In order to assess the economic stability of companies in the context of future climate change impacts, not the financial industry increasingly demands not only financial indicators but also ESG (environmental, social and governance) measures. Accounting, performance indicators and reporting standards set targets and “register” economic activity: they thus provide the (self-)perception of organisations and the basis for ratings. Models of sustainability and climate risk assessment now link tried and tested concepts of financial ratings with data and concepts from the natural sciences as well as with other non-financial information. Knowledge management and decision-making processes of organisations are thus expanding and increasingly developing into an infrastructure of sustainability governance through data, indicators and algorithms (DIA). Algorithms are in this context also understood as analogue, fixed procedural and administrative practices. These practices run in such an automated way that the underlying design decisions, assumptions and functions become a “black box”. Data sets, taxonomies and programmes that were optimised for traditional application fields such as finance and municipal management are now translated into the context of sustainability transformation. As part of this translation, the implicit action models and impact theories inscribed in such tools also travel to the emerging field of sustainability governance. Therefore, performative effects, possible biases and conceptual errors, algorithmic discrimination and risks for data security and sovereignty need to be investigated.
Green DIA explores the extent to which information on public climate protection regulations, regional provisioning infrastructures and spatial and urban planning measures in particular can improve and evaluate the analysis of sustainability risks and impacts. To this end, satellite data, corporate and financial information and public sector register data will be linked. The project is focused on the building sector and the use of private and commercial buildings. The aggregation, linkage and comparison of sustainability information from different contexts also allow for a reflexive review of the social, political and economic assumptions as well as the ethical implications of existing data collection and evaluation procedures.
Prof. Dr. ir. Walter Timo de Vries
Chair of Land Management, TUM School of Engineering and Design | Technical University of Munich
Prof. Dr. Frauke Kreuter
Chair of Statistics and Data Science in Social Sciences and the Humanities (SODA), Faculty of Mathematics, Informatics and Statistics | Ludwig-Maximilians-Universität in Munich
Prof. Dr. Michael Schmitt
Professor for Earth Observation, Department of Aerospace Engineering | University of the Bundeswehr Munich
Dr. Felicitas Sommer
Project lead at the Chair of Land Management, TUM School of Engineering and Design | Technical University of Munich
Dr. Andreas Dimmelmeier
Researcher at the Chair of Statistics and Data Science in Social Sciences and the Humanities (SODA), Faculty of Mathematics, Informatics and Statistics | Ludwig-Maximilians-Universität in Munich
Dr. Deepika Mann
Postdoctoral researcher at the Chair of Earth Observation, Department of Aerospace Engineering | University of the Bundeswehr Munich
Manuel Huber M.A.
PhD candidate at the Chair of Earth Observation, Department of Aerospace Engineering | University of the Bundeswehr Munich