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

Imperial College London
University College London
British Geological Survey
Department of Earth Science and Engineering
King's College London

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

5 Citations (SciVal)

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
Number of pages	12
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 - 30 Apr 2025

Bibliographical note

Publisher Copyright:
© 2025 The Author(s)

Data Availability Statement

Data is provided in the Supplementary Data file on Zenodo: https://zenodo.org/records/13,830,002 , and the codebase developed and used is available on GitHub, as detailed within the manuscript.

Acknowledgements

Participants of workshops to help inform the aggregates systems and input into appropriate assumptions for unknown data from the Mineral Products Association, British Aggregates Association, British Geological Survey, Infrastructure Pipeline Authority, Construction Products Association, and Office for National Statistics are thanked for their input.

Funding

Funding from EPSRC (EP/V011820/1) and the Office for National Statistics (https:// bidstats.uk/tenders/2022/W17/773587228) is acknowledged.

UN SDGs

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

SDG 8 Decent Work and Economic Growth
SDG 12 Responsible Consumption and Production

Keywords

Construction aggregates
Bayesian inference
Bayes theorem
Material flow analysis
England

Access to Document

10.1016/j.resconrec.2025.108135Licence: CC BY

Cite this

@article{371201ef40dd47bba7b64dbd6db797f7,

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.",

keywords = "Construction aggregates, Bayesian inference, Bayes theorem, Material flow analysis, England",

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doi = "10.1016/j.resconrec.2025.108135",

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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 - 2025/4/30

Y1 - 2025/4/30

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 - Construction aggregates

KW - Bayesian inference

KW - Bayes theorem

KW - Material flow analysis

KW - England

UR - https://www.scopus.com/pages/publications/85215939261

U2 - 10.1016/j.resconrec.2025.108135

DO - 10.1016/j.resconrec.2025.108135

M3 - Article

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

Bibliographical note

Data Availability Statement

Acknowledgements

Funding

UN SDGs

Keywords

Access to Document

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Cite this