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Subject and Keywords:
Inwentaryzacje gazów cieplarnianych ; Ghg inventory ; Agricultural sector ; Spatial correlation ; Disaggregation ; Conditional autoregressive model ; Sektor rolnictwa ; Korelacja przestrzenna ; Dezagregacja ; Warunkowy model autoregresyjny
This report presents a novel approach for allocation of spatially correlated data, such as emission inventories, to 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.
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Projects co-financed by:
Operational Program Digital Poland, 2014-2020, Measure 2.3: Digital accessibility and usefulness of public sector information; funds from the European Regional Development Fund and national co-financing from the state budget.