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Errors and uncertainties in a gridded carbon dioxide emissions inventory

Author

Listed:
  • Tomohiro Oda

    (NASA Goddard Space Flight Center
    Universities Space Research Association)

  • Rostyslav Bun

    (Lviv Polytechnic National University
    WSB University)

  • Vitaliy Kinakh

    (Lviv Polytechnic National University)

  • Petro Topylko

    (Lviv Polytechnic National University)

  • Mariia Halushchak

    (Lviv Polytechnic National University
    International Institute for Applied Systems Analysis)

  • 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
    Warsaw School of Information Technology)

  • Myroslava Lesiv

    (International Institute for Applied Systems Analysis)

  • Olha Danylo

    (Lviv Polytechnic National University
    International Institute for Applied Systems Analysis)

  • Joanna Horabik-Pyzel

    (Systems Research Institute of Polish Academy of Sciences)

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.

Suggested Citation

  • Tomohiro Oda & Rostyslav Bun & Vitaliy Kinakh & Petro Topylko & Mariia Halushchak & Gregg Marland & Thomas Lauvaux & Matthias Jonas & Shamil Maksyutov & Zbigniew Nahorski & Myroslava Lesiv & Olha Dany, 2019. "Errors and uncertainties in a gridded carbon dioxide emissions inventory," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 1007-1050, August.
  • Handle: RePEc:spr:masfgc:v:24:y:2019:i:6:d:10.1007_s11027-019-09877-2
    DOI: 10.1007/s11027-019-09877-2
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    References listed on IDEAS

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    3. YoungSeok Hwang & Jung-Sup Um & JunHwa Hwang & Stephan Schlüter, 2020. "Evaluating the Causal Relations between the Kaya Identity Index and ODIAC-Based Fossil Fuel CO 2 Flux," Energies, MDPI, vol. 13(22), pages 1-20, November.
    4. Zhibo Zhao & Tian Yuan & Xunpeng Shi & Lingdi Zhao, 2020. "Heterogeneity in the relationship between carbon emission performance and urbanization: evidence from China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(7), pages 1363-1380, October.
    5. Li, Zhihui & Deng, Xiangzheng & Peng, Lu, 2020. "Uncovering trajectories and impact factors of CO2 emissions: A sectoral and spatially disaggregated revisit in Beijing," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    6. Matthias Jonas & Rostyslav Bun & Zbigniew Nahorski & Gregg Marland & Mykola Gusti & Olha Danylo, 2019. "Quantifying greenhouse gas emissions," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 839-852, August.

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