Özet:

In this paper, we demonstrate value generalisation from a sample of ecosystem assets – municipally managed trees - to all tree assets within an urban ecosystem accounting area. A Bayesian network model is used to machine-learn non-parametric correlation patterns between biophysical site condition variables and output variables of an ecosystem service model – here iTree Eco for modelling the regulating services of urban forests. The paper also demonstrates the use of spatial Bayesian network modelling to quantify the reliability of value generalisation for accounting purposes. Value generalisation entails inferring ecosystem service values for all locations in an ecosystem accounting area, where the accounting practitioner has less information about the asset and its context, than in an available sample of managed sites within the accounting area. The modelling is carried out as a “proof-of-principle” of potential value generalisation and uncertainty analysis methods for ecosystem accounting. It does not cover all regulating ecosystem services of urban forests, nor cultural services. While noting that wide confidence intervals for generalised values pose challenges for using monetary accounts for the accounting purpose of change detection, we find that tree-specific asset valuation is possible in an urban accounting setting. Our findings serve the purpose of raising awareness about asset values of urban green infrastructure, to bring them more on a par with grey infrastructure in urban planning. We also argue that the reliability of the asset value of individual trees is also good enough to be used for non-accounting purposes, such as municipal tree damage assessments.

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