IDEAS home Printed from https://ideas.repec.org/a/spr/masfgc/v22y2017i6d10.1007_s11027-016-9709-9.html
   My bibliography  Save this article

A comparison of five high-resolution spatially-explicit, fossil-fuel, carbon dioxide emission inventories for the United States

Author

Listed:
  • Maya G. Hutchins

    (Arizona State University
    Appalachian State University
    Arizona State University)

  • Jeffrey D. Colby

    (Appalachian State University)

  • Gregg Marland

    (Appalachian State University)

  • Eric Marland

    (Appalachian State University)

Abstract

The quantification of fossil-fuel-related emissions of carbon dioxide to the atmosphere is necessary in order to accurately represent carbon cycle fluxes and to understand and project the details of the global carbon cycle. In addition, the monitoring, reporting, and verification (MRV) of carbon dioxide emissions is necessary for the success of international agreements to reduce emissions. However, existing fossil-fuel carbon dioxide (FFCO2) emissions inventories vary in terms of the data and methods used to estimate and distribute FFCO2. This paper compares how the approaches used to create spatially explicit FFCO2 emissions inventories affect the spatial distribution of emissions estimates and the magnitude of emissions estimates in specific locales. Five spatially explicit FFCO2 emission inventories were compared: Carbon Dioxide Information and Analysis Center (CDIAC), Emission Database for Global Atmospheric Research (EDGAR), Fossil Fuel Data Assimilation System (FFDAS), Open-source Data Inventory for Anthropogenic CO2 (ODIAC), and Vulcan. The effects of using specific data and approaches in the creation of spatially explicit FFCO2 emissions inventories, and the effect of resolution on data representation are analyzed using graphical, numerical, and cartographic approaches. We examined the effect of using top-down versus bottom-up approaches, nightlights versus population proxies, and the inclusion of large point sources. The results indicate that the approach used to distribute emissions in space creates distinct patterns in the distribution of emissions estimates and hence in the estimates of emissions in specific locations. The different datasets serve different purposes but collectively show the key role of large point sources and urban centers and the strong relationship between scale and uncertainty.

Suggested Citation

  • Maya G. Hutchins & Jeffrey D. Colby & Gregg Marland & Eric Marland, 2017. "A comparison of five high-resolution spatially-explicit, fossil-fuel, carbon dioxide emission inventories for the United States," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 22(6), pages 947-972, August.
  • Handle: RePEc:spr:masfgc:v:22:y:2017:i:6:d:10.1007_s11027-016-9709-9
    DOI: 10.1007/s11027-016-9709-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11027-016-9709-9
    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/s11027-016-9709-9?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. David Wheeler & Kevin Ummel, 2008. "Calculating CARMA: Global Estimation of CO2 Emissions from the Power Sector," Working Papers 145, Center for Global Development.
    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. Kazuyuki Miyazaki & Kevin Bowman, 2023. "Predictability of fossil fuel CO2 from air quality emissions," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Bin Zhou & Stephan Thies & Ramana Gudipudi & Matthias K B Lüdeke & Jürgen P Kropp & Diego Rybski, 2020. "A Gini approach to spatial CO2 emissions," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    3. 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.
    4. 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.
    5. 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.

    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. Daví-Arderius, Daniel & Sanin, María-Eugenia & Trujillo-Baute, Elisa, 2017. "CO2 content of electricity losses," Energy Policy, Elsevier, vol. 104(C), pages 439-445.
    2. Aboumahboub, Tino & Schaber, Katrin & Wagner, Ulrich & Hamacher, Thomas, 2012. "On the CO2 emissions of the global electricity supply sector and the influence of renewable power-modeling and optimization," Energy Policy, Elsevier, vol. 42(C), pages 297-314.
    3. Boruff, Bryan J. & Moheimani, Navid R. & Borowitzka, Michael A., 2015. "Identifying locations for large-scale microalgae cultivation in Western Australia: A GIS approach," Applied Energy, Elsevier, vol. 149(C), pages 379-391.
    4. Blankespoor, Brian & Basist, Alan & Dinar, Ariel & Dinar, Shlomi, 2012. "Assessing economic and political impacts of Hydrological variability on treaties : case studies on the Zambezi and Mekong basins," Policy Research Working Paper Series 5996, The World Bank.
    5. Grant, Don & Jorgenson, Andrew K. & Longhofer, Wesley, 2016. "How organizational and global factors condition the effects of energy efficiency on CO2 emission rebounds among the world's power plants," Energy Policy, Elsevier, vol. 94(C), pages 89-93.
    6. Schaber, Katrin & Steinke, Florian & Hamacher, Thomas, 2012. "Transmission grid extensions for the integration of variable renewable energies in Europe: Who benefits where?," Energy Policy, Elsevier, vol. 43(C), pages 123-135.
    7. Brian Blankespoor & Alan Basist & Ariel Dinar & Shlomi Dinar & Harold Houba & Neil Thomas, 2014. "Assessing the Economic and Political Impacts of Climate Change on International River Basins using Surface Wetness in the Zambezi and Mekong Basins," Tinbergen Institute Discussion Papers 14-005/II, Tinbergen Institute.
    8. Lindner, Soeren & Liu, Zhu & Guan, Dabo & Geng, Yong & Li, Xin, 2013. "CO2 emissions from China’s power sector at the provincial level: Consumption versus production perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 164-172.
    9. 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.
    10. Janina Ketterer & Jana Lippelt, 2009. "Kurz zum Klima: Globaler Stand der Erneuerbaren Energien," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(24), pages 83-85, December.
    11. 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.

    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:22:y:2017:i:6:d:10.1007_s11027-016-9709-9. 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.