Uncertainties in Greenhouse Gas Inventories

Ometto, Jean Pierre; Bun, Rostyslav; Jonas, M.; Nahorski, Zbigniew

doi:10.1007/978-3-319-15901-0

Cited by 6 publications

(2 citation statements)

References 89 publications

(127 reference statements)

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“…In this section, we discuss the importance and challenges of EIs in future GHG monitoring and management, the prospects of improving emission GHG estimates using atmospheric data, the utility of the spatially explicit EIs (for both scientific and political purposes), and the future of EIs and their scientific and political applications. There were five key topics addressed in the 3 previous workshops in this series on uncertainty in emissions inventories (see Liberman et al 2007;White et al 2011;Ometto et al 2015):…”

Section: Discussionmentioning

confidence: 99%

“…However, the uncertainty reported with EIs often serves as an indicator for the level of confidence, rather than for the accuracy (Jonas et al 2010). As previously discussed in Liberman et al (2007), White et al (2011), and Ometto et al (2015), studying the variety of sources of errors and uncertainties is crucial in order to make EIs more robust and accurate for providing science-based guidance to global climate mitigation.…”

Section: Introductionmentioning

confidence: 99%

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Errors and uncertainties in a gridded carbon dioxide emissions inventory

Oda

Bun

Kinakh

et al. 2019

Mitig Adapt Strateg Glob Change

101

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Emission inventories (EIs) are the fundamental tool to monitor compliance with greenhouse gas (GHG) emissions and emission reduction commitments. Inventory accounting guidelines provide the best practices to help EI compilers across different countries and regions make comparable, national emission estimates regardless of differences in data availability. However, there are a variety of sources of error and uncertainty that originate beyond what the inventory guidelines can define. Spatially explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes and there are no specific guidelines to achieve spatial emission estimates. The errors and uncertainties associated with the spatial estimates are unique to the approaches employed and are often difficult to assess. This study compares the global, high-resolution (1 km), fossil fuel, carbon dioxide (CO 2), gridded EI Open-source Data Inventory for Anthropogenic CO 2 (ODIAC) with the multiresolution, spatially explicit bottom-up EI geoinformation technologies, spatio-temporal approaches, and full carbon account for improving the accuracy of GHG inventories (GESAPU) over the domain of Poland. By taking full advantage of the data granularity that bottom-up EI offers, this study characterized the potential biases in spatial disaggregation by emission sector (point and non-point emissions) across different scales (national, subnational/regional, and urban policy-relevant scales) and identified the root causes. While two EIs are in agreement in total and sectoral emissions (2.2% for the total emissions), the emission spatial patterns showed large differences (10~100% relative differences at 1 km) especially at the urbanrural transitioning areas (90-100%). We however found that the agreement of emissions over urban areas is surprisingly good compared with the estimates previously reported for US cities. This paper also discusses the use of spatially explicit EIs for climate mitigation applications beyond the common use in atmospheric modeling. We conclude with a discussion of current and future challenges of EIs in support of successful implementation of GHG emission

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Section: Discussionmentioning

confidence: 99%