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Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources

Author

Listed:
  • Rostyslav Bun

    (Lviv Polytechnic National University
    University of Dąbrowa Górnicza)

  • Zbigniew Nahorski

    (Systems Research Institute of the Polish Academy of Sciences
    Warsaw School of Information Technology)

  • Joanna Horabik-Pyzel

    (Systems Research Institute of the Polish Academy of Sciences)

  • Olha Danylo

    (International Institute for Applied Systems Analysis)

  • Linda See

    (International Institute for Applied Systems Analysis)

  • Nadiia Charkovska

    (Lviv Polytechnic National University)

  • Petro Topylko

    (Lviv Polytechnic National University)

  • Mariia Halushchak

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

  • Myroslava Lesiv

    (International Institute for Applied Systems Analysis)

  • Mariia Valakh

    (Lviv Polytechnic National University)

  • Vitaliy Kinakh

    (Lviv Polytechnic National University)

Abstract

Greenhouse gas (GHG) inventories at national or provincial levels include the total emissions as well as the emissions for many categories of human activity, but there is a need for spatially explicit GHG emission inventories. Hence, the aim of this research was to outline a methodology for producing a high-resolution spatially explicit emission inventory, demonstrated for Poland. GHG emission sources were classified into point, line, and area types and then combined to calculate the total emissions. We created vector maps of all sources for all categories of economic activity covered by the IPCC guidelines, using official information about companies, the administrative maps, Corine Land Cover, and other available data. We created the algorithms for the disaggregation of these data to the level of elementary objects such as emission sources. The algorithms used depend on the categories of economic activity under investigation. We calculated the emissions of carbon, nitrogen sulfure and other GHG compounds (e.g., CO2, CH4, N2O, SO2, NMVOC) as well as total emissions in the CO2-equivalent. Gridded data were only created in the final stage to present the summarized emissions of very diverse sources from all categories. In our approach, information on the administrative assignment of corresponding emission sources is retained, which makes it possible to aggregate the final results to different administrative levels including municipalities, which is not possible using a traditional gridded emission approach. We demonstrate that any grid size can be chosen to match the aim of the spatial inventory, but not less than 100 m in this example, which corresponds to the coarsest resolution of the input datasets. We then considered the uncertainties in the statistical data, the calorific values, and the emission factors, with symmetric and asymmetric (lognormal) distributions. Using the Monte Carlo method, uncertainties, expressed using 95% confidence intervals, were estimated for high point-type emission sources, the provinces, and the subsectors. Such an approach is flexible, provided the data are available, and can be applied to other countries.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:masfgc:v:24:y:2019:i:6:d:10.1007_s11027-018-9791-2
    DOI: 10.1007/s11027-018-9791-2
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    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. Jörg Verstraete, 2014. "Solving the map overlay problem with a fuzzy approach," Climatic Change, Springer, vol. 124(3), pages 591-604, June.
    3. 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.
    4. 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.
    5. Joanna Horabik & Zbigniew Nahorski, 2014. "Improving resolution of a spatial air pollution inventory with a statistical inference approach," Climatic Change, Springer, vol. 124(3), pages 575-589, June.
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    Citations

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    Cited by:

    1. Hongjiang Liu & Fengying Yan & Hua Tian, 2020. "A Vector Map of Carbon Emission Based on Point-Line-Area Carbon Emission Classified Allocation Method," Sustainability, MDPI, vol. 12(23), pages 1-21, December.
    2. 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.
    3. Katarzyna Bebkiewicz & Zdzisław Chłopek & Jakub Lasocki & Krystian Szczepański & Magdalena Zimakowska-Laskowska, 2020. "The Inventory of Pollutants Hazardous to the Health of Living Organisms, Emitted by Road Transport in Poland between 1990 and 2017," Sustainability, MDPI, vol. 12(13), pages 1-12, July.
    4. 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.

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