2.2.1 Relationship to UNFCCC
There is a well-established system under the UNFCCC and the KP for reviewing inventories of developed countries, and this is the basis for assessing progress towards emissions reduction targets and commitments. As indicated above, COP decisions require consistency between FRELs/FRLs, GHGI and REDD+ emissions and removals estimates to be assessed as a requirement for participation in incentive schemes.
In 2011 the UNFCCC decided(1) that the 96GL in conjunction with the GPG2000 and GPG2003 should be used by developing countries for estimating and reporting anthropogenic emissions and removals. As a consequence for REDD+, the inventory framework in which GFOI operates is effectively defined by the GPG2003 . The MGD therefore cross-references the GPG2003 . Countries can presumably use scientific updates in the 2006GL within this framework, and so the MGD also provides references to corresponding sections of 2006GL and the 2013 Wetlands Supplement . In 2015 the UNFCCC Subsidiary Body for Implementation noted the requests from non-Annex I Parties for training on the use of the 2006GLs , which may also be used for REDD+ (paragraph 29 of document FCCC/SBI/2015/10 ).
The concept of good practice underpins the GPG2003 and the 2006GL . Good practice is defined by IPCC(2) as applying to inventories that contain neither over- nor under-estimates so far as can be judged, and in which uncertainties are reduced as far as is practicable. Although there is no pre-defined level of precision, this definition aims to maximize precision without introducing bias, given the level of resources reasonably available for GHGI development. This level of resource is implicitly understood by the international inventory review and technical assessment processes administered by the UNFCCC and outlined in context of REDD+ in Chapter 6.
Good practice covers cross-cutting issues relevant to GHGI development, including data collection including sampling strategies, uncertainty estimation, methodological choice based on identification of key categories (those which make greatest contributions to the absolute level and trend in emissions and removals), quality assurance and quality control (QA/QC), and time series consistency. QA/QC entails amongst other things internal self-consistency checks, and may include checks against independent, or at least independently-compiled, estimates (Section 2.3.4).
Good practice entails the following general principles:
- Transparency (documentation sufficient to assess the extent to which good practice requirements have been met – includes a clear description of input data, methods and assumptions )
- Completeness (all relevant categories of emissions and removals are estimated and reported)
- Consistency (differences between years reflect differences in emissions or removals and are not artefacts of changes in methodology or data availability)
- Comparability (inventory estimates can be compared between countries)
- Accuracy (delivered by the use of methods designed to produce neither under- nor over-estimates and reduces uncertainties so far as practicable – this addresses both accuracy and precision)
The REDD+ MRV decision 14/CP.19 refers to these terms except comparability, and in the REDD+ context completeness is used in the sense that the provision of information should allow for reconstruction of the results.
Use of remote sensing data may require special attention to consistency, because satellites go out of commission or operability, new ones enter into use, and ways of using the imagery evolve. This may affect time series of emissions estimates and the consistency with historical data which is necessary for establishing FRELs or FRLs. Generic guidance for maintaining consistency is provided in GPG2003 and the 2006GL(3). Techniques described in Chapter 5, Section 5.1 should also be applied to minimise bias even if data sources do change over time.
Developing countries currently may not have data and estimates that meet these general principles fully. The most common issues, based on those identified in a 2009 technical paper from UNFCCC(4), are:
- lack of suitable data for regularly estimating forest area change and changes in forest carbon stocks in many countries. Carbon stock data for above-ground and below-ground pools are often based on estimates or conversions using IPCC default data, and few countries are able to provide information on all five carbon pools or estimates from biomass burning. Consequently inventories are often incomplete.
- lack of transparency arising from the reliance on expert opinion, independent assessments or model estimations as information sources to produce forest carbon data in the absence of suitable data national specific data
- estimates based either on single-date, sample measurements or on integration heterogeneous data sources, rather than using a systematic and consistent measurement and monitoring approach, thus consistency cannot be ensured
- lack of experience in applying the IPCC GPG as a common approach for estimation and monitoring
- limited information on sources of error and uncertainty levels of the estimates provided by countries, and on approaches to analysing, reducing, and dealing with these in international reporting.
Despite significant (though not necessarily even) progress since 2009, these issues still need consideration. The joint use of remotely-sensed and ground-based data as outlined in the MGD can help address these issues, in the context of REDD+ activities.