coming directly from consultants offering related services (e.g. baseline setting) or from actual projects contained in the UNFCCC databases. For surveys, an important criterion is the sample size (in relation to the statistical universe) of responses, which is crucial in establishing the confidence level of the estimates. Some studies explicitly acknowledge this limitation (e.g. Hein and Blok, 1995; Mundaca, 2007b). One of the main reasons for having low statistically representative samples lies in stakeholders being reluctant to disclose information and thus participate in TCs related research. Logically, all methodologies that quantify TCs also entail numerous assumptions and expert input. As a whole, the methodologies applied to quantify of TCs have to be interpreted with reserve and precautions.

TCs are expressed in most cases as a proportion of total investment costs or a portion of carbon credits. TCs are evaluated in different functional units (e.g. as percentage of investment costs; as burden per issued CER or size of emission reductions) making comparisons impossible and emphasizing further that TCs are highly case- and context-specific. Therefore, in order to interpret TCs estimated values, it is essential to confront them to relevant parameters within the context of the projects or the investments. For instance, as in Michaelowa and Jotzo (2005), parameters for the impact of implementation and TCs in CDM projects are presented in three different scenarios (standard, low- and high- CDM TCs) and include the international sale price, the total CER sales, the CDM revenues and the share of CDM in the global carbon market.

Related to the transaction cost accounting problem mentioned previously, the level of awareness of respondents themselves is an important intrinsic uncertainty aspect to consider when analyzing the data collected. As part of the evaluation and the level of awareness about TCs, Skytte et al. (2003) provide an interesting self-assessment on the knowledge of the respondents on lead times and TCs. Based on a qualitative survey filled by 44 respondents, 17% of them consider to have a “expert knowledge” on TCs, around 30% a “moderate knowledge” and 28% have “little knowledge” (the remaining did not answer). There are some gaps in the completion of the survey: 12 respondents provided values for the total planning phase (without distinguishing sub-costs); 12 respondents evaluated the total production phase and their sub-costs; 13 respondents provided a value for the search and pre-feasibility costs as well as negotiation and development; only 8 values were given for the total costs of the project illustrating some lack of knowledge and the complexity in evaluating TCs. In turn, Skytte et al. (2003) note some discrepancies between the cumulative values of all the phases of the project and the single value provided for each phase. For instance the cumulative values provided for the planning phase is twice as high as the disaggregated cost estimation provided for the planning phase. The same has been observed for the production phase. Skytte et al. (2003) attribute this discrepancy to the fact that respondents tend to overestimate the value of each sub-cost.

# **7. Conclusions**

The objective of this report was to provide a comprehensive review of the empirical literature addressing transaction costs of GHG mitigation technologies. The review addresses transactions costs related to both market behavior (e.g. information asymmetries, trading activity) and policy conditions (e.g. regulatory

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