, show that 80% of predicted impacts come from choice of assumptions. They find that four assumptions are critical in leading to lower costs of mitigation. These are that:

• the economy responds efficiently to policy changes at least in the long run;
• international joint implementation is achieved;
• revenues from taxes or permit sales are returned to the economy through reducing burdensome taxes; and
• any co-benefits from reduced air pollution are fully included.

They conclude that under reasonable assumptions, the predicted economic impacts from the models for the USA in stabilizing CO₂ emissions at 1990 levels through to 2020 would be neutral or even favourable.

Most early studies are focused on the costs, rather than on the benefits of mitigation¹¹. More recently, top-down modellers have studied the impact of using the revenues collected from carbon taxes (or from auctions of carbon permits) to correct economic distortions in some sectors of the economy (typically to reduce taxes on labour, taxes on incomes and profits, or taxes on investment).

9.4.1.1 Top-down and Bottom-up Modelling

The adoption of top-down or bottom-up methods makes a significant difference to the results of mitigation studies (see 8.2.1 and 8.2.2 for discussion and results). In top-down studies the behaviours of the economy, the energy system, and their constituent sectors are analyzed using aggregate data. In bottom-up studies, specific actions and technologies are modelled at the level of the energy-using, GHG-emitting equipment, such as power-generating stations or vehicle engines, and policy outcomes are added up to find overall results. The top-down approach leads easily to a consideration of the effects of mitigation on different broad sectors of the economy (not just the energy and capital goods sectors), so that the literature on these effects tends to be dominated by this approach.

Table 9.10 compares the methodologies. They have a fundamentally different treatment of capital equipment and markets. Top-down studies have tended to suggest that mitigation policies have economic costs because markets are assumed to operate efficiently and any policy that impairs this efficiency will be costly. Bottom-up studies tend to suggest that mitigation can yield financial and economic benefits, depending on the adoption of best-available technologies and the development of new technologies. Some hybrid models include both approaches (see Laroui and van Leeuwen, 1995, for an example).

Table 9.10: A comparison of top-down and bottom-up modelling methodologies
Treatment	Top-down	Bottom-up
Concepts and terms	Economics-based	Engineering-based
Treatment of capital	Homogeneous and abstract concept	Precise description of capital equipment
Treatment of technical change	Trends rates (usually exogenous)	Menu of technical options
Motive force in the models	Responses of economic groups via income and price elasticities	Responses of agents via discount rates
Perception of the market in the model	Perfect markets are usually assumed	Market imperfections and barriers
Potential efficiency improvements	Usually low with assumption of all negative cost opportunities utilized	Opportunities for no regrets actions identified
Source: Bryden et al. (1995)

9.4.1.2 General Equilibrium and Time-series Econometric Modelling

There are two main types of macroeconomic models used for medium- and long-term economic projections¹²: resource allocation models (i.e. CGE) and time-series econometric models. Their main differences being the assumptions made about the real measured economy, aggregation, dynamics, equilibrium, empirical basis, and time horizons, among others.

The main characteristic of CGE models is that they have an explicit specification of the behaviour of all relevant economic agents in the economy. In the mitigation applications they have usually adopted assumptions of optimizing rationality, free market pricing, constant returns to scale, many firms and suppliers of factors, and perfect competition in order to provide a market-clearing equilibrium in all markets. Econometric models have relied more on time-series data methods to estimate their parameters rather than consensus estimates drawn from the literature. Results from these models are explained not only by their assumptions but also by the quality and coverage of their data. It is usually argued that CGE models are more suitable for describing long-run steady-state behaviour, while econometric models are more suitable for forecasting the short-run. However, the models have increasingly incorporated long-run theory and formal econometric methods, and several now include a mix of characteristics, from both resource allocation and econometric models; see Jorgenson and Wilcoxen (1993), McKibbin and Wilcoxen (1993, 1995), Barker and Gardiner (1996), Barker (1998b) and McKibbin et al. (1999).

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