Where if? Using spatial, building-stock-driven simulations to explore construction circularity strategies in Gothenburg, Sweden
Preprint
- 13 February 2026
- preprint
- Published by Elsevier in SSRN Electronic Journal
Abstract
Recycling construction materials can significantly reduce the environmental impact of the construction and demolition sector. However, implementing circularity strategies requires a spatially refined understanding of material stock dynamics. In this study, we introduce a stock-driven and rule-based simulation that forecasts – over space and time – material stocks, flows, and associated embodied carbon resulting from construction, renovation, and demolition activities. It enables the exploration of how urban parameters, such as the recycled content of new constructions, demolition rates, renovation rates, and different types of new construction, influence material flows and embodied carbon. Using Gothenburg’s residential building stock, we demonstrate the simulation's capabilities by tracking eight building materials along with their embodied carbon over time, across the parameters, and for two scenarios: a baseline, and a policy on energy efficiency. The simulation can be expanded to include more materials and built environment assets, facilitating informed decision-making and strategies for a sustainable construction sector.Keywords
This publication has 47 references indexed in Scilit:
- Machine learning-based prediction of construction and demolition waste generation in developing countries: a case studyEnvironmental Science and Pollution Research, 2024
- Data-driven prediction of construction and demolition waste generation using limited datasets in developing countries: an optimized extreme gradient boosting approachEnvironment, Development and Sustainability, 2024
- Unravelling the mystery of Chinese building lifetime: A calibration and verification based on dynamic material flow analysisApplied Energy, 2019
- Building, composing and experimenting complex spatial models with the GAMA platformGeoInformatica, 2018
- Using a segmented dynamic dwelling stock model for scenario analysis of future energy demand: The dwelling stock of Norway 2016–2050Energy and Buildings, 2017
- Dynamic building stock modelling: General algorithm and exemplification for NorwayEnergy and Buildings, 2016
- Dynamic building stock modelling: Application to 11 European countries to support the energy efficiency and retrofit ambitions of the EUEnergy and Buildings, 2016
- The role of in-use stocks in the social metabolism and in climate change mitigationGlobal Environmental Change, 2014
- An overview of construction and demolition waste management in Canada: a lifecycle analysis approach to sustainabilityClean Technologies and Environmental Policy, 2012
- Stock dynamics for forecasting material flows—Case study for housing in The NetherlandsEcological Economics, 2006