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Probabilistic approaches to the measurement of embodied carbon in buildings

 
: Gantner, Johannes; Fawcett, W.; Ellingham, I.

:

Pomponi, Francesco (Ed.):
Embodied Carbon in Buildings. Measurement, Management, and Mitigation
Cham: Springer International Publishing, 2018
ISBN: 978-3-319-72795-0 (Print)
ISBN: 978-3-319-72796-7 (Online)
S.23-50
Englisch
Aufsatz in Buch
Fraunhofer IBP ()

Abstract
The measurement of embodied carbon in buildings or building components encounters many problems of uncertainty, which are increased for life cycle measurement. The level of uncertainty for a measurement varies on a spectrum from precise knowledge to total ignorance. The most rudimentary way of measuring an uncertain variable is to use a single-value ‘best guess’. Methods that acknowledge uncertainty and give a probabilistic measurement of the variable include: a range, a three-point estimate, an empirical distribution, or a mathematical distribution. Life cycle measurement subject to uncertainty can be represented by a tree of possible future values, or by Monte Carlo simulation of sampled future values. When measurements are probabilistic, decision makers’ choices respond to their degree of risk aversion and time preference. In situations of uncertainty, flexible strategies that adapt to unfolding events can mitigate the risk of damaging outcomes. A worked example compares deterministic and probabilistic measurement of the embodied carbon of a construction system with reusable steel modules. The system reduces embodied carbon if the modules are reused. For the probabilistic measurement, the length of the service life of the modules and the probability of reuse are uncertain variables. The steel module system is compared with conventional reinforced concrete construction. The probabilistic approach provides additional information and understanding for decision makers.

: http://publica.fraunhofer.de/dokumente/N-487957.html