IDEAS home Printed from https://ideas.repec.org/a/spr/masfgc/v24y2019i6d10.1007_s11027-019-09877-2.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11027-019-09877-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11027-019-09877-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Khrystyna Boychuk & Rostyslav Bun, 2014. "Regional spatial inventories (cadastres) of GHG emissions in the Energy sector: Accounting for uncertainty," Climatic Change, Springer, vol. 124(3), pages 561-574, June.
    2. David Wheeler & Kevin Ummel, 2008. "Calculating CARMA: Global Estimation of CO2 Emissions from the Power Sector," Working Papers 145, Center for Global Development.
    3. Raupach, M.R. & Rayner, P.J. & Paget, M., 2010. "Regional variations in spatial structure of nightlights, population density and fossil-fuel CO2 emissions," Energy Policy, Elsevier, vol. 38(9), pages 4756-4764, September.
    4. Riley M. Duren & Charles E. Miller, 2012. "Measuring the carbon emissions of megacities," Nature Climate Change, Nature, vol. 2(8), pages 560-562, August.
    5. Kevin Ummel, 2012. "CARMA Revisited: An Updated Database of Carbon Dioxide Emissions from Power Plants Worldwide," Working Papers 304, Center for Global Development.
    6. A. P. Ballantyne & C. B. Alden & J. B. Miller & P. P. Tans & J. W. C. White, 2012. "Increase in observed net carbon dioxide uptake by land and oceans during the past 50 years," Nature, Nature, vol. 488(7409), pages 70-72, August.
    7. Zhu Liu & Dabo Guan & Wei Wei & Steven J. Davis & Philippe Ciais & Jin Bai & Shushi Peng & Qiang Zhang & Klaus Hubacek & Gregg Marland & Robert J. Andres & Douglas Crawford-Brown & Jintai Lin & Hongya, 2015. "Reduced carbon emission estimates from fossil fuel combustion and cement production in China," Nature, Nature, vol. 524(7565), pages 335-338, August.
    8. Matthias Jonas & Gregg Marland & Volker Krey & Fabian Wagner & Zbigniew Nahorski, 2014. "Uncertainty in an emissions-constrained world," Climatic Change, Springer, vol. 124(3), pages 459-476, June.
    9. Olha Danylo & Rostyslav Bun & Linda See & Nadiia Charkovska, 2019. "High-resolution spatial distribution of greenhouse gas emissions in the residential sector," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 941-967, August.
    10. Nadiia Charkovska & Mariia Halushchak & Rostyslav Bun & Zbigniew Nahorski & Tomohiro Oda & Matthias Jonas & Petro Topylko, 2019. "A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 907-939, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Angel Hsu & Xuewei Wang & Jonas Tan & Wayne Toh & Nihit Goyal, 2022. "Predicting European cities’ climate mitigation performance using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Kazuyuki Miyazaki & Kevin Bowman, 2023. "Predictability of fossil fuel CO2 from air quality emissions," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nadiia Charkovska & Mariia Halushchak & Rostyslav Bun & Zbigniew Nahorski & Tomohiro Oda & Matthias Jonas & Petro Topylko, 2019. "A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 907-939, August.
    2. Daví-Arderius, Daniel & Sanin, María-Eugenia & Trujillo-Baute, Elisa, 2017. "CO2 content of electricity losses," Energy Policy, Elsevier, vol. 104(C), pages 439-445.
    3. Nadiia Charkovska & Joanna Horabik-Pyzel & Rostyslav Bun & Olha Danylo & Zbigniew Nahorski & Matthias Jonas & Xu Xiangyang, 2019. "High-resolution spatial distribution and associated uncertainties of greenhouse gas emissions from the agricultural sector," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 881-905, August.
    4. Chunli Zhou & Xiqiao Lin & Renhao Wang & Bowei Song, 2023. "Real-Time Carbon Emissions Monitoring of High-Energy-Consumption Enterprises in Guangxi Based on Electricity Big Data," Energies, MDPI, vol. 16(13), pages 1-19, July.
    5. Mathieu Fortin, 2021. "Comparison of uncertainty quantification techniques for national greenhouse gas inventories," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 26(2), pages 1-20, February.
