Design and acceptance assessment of a digital product passport for recycled and natural aggregate concrete elements

Abstract

The construction sector accounts for a substantial share of global greenhouse gas emissions, making effective strategies for the reuse and recycling of building materials indispensable. However, relevant information may be lost over the relatively long use phase of buildings. Digital Product Passports (DPPs) offer a standardized means of preserving and communicating product information across the life cycle and may therefore also be applied in the construction sector to facilitate reuse and recycling of building components, even after service lives exceeding 50 years. This paper presents the results of a first, interdisciplinary study that (i) develops a DPP for concrete elements using the Asset Administration Shell (AAS) and (ii) experimentally evaluates how DPP-presented information shapes consumer perceptions of recycled aggregate concrete (RAC) versus natural aggregate concrete (NAC) stair elements. In a scenario-based vignette experiment ( N  = 83), participants evaluated eight DPP mock-ups in which material (RAC vs. NAC), environmental impact (low vs. high), and structural performance (high vs. low) were systematically manipulated. Participants indicated their willingness to pay, perceived environmental value, perceived functional risk and product preference for each DPP. Repeated-measures ANOVAs showed robust main effects of material and environmental impact on perceived environmental value, and main effects of material and structural performance on perceived functional risk. Willingness to pay and product preference were higher for RAC than NAC, for low versus high environmental impact, and for high versus low structural performance. Overall, RAC was perceived as more environmentally valuable but also as riskier than NAC, even when objective environmental and structural indicators were held constant. The results indicate that DPP design should account for target-group-specific interpretation and potential biases in processing technical and sustainability information, to better support resource-efficient decision-making in the construction sector.