Conditionally autoregressive model for spatial disaggregation of activity data in GHG inventory: Application for agriculture sector in Poland

Horabik-Pyzel J, Charkovska N, Danylo O, Nahorski Z, & Bun R (2015). Conditionally autoregressive model for spatial disaggregation of activity data in GHG inventory: Application for agriculture sector in Poland. In: Proceedings, 4th International Workshop on Uncertainty in Atmospheric Emissions, 7-9 October 2015, Krakow, Poland. pp. 25-31 Warsaw, Poland: Systems Research Institute, Polish Academy of Sciences. ISBN 83-894-7557-X

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Abstract

This report presents a novel approach for allocation of spatially correlated data, such as emission inventories, into finer spatial scales conditional on covariate information observable in a fine grid. Spatial dependence is modelled with the conditional autoregressive structure introduced into a linear model as a random effect. The maximum likelihood approach to inference is employed, and the optimal predictors are developed to assess missing values in a fine grid. The usefulness of the proposed technique is shown for agricultural sector of GHG inventory in Poland. An example of allocation of livestock data (a number of horses) from district to municipality level is analysed. The results indicate that the proposed method outperforms a naive and commonly used approach of proportional distribution.

Item Type: Book Section
Uncontrolled Keywords: 4th International Workshop on Uncertainty in Atmospheric Emissions
Research Programs: Ecosystems Services and Management (ESM)
Depositing User: Luke Kirwan
Date Deposited: 10 Feb 2016 11:48
Last Modified: 07 Mar 2016 13:47
URI: http://pure.iiasa.ac.at/11885

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