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The economic feasibility of short rotation coppice (SRC) production and energy conversion in areas contaminated by Chernobyl-derived (137)Cs was evaluated taking the spatial variability of environmental conditions into account. Two sequential GIS-embedded submodels were developed for a spatial assessment, which allow for spatial variation in soil contamination, soil type, and land use. These models were applied for four SRC production and four energy conversion scenarios for the entire contaminated area of Ukraine, Belarus, and Russia and for a part of the Bragin district, Belarus. It was concluded that in general medium-scale SRC production using local machines is most profitable. The areas near Chernobyl are not suitable for SRC production since the contamination levels in SRC wood exceed the intervention limit. Large scale SRC production is not profitable in areas where dry and sandy soils predominate. If the soil contamination does not exceed the intervention limit and sufficient SRC wood is available, all energy conversion scenarios are profitable.

A Monte Carlo analysis of two sequential GIS-embedded submodels, which evaluate the economic feasibility of short rotation coppice (SRC) production and energy conversion in areas contaminated by Chernobyl-derived (137)Cs, was performed to allow for variability of environmental conditions that was not contained in the spatial model inputs. The results from this analysis were compared to the results from the deterministic model presented in part I of this paper. It was concluded that, although the variability in the model results due to within-gridcell variability of the model inputs was considerable, the prediction of the areas where SRC and energy conversion is potentially profitable was robust. If the additional variability in the model input that is not contained in the input maps is also taken into account, the SRC production and energy conversion appears to be potentially profitable at more locations for both the small scale and large scale production scenarios than the model predicted using the deterministic model.