Research

GEFION {ˈɡevˌjon} Generation of digital emission certificates for climate-positive timber system buildings

Political measures in the area of climate protection and increasing digitalisation (including machine learning) are creating new challenging fields of action in the construction and construction supply industry. A study by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR online publication no. 17/2020) has shown that 40% (362 million tonnes of CO2 equivalents) of Germany's greenhouse gas (GHG) emissions are caused by the production, construction, modernisation, use and operation of residential and non-residential buildings. 75% of the GHG footprint of the ‘Construction and use of buildings’ field of action (297 million tonnes of CO2 equivalents) and thus 33% of national GHG emissions were caused by the use and operation of residential and non-residential buildings. Life cycle assessments of buildings (data sets) are currently calculated and awarded according to a static/technical system model (cf. LEED, BREEAM or DGNB system, among others). A large number of model parameters, consumption data and measurement data are incorporated here. The assessment is based on the technical building parameters according to which it was constructed. The subsequent operation and (re)utilisation of the building are included in the calculation as fixed parameters. If the subsequent utilisation profile of the building changes, the assessment only reflects the originally planned utilisation and operating context. The current approach to life cycle assessment does not include dynamic changes to utilisation parameters during the operating phase. 

In our research project, we are developing an improved, computer-implemented method for the dynamic calculation of the life cycle assessment of buildings. The starting point is the development of a life cycle-optimised BIM model (Building Information Modelling), which describes a timber system construction planned according to circular economy criteria and contains the necessary building parameters to dynamise the life cycle assessment. On this basis, we are modelling a digital building twin that simulates the use of the building. In parallel to these activities, we are building a demonstrator, also based on the BIM model, which provides real measurement data to verify the dynamic balancing. The utilisation information then flows into a machine-supported analysis for life cycle assessment. Technologically, we use artificial intelligence (AI) methods, in particular semi-supervised learning approaches are to be developed and tested. Through this dynamisation, the CO2 positivity of a building can be made transparent and documented, including through changes in the context of use during the operating phase. This transforms the static life cycle assessment for buildings into a technically analysed dynamic assessment that reflects the actual use of the building. This life cycle assessment can also be used to analyse whether emission certificates can be generated and issued for climate-positive buildings. By creating a digital interface between climate-positive buildings and emissions trading platforms, the surplus CO2 equivalents of climate-positive buildings can also be channelled into certificate trading. This innovative research project thus makes a significant contribution to achieving climate targets, to the construction of climate-positive buildings and thus also to the transformation of the construction industry towards a digital and climate-neutral industry.