    6. Rostyslav Bun & Zbigniew Nahorski & Joanna Horabik-Pyzel & Olha Danylo & Linda See & Nadiia Charkovska & Petro Topylko & Mariia Halushchak & Myroslava Lesiv & Mariia Valakh & Vitaliy Kinakh, 2019. "Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 853-880, August.
    7. Xiao, Hongwei & Ma, Zhongyu & Mi, Zhifu & Kelsey, John & Zheng, Jiali & Yin, Weihua & Yan, Min, 2018. "Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data," Applied Energy, Elsevier, vol. 231(C), pages 1070-1078.
    8. Cui, Yuanzheng & Zhang, Weishi & Wang, Can & Streets, David G. & Xu, Ying & Du, Mingxi & Lin, Jintai, 2019. "Spatiotemporal dynamics of CO2 emissions from central heating supply in the North China Plain over 2012–2016 due to natural gas usage," Applied Energy, Elsevier, vol. 241(C), pages 245-256.
    9. 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.
    10. Liu, Xiaoyu & Duan, Zhiyuan & Shan, Yuli & Duan, Haiyan & Wang, Shuo & Song, Junnian & Wang, Xian'en, 2019. "Low-carbon developments in Northeast China: Evidence from cities," Applied Energy, Elsevier, vol. 236(C), pages 1019-1033.
    11. Jinpei Ou & Xiaoping Liu & Xia Li & Meifang Li & Wenkai Li, 2015. "Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-20, September.
    12. Jörg Verstraete, 2019. "Solving the general map overlay problem using a fuzzy inference system designed for spatial disaggregation," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 1101-1122, August.
    13. Man, Yi & Yan, Yukun & Wang, Xu & Ren, Jingzheng & Xiong, Qingang & He, Zhenglei, 2023. "Overestimated carbon emission of the pulp and paper industry in China," Energy, Elsevier, vol. 273(C).
    14. Chen, Yuhong & Lyu, Yanfeng & Yang, Xiangdong & Zhang, Xiaohong & Pan, Hengyu & Wu, Jun & Lei, Yongjia & Zhang, Yanzong & Wang, Guiyin & Xu, Min & Luo, Hongbin, 2022. "Performance comparison of urea production using one set of integrated indicators considering energy use, economic cost and emissions’ impacts: A case from China," Energy, Elsevier, vol. 254(PC).
    15. Ling Yang & Michael L. Lahr, 2019. "The Drivers of China’s Regional Carbon Emission Change—A Structural Decomposition Analysis from 1997 to 2007," Sustainability, MDPI, vol. 11(12), pages 1-18, June.
    16. Jörg Verstraete, 2014. "Solving the map overlay problem with a fuzzy approach," Climatic Change, Springer, vol. 124(3), pages 591-604, June.
    17. Stephany Isabel Vallarta-Serrano & Ana Bricia Galindo-Muro & Riccardo Cespi & Rogelio Bustamante-Bello, 2023. "Analysis of GHG Emission from Cargo Vehicles in Megacities: The Case of the Metropolitan Zone of the Valley of Mexico," Energies, MDPI, vol. 16(13), pages 1-19, June.
    18. Wang, Xueqi & Liu, Gengyuan & Coscieme, Luca & Giannetti, Biagio F. & Hao, Yan & Zhang, Yan & Brown, Mark T., 2019. "Study on the emergy-based thermodynamic geography of the Jing-Jin-Ji region: Combined multivariate statistical data with DMSP-OLS nighttime lights data," Ecological Modelling, Elsevier, vol. 397(C), pages 1-15.
    19. Xiao, Huijuan & Wang, Daoping & Qi, Yu & Shao, Shuai & Zhou, Ya & Shan, Yuli, 2021. "The governance-production nexus of eco-efficiency in Chinese resource-based cities: A two-stage network DEA approach," Energy Economics, Elsevier, vol. 101(C).
    20. An, Runying & Yu, Biying & Li, Ru & Wei, Yi-Ming, 2018. "Potential of energy savings and CO2 emission reduction in China’s iron and steel industry," Applied Energy, Elsevier, vol. 226(C), pages 862-880.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:masfgc:v:24:y:2019:i:6:d:10.1007_s11027-019-09877-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.