A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling

Nadiia Charkovska, Mariia Halushchak, Rostyslav Bun, Zbigniew Nahorski (), Tomohiro Oda, Matthias Jonas and Petro Topylko
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Nadiia Charkovska: Lviv Polytechnic National University
Mariia Halushchak: Lviv Polytechnic National University
Rostyslav Bun: Lviv Polytechnic National University
Zbigniew Nahorski: Polish Academy of Sciences
Tomohiro Oda: NASA Goddard Space Flight Center
Matthias Jonas: International Institute for Applied Systems Analysis
Petro Topylko: Lviv Polytechnic National University

Mitigation and Adaptation Strategies for Global Change, 2019, vol. 24, issue 6, No 4, 907-939

Abstract: Abstract Industrial processes cause significant emissions of greenhouse gases (GHGs) to the atmosphere and, therefore, have high mitigation and adaptation potential for global change. Spatially explicit (gridded) emission inventories (EIs) should allow us to analyse sectoral emission patterns to estimate the potential impacts of emission policies and support decisions on reducing emissions. However, such EIs are often based on simple downscaling of national level emission estimates and the changes in subnational emission distributions do not necessarily reflect the actual changes driven by the local emission drivers. This article presents a high-definition, 100-m resolution bottom-up inventory of GHG emissions from industrial processes (fuel combustion activities in energy and manufacturing industries, fugitive emissions, mineral products, chemical industries, metal production and food and drink industries), which is exemplified for data for Poland. The study objectives include elaboration of the universal approach for mapping emission sources, algorithms for emission disaggregation, estimation of emissions at the source level and uncertainty analysis. We start with IPCC-compliant national sectoral GHG estimates made using Polish official statistics and, then, propose an improved emission disaggregation algorithm that fully utilises a collection of activity data available at the national/provincial level to the level of individual point and diffused (area) emission sources. To ensure the accuracy of the resulting 100-m resolution emission fields, the geospatial data used for mapping emission sources (point source geolocation and land cover classification) were subject to thorough human visual inspection. The resulting 100-m emission field even holds cadastres of emissions separately for each industrial emission category. We also compiled cadastres in regular grids and, then, compared them with the Emission Database for Global Atmospheric Research (EDGAR). A quantitative analysis of discrepancies between both results reveals quite frequent misallocations of point sources used in the EDGAR compilation that considerably deteriorate high-resolution inventories. We also use a Monte-Carlo method-based uncertainty assessment that yields a detailed estimation of the GHG emission uncertainty in the main categories of the analysed processes. We found that the above-mentioned geographical coordinates and patterns used for emission disaggregation have the greatest impact on the overall uncertainty of GHG inventories from the industrial processes. We evaluate the mitigation potential of industrial emissions and the impact of separate emission categories. This study proposes a method to accurately quantify industrial emissions at a policy relevant spatial scale in order to contribute to the local climate mitigation via emission quantification (local to national) and scientific assessment of the mitigation effort (national to global). Apart from the above, the results are also of importance for studies that confront bottom-up and top-down approaches and represent much more accurate data for global high-resolution inventories to compare with.

Keywords: Greenhouse gas emission; Industrial sector; Manufacturing industry; Fugitive emission; Spatial inventory; Uncertainty analysis; Monte-Carlo method (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11027-018-9836-6

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