Standardization of baseline and additionality determination under the CDM

Clean Development Mechanism (CDM) project developers have long complained about the complexities of project-specific baseline setting and the vagaries of additionality determination. In response to this, the CDM Executive Board took bold steps towards the standardization of CDM methodologies, culminating in the approval of guidelines for the establishment of performance standards in November 2011. The guidelines specify a performance standard stringency level for both baseline and additionality of 80% for several priority sectors and 90% for all other sectors. However, an analysis of 14 large-scale CDM methodologies that use performance standard approaches challenges this top-down approach to the performance standard design. An appropriate performance standard stringency level strongly depends on sector and technology characteristics. A single stringency level for baseline and additionality determination is appropriate only for greenfield projects, but not for retrofit ones. Overly simple, highly aggregated performance standards are unlikely to ensure high environmental integrity, and difficult questions regarding stringency and updating frequency will eventually have to be addressed on a rather disaggregated level. A careful balance between data requirements and the practicability of performance standards is essential because the heavy data requirements of the existing performance standard methodologies have been the key barrier to their actual implementation. Policy relevance CDM regulators have been pushed by many stakeholders to standardize baseline setting and eliminate project-specific additionality determination. At first glance, performance standards seem to provide the perfect solution for both tasks. However, a one-size-fits-all political decision – e.g. the average of the top 20% performers as enshrined in the Marrakech Accords – is inappropriate. Substantial disaggregation of performance standards is required both technologically and geographically in order to limit overand under-crediting and close loopholes for non-additional projects. As a lack of reliable and complete data has been and will be a key bottleneck for the development of performance standards, international support for data collection will be indispensable, but costly, and time-consuming. Empirically driven, techno-economic assessments of performance standard stringency levels must be the central task of the future work on standardized methodologies, and should not be sidelined by perceived needs of policy makers to take bold decisions under time pressures. DOI: https://doi.org/10.1080/14693062.2013.745114 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-77129 Accepted Version Originally published at: Hayashi, Daisuke; Michaelowa, Axel (2013). Standardization of baseline and additionality determination under the CDM. Climate Policy, 13(2):191-209. DOI: https://doi.org/10.1080/14693062.2013.745114 1 Standardization of baseline and additionality determination under the Clean Development Mechanism Daisuke Hayashi 1,* and Axel Michaelowa 1,2 1 Department of Political Science, University of Zurich, Affolternstrasse 56, 8050 Zurich, Switzerland 2 Perspectives, Klosbachstrasse 2, 8032 Zurich, Switzerland * Corresponding author: [email protected] Abstract: Clean Development Mechanism (CDM) project developers have complained for many years about the complexities of project-specific baseline setting and the vagaries of additionality determination. In response to this, the CDM Executive Board took bold steps towards standardization of CDM methodologies, culminating in approval of guidelines for the establishment of performance standards in November 2011. The guidelines specify performance standard stringency for both baseline and additionality at a level of 80% for several priority sectors and 90% for all other sectors. However, an analysis of 14 large-scale CDM methodologies using performance standard approaches challenges this top-down approach to the performance standard design. An appropriate level of performance standard stringency strongly depends on sector and technology characteristics. A single stringency level for baseline and additionality determination is appropriate only for greenfield projects, but not for retrofit ones. Overly simple, highly aggregated performance standards are unlikely to ensure high environmental integrity, and difficult questions regarding stringency and updating frequency will eventually have to be addressed on a rather disaggregated level. A careful balance between data requirements and practicability of performance standards is essential because heavy data requirements of the existing performancestandard methodologies have been the key barrier to the actual implementation. Clean Development Mechanism (CDM) project developers have complained for many years about the complexities of project-specific baseline setting and the vagaries of additionality determination. In response to this, the CDM Executive Board took bold steps towards standardization of CDM methodologies, culminating in approval of guidelines for the establishment of performance standards in November 2011. The guidelines specify performance standard stringency for both baseline and additionality at a level of 80% for several priority sectors and 90% for all other sectors. However, an analysis of 14 large-scale CDM methodologies using performance standard approaches challenges this top-down approach to the performance standard design. An appropriate level of performance standard stringency strongly depends on sector and technology characteristics. A single stringency level for baseline and additionality determination is appropriate only for greenfield projects, but not for retrofit ones. Overly simple, highly aggregated performance standards are unlikely to ensure high environmental integrity, and difficult questions regarding stringency and updating frequency will eventually have to be addressed on a rather disaggregated level. A careful balance between data requirements and practicability of performance standards is essential because heavy data requirements of the existing performancestandard methodologies have been the key barrier to the actual implementation.