High-Resolution Mapping of Material Stocks in Belgian Road Infrastructure: Material Efficiency Patterns, Material Recycling Potentials, and Greenhouse Gas Emissions Reduction Opportunities

Abstract

Road infrastructure is an integral part of built environment stocks, as it delivers essential social and economic services. While previous work has assessed material stocks, flows, and embodied emissions, spatially refined mapping of materials accumulated in road infrastructure can highlight hitherto underappreciated synergies between improved spatial planning, material stock efficiency, and urban mining. In this study, we mapped the materials stocked in road infrastructure across Belgium, explored the patterns of material stock efficiency and the recyclability of end-of-life road materials, and examined the greenhouse gas (GHG) emissions reductions of improving stock efficiency and recycling. We assembled data scattered across various governmental sources and crowdsourced platforms and developed a comprehensive database to warehouse locational information on road typology, layer geometry and thickness, material characteristics, traffic volume, climatic conditions, and soil conditions. Our results reveal a strong but nonlinear correlation between material stock efficiency and population density, indicating that spatial planning can reduce the required road stocks and associated GHG emissions. Urban mining potentials in road infrastructure hinge on multiple factors, such as the proximity to recycling facilities and the degradation of pavements during use. Our counterfactual analysis shows that urban road planning and reusing recycled asphalt can cut GHG emissions by up to 53 and 70%, respectively. Therefore, material-efficient road planning and improved material recycling can help realize circular economy potentials and mitigate GHG emissions moving forward.

Weighing China's Fast Deployment of Offshore Wind Turbines through Satellite Images

“…One potential area for improvement is the year-byyear refinement of material percentage/intensities. Take the studies focusing on buildings or infrastructure for example, researchers have used techniques such as machine learning to develop material intensity datasets and refine material stocks in different years and spatial locations 38,39 . Additionally, a more detailed classification of synthetic fiber categories within wind turbine blades is encouraged.…”