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- Verifying greenhouse gas emissions: Methods to support international climate agreements
AB - The world's nations are moving toward agreements that will bind us together in an effort to limit future greenhouse gas emissions. Verifying greenhouse gas emissions: Methods to support international climate agreements. Abstract The world's nations are moving toward agreements that will bind us together in an effort to limit future greenhouse gas emissions. Fingerprint Climate. International Cooperation.
Climate Change. Fossil Fuels. Public Sector. Human Activities. IG 3 IS is not designed to check compliance with regulations, but rather to provide information on policy- and management-relevant scales and ensure that the information provided is consistent with a global network of high quality observations and models. IG 3 IS looks to serve users decision-makers who are able to take action to reduce emissions of greenhouse gases GHGs and pollutants that reduce air quality. This service is based on existing, successful methods and use cases for which the scientific and technical skill is proven.
Therefore, we wanted our organization to show the progression of this change in uncertainty, from guessing to knowing, with the flipping of a question mark. As can be seen on the logo above, in light blue is a right side up question mark encasing the number three. The authors analyze the increase in uncertainty and develop methods to reduce it. The two approaches followed are i tracing emissions by source and estimating gridded totals Bun et al. The group of authors following the first approach namely under [i] does not start from a regular grid. These are classified as point, line, or areal sources and according to intensity and physical size with respect to the territory under investigation.
The resulting geospatial database contains information about the administrative assignment of each emission source as a vector map object. The emissions from very diverse emission sources can be combined into a grid, allowing total emissions to be calculated, while the grid size can be chosen arbitrarily depending on analysis and visualization needs.
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This offers a unique opportunity to calculate total emissions for different levels of administrative unit settlements, municipalities, districts, provinces without loss of accuracy, as well as separately by emission category, greenhouse gas, type of fossil fuel, etc. The approach allows uncertainty to be considerably reduced for high-resolution inventories. Starting by assembling statistical data from the lowest available administrative level ideally municipality limits disaggregation depth and errors.
This is in contrast to handling uncertainty by way of disaggregation, which is applied, for example, in gridded approaches.
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Here, uncertainty is determined by disaggregation depth and increases with it, when going from large to smaller scales. Note that this important difference is preserved even if emissions estimated at, or aggregated to larger e. Poland serves as a joint case study. Emissions from various sources are calculated both for grids and administrative units.
The results demonstrate the considerable unevenness of spatial distributions of GHG emissions. Distributions and their uncertainty ranges are estimated by applying a Monte Carlo method. Bun et al. The authors following the second approach namely [ii] look, in particular, at the uncertainty associated with the allocation of point sources.
Any misallocation of these emission sources can have important consequences for high-resolution inventories, especially if their emission intensities are high. Such misallocations happen, for example, when databases are combined to merge information using logical rules. Hogue et al. The authors use population density as a proxy to distribute emissions spatially across grids that vary in size. They find that relative uncertainty total uncertainty divided by total emissions at grid-cell level decreases with increasingly coarser resolution.
In most cases, relative uncertainty also decreases with increasing emissions from point sources. The papers by Lesiv et al. Lesiv et al. The forest map contains information about dominant tree species, total biomass, and net primary production NPP. To constrain that carbon flux, both a flux-based and a stock-based method are applied. Oda et al. The difference between the two inventories is understood to serve as a proxy for errors and uncertainties associated with ODIAC.
This difference is small for total emission estimates of countries 2.
However, it increases toward smaller spatial scales, indicating that disaggregation error and uncertainty increase. Based on their findings for Poland, the authors envisage using ODIAC globally to support monitoring verification and even at subnational levels—it is not unusual for countries to run emission inventories at the state or provincial levels while reporting only national emissions to the UNFCCC. However, as noted by the authors, such a request would need to accompany concerted global actions, ranging from the collection and reporting of data, through monitoring, to international governance.
Zimnoch et al. They present a set of methods based on atmospheric observations of CO 2 and CH 4 mixing ratios and their isotopic composition, the use of additional data relating to the atmospheric concentration of radon and mixing layer height, and atmospheric modeling, to identify and quantify urban emissions.
These methods complement each other; they allow a determination not only of the contribution of different emission sources to the total atmospheric load but also of the fluxes of those gases. The methods provide an efficient way of quantifying the surface emissions of major GHGs from distributed sources and thus represent a complementary approach to accounting emission bottom-up.
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The papers by La Notte et al. The research described in these papers contributes equally to key issues 1 one-sided perspective , 3, and 4. La Notte et al. By considering uncertainty in an on—off mode instantaneous learning , policy recommendations at regional and provincial levels can be made.
Verstraete proposes a new method that does not lead to an increase in uncertainty during the process of overlaying data sets mapped to different grids. This so-called regridding process is an important preprocessing tool in handling spatially resolved datasets, offering considerable potential, particularly for authors compiling high-resolution spatial inventories cf.
Group I. Such researchers frequently face the problem of having to rely on data represented using different grids e. The approach offered by Verstraete can be used for remapping, for example, a grid onto administrative borders or vice versa. Fuzzy rule-based methods are elaborated and tested for regridding using additionally available knowledge in order to obtain better results, particularly during spatial disaggregation processes.
Gusti et al. The authors conclude that considering the quality of governance is key if medium-term mitigation policies, usually designed for low CO 2 prices, are developed.
Verifying greenhouse gas emissions: Methods to support international climate agreements
The authors demonstrate how knowledge of a change in uncertainty can be gained from analyzing annually revised emission estimates in retrospect. In cases of pronounced learning, the reduction in uncertainty can be well described by the coefficient in an exponential model. Their approach goes beyond that favored by the IPCC of estimating and monitoring uncertainty to help prioritize efforts to improve the accuracy of inventories and guide decisions on methodological choice IPCC : Chapter 6.
Understanding what it takes to decrease uncertainty over time is crucial, on the one hand for evaluating the quality of compliance under which countries meet their emission reduction targets and, on the other, for setting future emission reduction targets more skillfully, that is, from an emission change-versus-uncertainty perspective rather than from an emissions change-only perspective. Data are subdivided for testing and learning. They thus reflect the challenges and benefits of including inventory uncertainty in policy analysis, and where advances are being made.
The issues raised by the authors and featured in their papers, and the role played by uncertainty analysis in many of their arguments, highlight the importance of such efforts. While the IPCC clearly stresses the value of conducting uncertainty analyses and offers guidance on executing them, the arguments made here in favor of studying uncertainty go well beyond any suggestions made by the IPCC to date.
The rationale recalled in Box 2 for improving and conducting uncertainty analyses is considered to still hold true: uncertainty analysis is needed for developing clear understanding and informed policy. Uncertainty matters and it is key to many issues related to inventorying and reducing emissions. Dealing proactively with uncertainty allows useful knowledge to be generated that the international community should have to hand while strengthening the Paris Agreement, the successor agreement to the Kyoto Protocol.
All papers in this special issue confirm or advance key insights 1, 3, 4, and 5.