explained by the choice of the model, the model’s structure and the assumptions about the policy choices.

A few lessons can be distilled from this ex-ante literature review which should be learnt by policy makers in relation to the ex-ante work on the EU ETS and using it for informing further policy decision making. These lessons fall into three categories: those concerning the choice of the simulating model, the assumptions about model’s structure, and the assumptions about policy design. Accordingly, as is true of all lessons and concluding comments, these will need to be applied carefully and some may not apply to all ex-ante models.

The choice of the modelling approach – partial equilibrium vs. general equilibrium, bottom up vs. top-down – depends primarily on the objective of the particular study. However, to fully understand the cost estimates of any climate change policy such as the EU ETS and, in turn, to inform policy makers, analysis by both partial and general equilibrium models' is needed, since these different modelling approaches complement each other. Harrington et al. (2004) point out that, when individual regulation targets one particular sector, one might expect that the general equilibrium effects are negligible and detailed partial equilibrium models are more telling. However, in the case of the EU ETS, where many agents are directly or indirectly affected by this trading programme, it is important to evaluate the overall effects.

Each model’s structure represents a level of geographical and sectoral disaggregation and the interrelations among different regions and sectors. Regional and sectoral disaggregation itself determines important aspects such as the distribution of costs, the nature of market, and the representation of technological change. A distribution of total costs of climate change policies among the various economic agents is especially important for politicians. Therefore, more disaggregated models covering as many economic agents and all policy-affected regions would be of particular interest for policy makers as they answer the key questions of where different impacts of the climate policies will be felt. Likewise, high levels of aggregation fail to capture the ‘true’ nature of the various markets. For instance, most of the selected general equilibrium models assume perfect competition, constant returns to scale, as well as, rational consumers and producers, across different regions. However, these assumptions do not allow for the imperfect markets which might be present in some of the sectors and regions. Likewise, low level of disaggregation does not allow for a better representation of technological change which, as shown by the meta-analyses, is crucial in understanding the models’ outcomes. As the sectoral disaggregation is crucial for understanding the sources of technological change, the exogenous technological change representation, i.e. the application of a constant rate across all sectors of an economy will result in mis-estimation of carbon emissions, which is a key implication for climate change policy implication. All this implies that models with higher levels of regional and sectoral disaggregation deliver more comprehensive and reliable cost estimates. In the context of the EU ETS with twenty seven EU member states, sufficient disaggregation on the regional and sectoral levels is especially important.

The assumptions about the policy design are as important as the assumptions about the baseline and the structural modelling features. Anticipation of policy design options and institutional context as the reality unfolds is likely to lead to a credible ex-ante cost evaluation. The short EU ETS experience indicates that the initial allocation and timing of the political decision making matter for both cost-efficiency and equity concerns. Another lesson is that any climate policy, even the one operating on the larger scale, cannot be analysed in isolation as it is affected by other policies helping or impeding to meet the preset environmental targets. Likewise, the