Development of a machine learning model to improve estimates of material stock and embodied emissions of roads

Material flow analysis is an important tool for estimating material flows and embedded emissions of transport infrastructure. Missing attributes tend to be a major barrier to accurate estimates. In this study a machine learning model is developed to estimate the missing data in a statistics dataset of roads, to enable a bottom-up material stock and flow analysis. The proposed approach was applied to the Swedish road network to predict missing data for road width in the statistical dataset. The predicted hybrid dataset was then used to estimate material stocks, flows, and embodied emissions from Year 2020 to Year 2045 using decarbonization scenarios with a supply chain perspective. The study demonstrates that machine learning models can be used to enable national-level material stock and flow analyses of roads. Multiple machine learning algorithms were tested, and the best performing model achieved an R² value of 0.784. In the scenario-based analysis, the embodied emissions of Swedish roads could be reduced by up to 51% using available materials.