Bridging Science and Policy for Local Heat Transitions: An Empirical Agent-Based Modeling Approach

Achieving the EU’s 2050 net-zero emissions target requires effective strategies to drive the energy transition at the local level, especially among households. The ABM4EnergyTransition (ABM4ET) project addresses this critical need by supporting heat transition efforts in the state of Styria, Austria. The project focuses on bridging science and policy through the development of a spatially explicit, empirically grounded agent-based model (ABM) to be used by energy experts and policymakers to simulate a range of policy intervention scenarios.

Our ABM recreates household decision-making behavior regarding energy renovations by integrating empirically derived agent rules and parameters that reflect the complexity of these processes. More specifically, we surveyed 2,410 homeowners across Styria to capture decision factors influencing the adoption of thermal insulation and low-carbon heating systems. Using this survey data, we implemented a two-step multivariate analysis strategy. In the first phase, we developed a Partial Least Squares-Structural Equation Model (PLS-SEM) to estimate the effect of contextual, psychological, and social influences on renovation decisions. In the second phase, we performed a Multigroup Analysis (MGA) to determine whether predefined data groups relevant to social tipping dynamics exhibit significant differences in their group-specific parameter estimates.

Our contribution is twofold. First, we identify potential leverage points and barriers to behavior change, building on our experience in analyzing and integrating empirical data into an ABM. Second, we demonstrate how ABM simulation offers a promising method for examining the intertwined roles of agency and interventions in driving social tipping dynamics toward sustainability.