and climate policy to affect the technology choices. This argument is somewhat supported by the early ex-post outcomes of the EU ETS. Hoffmann (2007) concludes that the EU ETS had some impact only on the operators' short term investment decision making due to too many uncertainties involved.

## Policy options

However, a few of the selected studies irrespective of their main modelling features generate very low costs estimates (exceptions are Klepper and Peterson, 2005, Kemfert et al., 2006, Peterson, 2006, who employ CGE models and provide lower than the average MAC estimates) suggesting that not all differences in the ex-ante costs estimates can be explained by the differences in the structural modelling choices. This might imply that the assumptions about the policy modelling choices are equally important in understanding the ex-ante costs of the EU ETS. This argument is strengthened by the dynamics of the actual price of EUA that was mostly driven by the policy making process rather than fundamentals during 2005-2007.

The assumptions about the actual EU ETS design got little attention in the early modelling of the ex-ante costs of the EU ETS (see Annex for a summary of these assumptions in our selected models). These studies (Capros and Mantzos, 2000, IPTS, 2000, Proost and Van Regemorter, 2003) assume one of the salient features of the EU ETS – grandfathered permit trading across different EU countries. Other attributes of EU ETS, such as an auctioning of the permits, a linkage to other flexibility mechanism (CDM and JI) or transaction cost of EU-wide emission trading and CDM and JI project mechanisms had been incorporated just before the launch of the trading system, when the design of the EU ETS was clear.

Due to the timing, the early simulation studies were not able to introduce the empirical allocation of $ CO_{2} $ permits – a driver of the actual price of the EUA – since the actual National Allocation Plans (NAPs) were not known when these studies were undertaken. This might explain why the early studies on average predicted higher marginal abatement costs than the later studies. The average MAC estimate of these studies is $ 60\ €_{2005} $ – one and half time higher than the average MAC estimate ( $ 39\ €_{2005} $ ) provided by late studies (see Table 4). Some of the late studies delivering very low permit price clearly state that the lower permit price is partly due to the efficiency gains from EU-wide trading but also due to the generous allocation of allowances to the exposed sectors (Klepper and Peterson, 2005). In the other study (Bohringer et al., 2005) the marginal value of carbon in the EU emissions trading system even falls to zero due to too generous allocation of permits for trading sectors.

Differently than the late studies, the early studies ignore the possibility of using CDM and JI credits by the operators in the EU ETS. Few studies show that even the currently limited availability of CDM and JI credits (some studies also incorporate the transaction costs related to these mechanisms, see Annex ) helps to accomplish the $ CO_{2} $ reduction targets in more efficient manner and, hence, leads to lower permit price. On average these studies (Stankeviciute and Criqui, 2008, COWI, 2004, Klepper and Peterson, 2005, Peterson, 2006, Alexeeva-Talebi and Anger, 2007) deliver the MAC estimate of $ 22\ €_{2005} $ – two times lower than the average MAC estimate of all selected studies.

### 5. Concluding lessons

It should be kept in mind that the models’ results are hypothetical and derived from optimisation exercise of different policy options, and that these results, as well as their reliability are mainly