The European Union aims to achieve net-zero greenhouse gas emissions by 2050. Local authorities play a crucial role in fulfilling this target by setting and implementing ambitious energy transition strategies. However, significant challenges remain in understanding decision-making among different system actors to accelerate the phase-out of fossil fuels. In particular, further research on how to incentivize households to replace carbon-intensive heating systems, adopt low-carbon technologies, and undertake energy retrofit measures in different local contexts is required.
Against this backdrop, the interdisciplinary project ABM4EnergyTransition (ABM4ET) seeks to develop a spatially explicit, empirically grounded agent-based model (ABM) to support the heat transition in the Bundesland of Styria, Austria. ABM4ET is positioned at the science-policy interface to facilitate transdisciplinary collaboration between energy experts and policymakers in designing, implementing, and evaluating long-term policy interventions. This is to be accomplished by developing this computerized modeling tool that encompasses sociotechnical, regulatory, and behavioral dimensions to simulate transition pathways at the local level.
The development of this ABM included a process of characterization and parameterization of human agents and their rationality based on empirical data. For this purpose, a survey on energy renovation decisions was administered to 2,410 homeowners across Styria. The collected data were used to implement a partial least squares-structural equation model (PLS-SEM) to statistically discern the effects of household context, environmental attitudes, and social influence on decision-making for adopting thermal insulation measures and heating system replacement in the residential sector. In this coupled ABM-SEM approach, special emphasis was placed on analyzing the perception of (and aversion to) climate and energy risks as a driver of household decision-making.
This presentation makes a twofold contribution. First, it presents key insights on the role of interventions in the heat transition, particularly behavioral change interventions, derived from our experience of integrating relevant empirical data into an ABM. We shed light on potential leverage points and barriers to change by quantitatively estimating the relative influence of various parameters on the replacement of heating systems and the adoption of thermal insulation. Second, it reflects on the use of empirical ABMs for fostering the cooperation between science and policy in the design of solutions to the societal challenges posed by the energy transition. Accordingly, we discuss the possibilities offered by our simulation approach to deal with uncertainty in policy design, test local-level interventions, and analyze social tipping dynamics.