Bayesian material flow analysis of the construction aggregate cycle in England

Adam R. Mason; Tom Bide; Junyang Wang; John Morley; Mohit Arora; Alperen Yayla; Julia A. Stegemann; Rupert J. Myers

doi:10.1016/j.resconrec.2025.108135

Bayesian material flow analysis of the construction aggregate cycle in England

Adam R. Mason, Tom Bide, Junyang Wang, John Morley, Mohit Arora, Alperen Yayla, Julia A. Stegemann, Rupert J. Myers^*

^*Corresponding author for this work

Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Quantitative analysis of material over their life cycles provides crucial insight into the movement of materials within economies, informing economic and environmental impact assessment, and governmental and industrial interventions. Material Flow Analysis (MFA) for whole material cycles is often hindered by data gaps, limiting its practical value. We apply Bayesian Material Flow Analysis (BaMFA) to quantify England's 2019 construction aggregates (sand, gravel, crushed rock) system, reducing the labour-intensive manual data reconciliation requirement of conventional MFA approaches. Despite industry-reported data describing only 20 % of the system, BaMFA fully quantifies the system, provides novel insights into its supply-demand balance, and highlights opportunities for enhanced resource efficiency and waste minimisation. This includes improved quantification of primary aggregate consumption (142 Mt, 68 % from indigenous sources) and landfilling (20 Mt, 96 % demolition waste). This research demonstrates the potential of BaMFA for quantitative analysis of material systems and evidence-based action for more sustainable and resilient futures.

Original language	English
Article number	108135
Journal	Resources, Conservation and Recycling
Volume	215
Early online date	25 Jan 2025
DOIs	https://doi.org/10.1016/j.resconrec.2025.108135
Publication status	Published - Apr 2025

Keywords

Bayes theorem
Bayesian inference
Construction aggregates
England
Material flow analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.resconrec.2025.108135

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title = "Bayesian material flow analysis of the construction aggregate cycle in England",

abstract = "Quantitative analysis of material over their life cycles provides crucial insight into the movement of materials within economies, informing economic and environmental impact assessment, and governmental and industrial interventions. Material Flow Analysis (MFA) for whole material cycles is often hindered by data gaps, limiting its practical value. We apply Bayesian Material Flow Analysis (BaMFA) to quantify England's 2019 construction aggregates (sand, gravel, crushed rock) system, reducing the labour-intensive manual data reconciliation requirement of conventional MFA approaches. Despite industry-reported data describing only 20 % of the system, BaMFA fully quantifies the system, provides novel insights into its supply-demand balance, and highlights opportunities for enhanced resource efficiency and waste minimisation. This includes improved quantification of primary aggregate consumption (142 Mt, 68 % from indigenous sources) and landfilling (20 Mt, 96 % demolition waste). This research demonstrates the potential of BaMFA for quantitative analysis of material systems and evidence-based action for more sustainable and resilient futures.",

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T1 - Bayesian material flow analysis of the construction aggregate cycle in England

AU - Mason, Adam R.

AU - Bide, Tom

AU - Wang, Junyang

AU - Morley, John

AU - Arora, Mohit

AU - Yayla, Alperen

AU - Stegemann, Julia A.

AU - Myers, Rupert J.

PY - 2025/4

Y1 - 2025/4

N2 - Quantitative analysis of material over their life cycles provides crucial insight into the movement of materials within economies, informing economic and environmental impact assessment, and governmental and industrial interventions. Material Flow Analysis (MFA) for whole material cycles is often hindered by data gaps, limiting its practical value. We apply Bayesian Material Flow Analysis (BaMFA) to quantify England's 2019 construction aggregates (sand, gravel, crushed rock) system, reducing the labour-intensive manual data reconciliation requirement of conventional MFA approaches. Despite industry-reported data describing only 20 % of the system, BaMFA fully quantifies the system, provides novel insights into its supply-demand balance, and highlights opportunities for enhanced resource efficiency and waste minimisation. This includes improved quantification of primary aggregate consumption (142 Mt, 68 % from indigenous sources) and landfilling (20 Mt, 96 % demolition waste). This research demonstrates the potential of BaMFA for quantitative analysis of material systems and evidence-based action for more sustainable and resilient futures.

AB - Quantitative analysis of material over their life cycles provides crucial insight into the movement of materials within economies, informing economic and environmental impact assessment, and governmental and industrial interventions. Material Flow Analysis (MFA) for whole material cycles is often hindered by data gaps, limiting its practical value. We apply Bayesian Material Flow Analysis (BaMFA) to quantify England's 2019 construction aggregates (sand, gravel, crushed rock) system, reducing the labour-intensive manual data reconciliation requirement of conventional MFA approaches. Despite industry-reported data describing only 20 % of the system, BaMFA fully quantifies the system, provides novel insights into its supply-demand balance, and highlights opportunities for enhanced resource efficiency and waste minimisation. This includes improved quantification of primary aggregate consumption (142 Mt, 68 % from indigenous sources) and landfilling (20 Mt, 96 % demolition waste). This research demonstrates the potential of BaMFA for quantitative analysis of material systems and evidence-based action for more sustainable and resilient futures.

KW - Bayes theorem

KW - Bayesian inference

KW - Construction aggregates

KW - England

KW - Material flow analysis

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DO - 10.1016/j.resconrec.2025.108135

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AN - SCOPUS:85215939261

SN - 0921-3449

VL - 215

JO - Resources, Conservation and Recycling

JF - Resources, Conservation and Recycling

M1 - 108135

ER -

Bayesian material flow analysis of the construction aggregate cycle in England

Abstract

Keywords

UN SDGs

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