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.
Mineral-based construction materials (MCMs) are the largest resource flow globally and in the UK. They are of strategic importance for construction, the third largest UK economic sector. MCMs include aggregate, cement/mortar/concrete, lime, block/brick masonry, plasterboard, building and roofing stone, cladding, glass, etc. Associated with UK primary extraction of >500,000 t/d, these materials are traditionally sourced from natural mineral resources (e.g., clay, limestone, dolomite, chalk, sandstone, igneous rock, brick clay, gypsum, slate, sand and gravel), and delivered to construction sites with the embodied environmental and social impacts that accumulate from their production and manufacturing.
In the UK, production and import data for MCMs are routinely reported, including geospatial data showing thousands of quarry sites across the UK. However, the system for their processing (i.e., transformation and distribution) from these myriad sites to service (e.g., use in infrastructure), and through to end-of-use, is poorly characterised. Material flow analysis to model the UK’s metabolism of all materials in detail 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. By 2030, the UK is likely to require 3.2-3.8 Gt of aggregates for major infrastructures projects. However, it will be challenging to meet this demand domestically at current production rates.
To gain insights and increase our understanding of the potential quantitative benefits and risks of implementing a circular economy for construction aggregates in England, the team, led by Dr Junyang Wang and Dr Rupert Myers from Imperial College London, Professor Julia Stegemann at UCL, and Tom Bide and Dr Joseph Mankelow at the British Geological Survey, have developed a new approach to material flow analysis, which uses Bayesian statistical modelling to bridge data gaps in existing incomplete stocks and flows datasets of MCMs.
The model has been developed and undergone preliminary testing with data sets from the literature. With support and inputs from industry partners, it is now being tested for the English construction aggregate system. The team is working with the Mineral Products Association, Construction Products Association, the British Aggregate Association, and other stakeholders to develop the system flow diagram and map the construction aggregate life-cycle based on estimates and current data sets collated through their memberships. In addition to the industry input, the team is collaborating with the Office for National Statistics (ONS) to assess the model applicability for use by their new Integrated Data Service. ONS has provided a financial contribution to support the collation of data and continue development of the model to 1) combine or disaggregate processes and sub-processes in the value chain, 2) enable ranking of variables based on their statistical importance, and 3) present findings in a format to persuade commercial competitive organisation to work together for better data transparency.
The material flow analysis model and representation of data for UK construction aggregate flows, is expected to support activities by the Office for National Statistics to form a new data pooling system that will enable resource traceability in the UK construction/built environment domain and improve data interoperability between UK government and industry sourcing of MCMs for better environmental (e.g., less emissions, land use) and economic performance (e.g., lower project costs). Beyond the expected impact of the model on the construction sector, this method will be applicable to virtually all resource streams and could be used by governments in the UK and internationally to guide collection of operational and commercially sensitive data.