Reductions in carbon emissions can be achieved by switching away from carbon-intensive fuels. Such a switch depends on several layers of substitution possibilities: substitution of energy by other inputs (labour and capital) in the production process, substitution of energy intensive goods by non-energy intensive goods in final consumptions, substitution of more polluting fossil fuels by less polluting ones (intra-fossil fuel substitution), substitution of fossil fuels by non-fossil ones (non-fossil fuel substitution or non-carbon back-stop technology). The easier these substitution possibilities are, the lower the overall burden of reducing $ CO_{2} $ emissions (Repetto and Austin, 1997).

As the existence of competitive non-carbon back-stop technologies eases substitution away from conventional fuels, assumptions related to the availability and the cost of such technologies are crucial (Barker et al., 2002). Naturally, models that include viable back-stop technologies mostly tend to predict smaller economic impacts for emissions mitigation than models that don’t.

Assumptions about TC and the associated efficiency improvements strongly influence projected emissions and energy consumption for both the baseline and the policy simulations. In climate change policy modelling TC is usually captured in two ways: (i) by a parameter called ‘autonomous energy efficiency improvement’ (AEEI); or (ii) by total factor productivity (TFP) growth (Barker et al., 2006). According to this treatment, TC is exogenous and independent from both scenarios and price levels. This exogenous TC representation totally neglects the literature on endogenous induced and direct TC that proves, in theory and practice, that TC reacts to market incentives provided either by regulation or by changing energy prices (Acemoglu, 1998, Newell et al., 1999, Barker et al., 2006). This suggests that exogenous representation of TC could over- or underestimate (depending on the values of crucial parameters) the costs of abatement.

Climate change policies are designed to reduce GHG emissions in order to avoid damaging consequences of global warming and improve environmental quality. However, many simulation models do not account for these avoided damages or benefits (climate and non-climate) linked to these policies, and only consider the direct economic consequences of preventing climate change. The inclusion of these benefits unambiguously reduces the overall mitigation costs and might even lead to estimating negative net costs.

As the meta-analyses show, it is not only assumptions about the structural modelling features to be important, but also the assumptions about the design of climate change policy. It has been shown that carbon tax (permit auctioning) revenue recycling considerably reduces the costs of the climate policies. The modelling literature considers two main forms of revenue recycling: (1) via lump-sum transfers; or (2) via a reduction in other distortionary forms of taxation. The empirical literature shows that carbon tax recycling by providing lump-sum transfers to the society is less efficient than by reducing existing taxes. The latter, the so-called green tax reform, potentially provides two kinds of benefits, as the improvement in environmental quality is obtained while at the same time increasing overall economic efficiency: a “double dividend” (Repetto et al., 1992). Because of this opportunity ‘to kill two birds with one stone’ a carbon tax might be politically more acceptable than other climate change mitigation policies.

The main purpose of flexibility mechanisms – international emissions trading and other project based mechanisms (e.g. Clean Development Mechanism and Joint Implementation under the Kyoto Protocol) – is to reach cost effectiveness in mitigating climate change. These mechanisms are designed to ensure that abatement of GHG emissions occurs first where it is cheapest. Models