Focus Area 1A – MFA of aggregates

This focus area aims to map the UK’s metabolism of MCMs at sufficiently high detail to enable reliable modelling of implementation and effects of technical circular economy practices.

Description

PDR1A Dr Junyang Wang, worked with Dr Rupert Myers and his research team at Imperial College, with the close involvement of Professor Julia Stegemann at UCL, and Dr Joseph Mankelow and Tom Bide. The overall aim of the project was to enable mapping of the UK metabolism of MCMs at sufficiently high detail (material properties, spatial and temporal distribution) to enable:

1. Understanding of how MCMs flow through and accumulate over their life cycles
2. Modelling of implementation and effects of technical circular economy practices. The first part of this project focussed on developing a novel Bayesian material flow analysis (MFA) methodology, which is needed to map real material systems that contain data gaps, without losing significant information through assumptions. A Bayesian MFA model was created and tested and described in a journal paper titled Bayesian Material Flow Analysis for systems with multiple levels of disaggregation and high dimensional data.

In the latter part of this project, use of the model for mapping of UK stocks and flows of aggregates was demonstrated. In the case study chosen, 3.2-3.8 Gt of aggregates are likely to be required in GB by 2030, but this demand cannot be met by current low approval rates for extraction. Major infrastructure projects, such as a tidal power project in the River Severn, will distort demand and disrupt supply.

Modelling the UK’s metabolism of all materials in high detail using MFA is an essential first step to assessment of life cycle environmental and economic impacts, including, for example, security of supply of MCMs for the UK National Infrastructure and Construction Pipeline. It will also enable investigation of the effects of interventions to support future security of supply and the transition to a Circular Economy.

Through the Bayesian approach, material flow systems can be modelled despite imperfect availability of data. The results will inform future data collection strategies, so that unnecessary data collection can be avoided, reducing data collection and reporting expenses for both industry and government. They will help to understand the required degree of transparency and detail for collection of data from industry.

Ideas about the Bayesian MFA approach and progress have been shared with potential partner organisations (ONS, Cloud Cycle, GCP Applied Technologies, Network Rail, Balfour Beatty), to generate interest in collaboration. The project may address materials other than MCMs in the context of the ONS Integrated Data Service.