existing ex-post measures is valuable for understanding not only why the ex-ante predictions differ from the ex-post measures, but also why the ex-ante measures themselves diverge so much. This implies that before discussing how in general ex-ante predictions compares with ex-post outcomes, firstly, it should be understood why there is such a range of the ex-ante cost estimates and which of these estimates are more reliable and, hence, might be used for a policy decision making and for a comparison with the ex-post outcomes.

Previous literature surveys of the modelling studies on the ex-ante costs of the EU ETS (Dannenberg et al., 2007, Oberndorfer and Rennings, 2007) have not tried to explain the fundamental differences in their selected models and their costs estimates. The meta-analyses (Repetto and Austin, 1997, Barker et al., 2002, Fischer and Morgenstern, 2005, Barker et al., 2006) complement the modelling literature, albeit without a specific reference to the EU ETS, by identifying the key assumptions that explain most of the variations in costs estimates across a broad range of models. To the best of our knowledge, no study has tried to merge the findings from the meta-analyses, the ex-ante and the ex-post literature dedicated for the EU ETS within a comprehensive framework.

The remainder of the paper is organised as follows. Section 2 focuses on the main concepts and definitions of the modelling studies devoted to quantifying costs of the environmental policies and presents firstly taxonomy of modelling studies, secondly, the key modelling assumptions and, finally, the different concepts of costs. Section 3 summarises the early ex-post literature. Section 4 investigates the ex-ante costs of the EU ETS as suggested by simulation studies, firstly, introducing our selected studies; secondly, comparing the ex-ante cost outcomes; and, finally, discussing why differences or similarities of ex-ante costs across different models. Section 5 provides some concluding lessons for undertaking the ex-ante work and using it for informing the policy decision-making process.

### 2. Guiding definitions and concepts of models assessing costs of climate change policies

### 2.1 Taxonomy of modelling studies

The numerical simulation models representing the key economic interrelations between consumers, producers and policy makers in one or more markets, countries or regions are intensively used to assess the effects of various climate change policies on economic systems. The studies reviewed below mainly uses two kinds of models: partial equilibrium (PE) models, and (computable) general equilibrium (CGE) models $ ^{1} $ . PE and CGE models mainly differ with respect to the degree of detail that characterises their description of the energy system, besides their consideration of economy-wide feedback effects (Böhringer and Löschel, 2002). Both PE and CGE models can be either static or dynamic. Static models analyse equilibrium of all variables either at the specific point of time or compares two static outcomes of all variables of different time points. Comparative static models do not take into account adaptation process from one equilibrium to another one. Dynamic models account for these transitional effects which can be simulated and evaluated. The geographical dimension of both PE and CGE models might be national (e.g. Germany), regional (e.g. EU Member States) or global. The choice of the geographical scale and its aggregation determine how the international trade relations are presented.