Errors and uncertainties in a gridded carbon dioxide emissions inventory

Tomohiro Oda, Rostyslav Bun, Vitaliy Kinakh, Petro Topylko, Mariia Halushchak, Gregg Marland, Thomas Lauvaux, Matthias Jonas, Shamil Maksyutov, Zbigniew Nahorski, Myroslava Lesiv, Olha Danylo and Joanna Horabik-Pyzel ()
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Tomohiro Oda: NASA Goddard Space Flight Center
Rostyslav Bun: Lviv Polytechnic National University
Vitaliy Kinakh: Lviv Polytechnic National University
Petro Topylko: Lviv Polytechnic National University
Mariia Halushchak: Lviv Polytechnic National University
Gregg Marland: Appalachian State University
Thomas Lauvaux: Laboratoire des sciences du climat et de l’environnement
Matthias Jonas: International Institute for Applied Systems Analysis
Shamil Maksyutov: National Institute for Environmental Studies
Zbigniew Nahorski: Systems Research Institute of Polish Academy of Sciences
Myroslava Lesiv: International Institute for Applied Systems Analysis
Olha Danylo: Lviv Polytechnic National University
Joanna Horabik-Pyzel: Systems Research Institute of Polish Academy of Sciences

Mitigation and Adaptation Strategies for Global Change, 2019, vol. 24, issue 6, No 8, 1007-1050

Abstract: Abstract 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 (CO2), gridded EI Open-source Data Inventory for Anthropogenic CO2 (ODIAC) with the multi-resolution, 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 urban-rural 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 monitoring and mitigation activity under the Paris Climate Agreement from the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of the Parties (COP21). We highlight the importance of capacity building for EI development and coordinated research efforts of EI, atmospheric observations, and modeling to overcome the challenges.

Keywords: Greenhouse gas emission; Emission inventory; Carbon dioxide; Carbon cycle; Uncertainty analysis; Climate mitigation; Remote sensing; Monitoring; Reporting and verification; Paris Agreement (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11027-019-09877-2

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