Advancing building stock transformation models: An agent-based approach and its application to Germany

The building sector is pivotal for achieving climate neutrality, requiring sophisticated modeling tools to guide the energy transition. It is highly heterogeneous with variety in both the built environment and in its decision-makers. While many sectoral models exist, most of them fall short of capturing the barriers and limitations in the energy transition as they lack high spatial resolution and a detailed representation of heterogeneous building characteristics and local infrastructure constraints. To address this gap, we present RENDER-Building, a new agent-based model (ABM) designed for high-resolution analysis of building stock transformation. We validate and apply the model to the German building stock to simulate potential transformation pathways until 2050 under three distinct scenarios. The individual buildings are the agents here with detailed attributes, located in a settlement type in a NUTS3 region. The model explicitly considers the availability of energy infrastructure and simulates agents’ decisions about renovation and technology adoption based on bounded rationality. Our case study’s results indicate that even with ambitious measures, Germany’s building sector may miss its short-term emission targets due to the inertia of the existing stock. A transformation pathway considering realistic challenges could substantially exceed the short- and long-term emission targets, necessitating difficult and potentially costly interventions to get back on track. Our study demonstrates the utility of high-resolution ABMs in providing nuanced, actionable insights for policymakers, helping them to navigate the complexities of the building sector’s energy transition. • The presented model captures granular dynamics in the heterogeneous building sector. • Agents have 35+ characteristics including users/units that define behavioral patterns. • Agents and availability of infrastructure are specified at high spatial resolution. • Sectoral pathways with varied political, technical and social conditions are explored. • Nuanced, actionable insights are derived to navigate complex sectoral transformations.