[Zurück]

@article{zoboli16:1334[TUW-249278],
    author = {Zoboli, Ottavia and Laner, David and Zessner, Matthias and Rechberger, Helmut},
    title = {{A}dded {V}alues of {T}ime {S}eries in {M}aterial {F}low {A}nalysis - {T}he {A}ustrian {P}hosphorus {B}udget from 1990 to 2011},
    journal = {{J}ournal of {I}ndustrial {E}cology},
    year = {2016},
    volume = {20},
    pages = {1334--1348},
    keywords = {data reconciliation; industrial ecology; phosphorus; substance flow analysis ({S}{F}{A}); time series; uncertainty},
    abstract = {{M}aterial flow analysis is a tool that is increasingly used as a foundation for resource management and environmental protection. {T}his tool is primarily applied in a static manner to individual years, ignoring the impact of time on the material budgets. {I}n this study, a detailed multiyear model of the {A}ustrian phosphorus budget covering the period 1990-2011 was built to investigate its behavior over time and test the hypothesis that a multiyear approach can also contribute to the improvement of static budgets. {F}urther, a novel method was applied to investigate the quality and characteristics of the data and quantify the uncertainty. {T}he degree of change between the budgets was assessed and showed that approximately half of the flows have changed significantly and, at times, abruptly since 1990, but it is not possible to distinguish unequivocally between constant and moderately changing flows given their uncertainty. {T}he study reveals that the phosphorus transported in waste flows has increased more rapidly than its recovery, which accounted for 55{\%} to 60{\%} of the total waste phosphorus in 1990 and only 40{\%} in 2011. {T}he loss ratio in landfills and cement kilns has oscillated in the range of 40{\%} to 50{\%}. {F}rom a methodological point of view, the multiyear approach has broadened the conceptual model of the budget, making it more suitable as a basis for material accounting and monitoring. {M}oreover, the analysis of the data reconciliation process over a long period of time proved to be a useful tool for identifying systematic errors in the model.}
}