Environmental and economic implications of selective demolition and advanced recycling of construction waste
Scientific Article 07/2025 | Berfin Bayram | Kathrin Greiff | Lion Gerlich | Anna Luthin | Linda Hildebrand | Marzia Traverso |
Selective demolition and high-quality recycling of construction and demolition waste are crucial for the implementation of a circular economy in the built environment. To investigate this further, we conducted a case study on a building to assess the environmental and economic potential of selective demolition and advanced recycling. Two demolition strategies (highly and partially selective) were assessed, in combination with four mineral waste recycling scenarios that differ in plant type (mobile or stationary) and recycling technique (conventional or advanced). Highly selective demolition resulted in lower environmental impacts across most categories. The global warming potential results range between −33.8 and − 15.1 kg CO2-eq/m2, with the highly selective options performing substantially better, considering the avoided impacts. The environmental differences between mineral waste recycling options were minor in most impact categories, although two exceptions occur in terrestrial ecotoxicity and ionizing radiation categories. Also, different impact methods for mineral resources lead to different outcomes, depending on whether the construction aggregates are considered within characterization factors. From an economic perspective, the difference between highly selective demolition and partially selective options is rather smaller due to higher labor costs and low market conditions for higher quality recycled aggregates. Estimates of material flows through detailed modelling of selective demolition and different options for recycling of mineral waste are essential in the context of the circular economy. The availability of primary data for detailed modelling is crucial together with the inclusion of different impact assessment methods for resource indicators and critical interpretation through sensitivity analysis.