## Baseline

The costs of reducing emissions to a desired level depend heavily on how much would have grown otherwise: the more quickly emissions grow in the absence of a policy, the larger will be the reductions needed to reduce them to a desired target (Owen and Hanley, 2004). As our selected studies were conducted at different points in time, they differ in time horizon, geographical coverage, projections about the baseline of the economic system and, most importantly, baseline emissions. Different reference scenarios lead to the broad range of actual reduction required and, therefore, to different magnitude of total abatement costs shown in table 5.

## Modelling features

Because of the different structural modelling assumptions some of the differences of the ex-ante cost outcomes might be explained.

The selected Partial Equilibrium (PE) models predict 2.6 times lower average total emission abatement costs than the CGE models. This pattern is not so clear for the marginal abatement cost estimates: the selected PE models predict negligibly lower marginal abatement costs $ (45\ \text{€}_{2005} $ on average) than the CGE models $ (48\ \text{€}_{2005} $ on average). Babiker et al. (2004) and Russ et al. (2005) use PE model and CGE model respectively and derive equally high marginal abatement costs suggesting that a common principle, that CGE models tend to deliver higher costs estimates than PE models, does not always hold and, hence, model type alone cannot explain all variation in the cost estimates. The highest MAC of $ 159\ €_{2005} $ is provided by Babiker et al. (2004) who employ a CGE model and assume the existence of distortionary energy taxes when the EU-wide trading is introduced. While Russ et al. (2005) use a PE model and assume that the EU is committed alone to reduce its emissions by 2025 to 20 percent below 1990 levels.

Modelling of technological change might be treated as one of the most important assumptions responsible for different ex-ante cost estimates across different modelling studies, especially between top-down and bottom-up models. Despite an understanding, that technological change is not an exogenous phenomenon that can be simply defined outside the model, but to an important degree endogenous, is induced by needs and pressures of the current economic situation, most of the selected models still rely on the exogenous technological change representation (see Annex for the summary of technical change representation in the selected studies). EPPA model used by Babiker et al. (2004), for instance, applies CGE modelling approach and specifies future carbon free technologies (back-stop energy sources) and assumes autonomous energy efficiency improvement. PE models represent technological side of the energy sector in more detail and allows for technological learning effects and other market and non-market related factors to influence the penetration of new technologies. For instance, in PRIMES model learning by doing curves, parameters that represent subjective perception of technology costs as seen by consumers, standards and other factors are built-in to represent the penetration of new technologies (Capros, 2005). As the selected studies do not explicitly state their choice of modelling of technological state we don not discuss how these assumption could affected the selected cost estimates. Nevertheless, we argue that one of the possible explanations why endogenous technological change representation and back-stop energy sources got so little attention in the modelling of the EU-wide climate change policies might be that most of the selected studies only predict costs in the short or medium run, i.e. for the years 2010 and 2025. Therefore it might be believed that for this time it is too early to expect back-stop energy sources substitute for conventional fossil fuels.