Regional spatial inventories (cadastres) of GHG emissions in the Energy sector: Accounting for uncertainty

• DOI: 10.1007/s10584-013-1040-9
• Publication Date: 2014-01-31T21:45:05Z
• Authors: Khrystyna Boychuk; Rostyslav Bun
• Keywords: Emission inventory; Inventory valuation

An improvement of methods for the inventory of greenhouse gas (GHG) emissions is necessary to ensure effective control of commitments to emission reduction. The national inventory reports play an important role, but do not reflect specifics of regional processes of GHG emission and absorption for large-area countries. In this article, a GIS approach for the spatial inventory of GHG emissions in the energy sector, based on IPCC guidelines, official statistics on fuel consumption, and digital maps of the region under investigation, is presented. We include mathematical background for the spatial emission inventory of point, line and area sources, caused by fossil-fuel use for power and heat production, the residential sector, industrial and agricultural sectors, and transport. Methods for the spatial estimation of emissions from stationary and mobile sources, taking into account the specifics of fuel used and technological processes, are described. Using the developed GIS technology, the territorial distribution of GHG emissions, at the level of elementary grid cells 2 km×2 km for the territory ofWestern Ukraine, is obtained. Results of the spatial analysis are presented in the form of a geo-referenced database of emissions, and visualized as layers of digital maps. Uncertainty of inventory results is calculated using the Monte Carlo approach, and the sensitivity analysis results are described. The results achieved demonstrated that the relative uncertainties of emission estimates, for CO2 and for total emissions (in CO2 equivalent), depend largely on uncertainty in the statistical data and on uncertainty in fuels’ calorific values. The uncertainty of total emissions stays almost constant with the change of uncertainty of N2O emission coefficients, and correlates strongly with an improvement in knowledge about CH4 emission processes. The presented approach provides an opportunity to create a spatial cadastre of emissions, and to use this additional knowledge for the analysis and reduction of uncertainty. It enables us to identify territories with the highest emissions, and estimate an influence of uncertainty of the large emission sources on the uncertainty of total emissions. Ascribing emissions to the places where they actually occur helps to improve the inventory process and to reduce the overall uncertainty.