Modeling Material Stocks and Embodied Emissions from the Built Environment: A machine learning and dynamic material stock and flow framework
Year: 2026
Type: dissertation
Abstract: The building and construction sector is responsible for around 37% of global carbon emissions. It is therefore crucial to dicarbon the sector, and it is currently not on track to meet the Paris Agreement aligned decarbonization pathways. The carbon emissions from building and construction can be classified into operational emissions from direct energy consumption, and the embodied emissions associated with the material production process. Policy efforts have been mainly focused on reducing operational emissions through measures to reduce energy consumption by building renovation such as the Energy Performance of Buildings Directive (EPBD) in the European Union. However, prioritizing renovation could result in trade-offs such as increased embodied emissions. It is therefore important to understand and model the embodied emissions from the building and construction sector, especially regarding maintenance and renovation. A key challenge in developing detailed bottom-up material stock and flow model for the built environment is the lack of data. This thesis addresses this challenge by developing a modeling framework that applies machine learning to predict and impute missing inventory data, and subsequently further develops material flow analysis models to better estimate embodied emissions from maintenance and renovation. This modeling framework is first developed using roads in Sweden as a case study and further developed and refined for residential buildings in Sweden. The machine learning approach is developed to circumvent the need for scarce height data to predict usable floor space and construction year for buildings. The building inventory dataset is then complemented by a detailed material intensity (MI) dataset that can be disaggregated into building layers, and a material flow model that ensures renovation is correlated with demolition is developed. Lastly, the modeling framework is integrated with a building energy model to analyze the total emissions reduction potential for energy efficiency renovation for the Swedish city of Uddevalla.The results show that firstly machine learning can be effectively applied to predict inventory data in absence of height data, and such approaches should be tested in other geographical areas. The embodied emissions results show that in Sweden embodied emissions from maintaining roads and renovating residential buildings (both functional and energy efficiency renovations) is expected to exceed the embodied emissions from new construction. Therefore, more policy attention is needed to reduce embodied emissions from renovations.
Author: Qiyu Liu
Institution: Chalmers University of Technology
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204471731