Fichtner et al. (2003), Krey (2004) and Mundaca and Rodhe (2005) in classifying TCs under the Kyoto Flexible Mechanisms contain conceptual similarities regarding the different sources of TCs (e.g. negotiation, approval, M&V activities).

In turn, conceptual choices have an impact on the scope or boundary of the investigation and thus nature, scale and resulting burden of TCs. If we strictly follow Coase’s definition, one can argue that elements of TCs included under carbon markets may not qualify for TCs. This is because the CDM or JI processes that must be followed by project activities –such as registration, verification, certification- are necessary “production” steps for certified emission reductions. The same argument could be given for similar production steps involved, or required, in tradable certificate schemes for renewable energy and efficiency improvements. In all these markets, what is traded here is ‘certified emission reduction’, ‘certified energy savings’ or ‘certified renewable energy production’, and not emission reductions _per se_ . Another aspect to consider is the frequency and the time dimensions of TCs. Costs can be broken down as one-time costs, ongoing or fixed costs (MRC, 2004), and proportional (Michaelowa et al., 2003). For instance McCann et al. (2005) argue for a chronological classification for the measurement of TCs. While information costs may occur throughout the whole life of a project, contracting costs are likely to occur only during the implementation of the project. Such aspects must be carefully considered when determining conceptual choices as they influence interpretations and may or may not facilitate relative comparisons throughout the research field.

The quantification of TCs is a very complex exercise. It is thus no wonder why all the reviewed studies focus heavily on specific endogenous factors. Common to all of the reviewed studies is the need to analyze TCs at the micro level, which is consistent with Williamson (2010), who argues the need to examine economic organization at the microanalytic level. Within this context, the scale and resulting burden of TCs are estimated based on a variety of methods including: a ‘Total Resource Cost’ (utility) model (Joskow and Marron, 1992), ‘Energy Conservation Curves’ (Sathaye and Murtishaw, 2004), ‘Carbon Abatement Curves’ (Krey, 2005), and ‘Cost-effectiveness Analysis’ (Mundaca, 2007b). The model used by Joskow and Marron (1999) compared the costs and savings of DSM programs against known projections for the same types of costs and savings. The model focuses on the life-cycle cost of electric efficiency investments before taking into account the associated savings in electricity costs. Sathaye and Murtishaw, 2004 et al. (2004) focused on energy efficiency options and the representation of their marginal cost curves (i.e. cost of conserved energy (CCE) curves). The method quantifies the costs and benefits faced by consumers. The approach adopted by Krey (2005) estimated marginal carbon emission reduction cost and their relation to specific TCs. Mundaca (2007a) focused on the cost of energy savings. Similar to the approach used by Joskow and Marron (1999), the method aimed to quantify aggregated TCs based on the lifecycle stage of energy efficiency projects. However, unlike Joskow and Marrow, net financial benefits for end-users were estimated using actual residential energy prices (electricity and natural gas) as benchmarks. To populate these models, information and parameters come, for example, from statistical data, surveys, interviews and secondary data. In the case of the Kyoto Protocol mechanisms, studies source figures

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