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Verifying Greenhouse Gas Emissions: Methods to Support International Climate Agreements
nings, but they need to be significantly expanded. An international effort among developed countries to prepare and publish spatially and temporally gridded estimates of national emissions would provide critical initial data for the independent verification of national emissions, and for this effort, the disaggregated data may be more important than just the total magnitudes of emissions.
Governments also have a critical role in developing standardized methods to ensure that the gridded estimates are comparable and that they are produced at the appropriate spatial and temporal resolution to support comparison with the models. The horizontal resolution necessary is likely to be 50 to 100 km for global models, 8 km for regional models (Lauvaux et al., 2008), and 1 km for point sources. All of these methods would yield improved emissions estimates with any good initial data, even if they were not at the optimal resolution for the method. Temporal resolution, particularly the typical diurnal, weekly, and/or seasonal cycle of emissions, is also a critical piece of initial data. Because of day-night patterns in meteorology, greenhouse gases emitted at different times of the day can end up at different locations. For near-instantaneous measurements near point sources, the emissions measured are only from the last hour and depend on the duty cycle of the power plant.
The uncertainties in current estimates of emissions for the various greenhouse gases and major sources, as evaluated by the countries submitting the national reports, are summarized in Table 2.2. Uncertainties in self-reported CO2 emission estimates are low (<10 percent) in many developed countries and could be lowered to similar levels in others by using the most accurate IPCC methods. Reducing uncertainties for N2O and CH4 emission estimates will also require improved activity data and emission factors, which will in some cases require research. Uncertainties in total anthropogenic N2O are driven by the AFOLU component and are likely to remain high (10-100 percent) in the near term. For CH4, the relative importance of energy and AFOLU sources will determine the extent to which uncertainty can be reduced. If improvements are made, uncertainties for total anthropogenic emissions of CH4 could be as low as 10 percent in countries where emissions are dominated by energy, and as high as 50 percent in countries where emissions are dominated by AFOLU.
Extending regular UNFCCC inventories to other countries, pushing inventories to higher tiers, carrying out research to improve emission factors, and encouraging the expansion of inventories to estimate emissions at finer spatial and temporal scales should not only reduce uncertainty, but also improve opportunities for independent verification of traditional, national emissions inventories. Consequently, UNFCCC parties should strengthen self-reported national emissions inventories in the following manner:
Extend regular inventory reporting and review to all countries.
Where necessary, provide sustained technical and financial support to develop and maintain institutional capacity and tools and training for preparing inventories in developing countries.
Create a central land-use database to improve AFOLU estimates in national inventories from developing countries (see Chapter 3).
Continue to improve methods used by all countries.
Support basic research on greenhouse gas emissions processes and corresponding improvements in IPCC methodologies, particularly for biogenic sources and the AFOLU sector (see Chapter 3).
Extend top-tier (most stringent) reporting to the most important greenhouse gas sources in each country.
Facilitate cross-comparisons of self-reported data with data derived from other monitoring methods and develop independent data sources.
Support the development of inventories of naturally occurring, land-based emissions and sinks for all lands, not just managed lands.
Promote free and open access to relevant national and international statistics.