Improving the life cycle greenhouse gas emissions of transport infrastructure assets – a case study in Brussels, Belgium

Infrastructure transportation assets represent a significant share of environmental effects associated with built stocks. Although there is a growing body of knowledge on the life cycle assessment of buildings and material flow analysis of building stocks, infrastructure assets have received less attention. Existing life cycle assessment studies tend to rely on process analysis which is known to underestimate embodied environmental flows. The aim of this paper is to introduce a bottom-up parametric model for infrastructure transport assets, which can support a detailed characterization of the stock in a consistent and transparent manner. It relies on comprehensive hybrid exchange data for the quantification. This paper establishes the approach and equations used in the proposed model. The model consists of four main steps, namely: developing parametric representations of transport infrastructure assets, quantifying their material stock and replacement-related flows, quantifying life cycle embodied environmental flows and visualizing the results. One pilot case study of a road (including pedestrian sidewalks and bike lanes) and light railway, is used to demonstrate the model. Results assessing this pilot case study over 60 years are presented, including the material stock and replacement flows, as well as life cycle embodied greenhouse gas emissions (LCEGHG). Together these results provide important insights into material/infrastructure asset hotspots assets (e.g. steel lighting posts). Emissions-intensive material products and assets have a notable impact on results of LCEGHG, and they dominate both Initial greenhouse gas emissions (IEGHG) and recurrent greenhouse gas emissions (REGHG). However, they contribute marginally to the total mass. Another important observation is the significance of REGHG representing ~30% of LCEGHG. Interestingly, steel, asphalt and concrete are the main contributors to both REGHG and IEGHG. Future research consists of automating the proposed model and packaging it as a python-based Rhinoceros Grasshopper plugin before conducting testing and verification.