Spatial GHG Inventory: Analysis of Uncertainty Sources. A Case Study for Ukraine |
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Authors: | R Bun M Gusti L Kujii O Tokar Y Tsybrivskyy A Bun |
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Institution: | (1) National University ‘Lviv Polytechnics’, 12 Bandera Street, 79013 Lviv, Ukraine;(2) International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria;(3) State Scientific and Research Institute of Information Infrastructure, National Academy of Sciences of Ukraine, P.O. Box 5446, 79031 Lviv, Ukraine |
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Abstract: | A geoinformation technology for creating spatially distributed greenhouse gas inventories based on a methodology provided
by the Intergovernmental Panel on Climate Change and special software linking input data, inventory models, and a means for
visualization are proposed. This technology opens up new possibilities for qualitative and quantitative spatially distributed
presentations of inventory uncertainty at the regional level. Problems concerning uncertainty and verification of the distributed
inventory are discussed. A Monte Carlo analysis of uncertainties in the energy sector at the regional level is performed,
and a number of simulations concerning the effectiveness of uncertainty reduction in some regions are carried out. Uncertainties
in activity data have a considerable influence on overall inventory uncertainty, for example, the inventory uncertainty in
the energy sector declines from 3.2 to 2.0% when the uncertainty of energy-related statistical data on fuels combusted in
the energy industries declines from 10 to 5%. Within the energy sector, the ‘energy industries’ subsector has the greatest
impact on inventory uncertainty. The relative uncertainty in the energy sector inventory can be reduced from 2.19 to 1.47%
if the uncertainty of specific statistical data on fuel consumption decreases from 10 to 5%. The ‘energy industries’ subsector
has the greatest influence in the Donetsk oblast. Reducing the uncertainty of statistical data on electricity generation in
just three regions – the Donetsk, Dnipropetrovsk, and Luhansk oblasts – from 7.5 to 4.0% results in a decline from 2.6 to
1.6% in the uncertainty in the national energy sector inventory. |
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Keywords: | energy sector geoinformation system greenhouse gas greenhouse gas inventory multilevel model spatial analysis uncertainty |
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