Imperial College London
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DrYvesPlancherel

Faculty of Engineering, Department of Earth Science & Engineering

Lecturer in Climate Change and the Environment
 
 
 
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Contact

 

+44 (0)20 7594 2967y.plancherel

 
 
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Location

 

4.44Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Wang:2022,
author = {Wang, J and Ray, K and Brito-Parada, P and Plancherel, Y and Bide, T and Mankelow, J and Morley, J and Stegemann, J and Myers, R},
journal = {ArXiv},
title = {A Bayesian approach for the modelling of material stocks and flows with  incomplete data},
url = {http://arxiv.org/abs/2211.06178v1},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Material Flow Analysis (MFA) is used to quantify and understand the lifecycles of materials from production to end of use, which enables environmental,social and economic impacts and interventions. MFA is challenging as availabledata is often limited and uncertain, giving rise to an underdetermined systemwith an infinite number of solutions when attempting to calculate the values ofall stocks and flows in the system. Bayesian statistics is an effective way toaddress these challenges as it rigorously quantifies uncertainty in the dataand propagates it in a system flow model to provide the probabilitiesassociated with model solutions. Furthermore, the Bayesian approach provides anatural way to incorporate useful domain knowledge about the system through theelicitation of the prior distribution.  This paper presents a novel Bayesian approach to MFA. We propose a mass basedframework that directly models the flow and change in stock variables in thesystem, including systems with simultaneous presence of stocks anddisaggregation of processes. The proposed approach is demonstrated on a globalaluminium cycle, under a scenario where there is a shortage of data, coupledwith weakly informative priors that only require basic information on flows andchange in stocks. Bayesian model checking helps to identify inconsistencies inthe data, and the posterior distribution is used to identify the variables inthe system with the most uncertainty, which can aid data collection. Wenumerically investigate the properties of our method in simulations, and showthat in limited data settings, the elicitation of an informative prior cangreatly improve the performance of Bayesian methods, including for bothestimation accuracy and uncertainty quantification.
AU  - Wang,J
AU  - Ray,K
AU  - Brito-Parada,P
AU  - Plancherel,Y
AU  - Bide,T
AU  - Mankelow,J
AU  - Morley,J
AU  - Stegemann,J
AU  - Myers,R
PY  - 2022///
TI  - A Bayesian approach for the modelling of material stocks and flows with  incomplete data
T2  - ArXiv
UR  - http://arxiv.org/abs/2211.06178v1
ER  -
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