How actors perceive the transition pathways towards sustainable energy production and use will likely influence their support in their everyday behaviour and political engagement towards the energy transition. Mapping actors’ mental models of the drivers of the energy transition can provide key insights into such perceptions. The present study is the first to systematically map mental models of the drivers of the energy transition, compare mental models between actor groups, and explain differences in mental models with political orientation and worry about climate change. We mapped mental models about the energy transition among a sample of experts (N = 25), and representative samples of Dutch (N = 299) and Norwegian (N = 313) citizens. Participants visualised their perceptions of the causal relations of different energy transition pathways by drawing a diagram using a standardised tool to map mental models (M-Tool). The results demonstrate (1) a key focus in the mental models on renewable energy generation such as solar panels, wind farms, and hydropower, (2) that expert mental models are more focused on policy pathways compared to citizen mental models, (3) that mental models of actors leaning towards the political right focus less on individual behaviour than left-leaning actors, and (4) that climate change worry results in more focus on individual behaviour and policy pathways in the mental models. Policymakers could use these insights to engage citizens with the energy transition, for example, by tailoring their messages to the mental models of the target group.

The energy transition brings about complex environmental and societal changes that require robust science-based policy guidance. Conventional Life Cycle Assessment (LCA) methods to evaluate the implications of these changes often fail to capture the dynamic interactions among energy systems, human behavior, and environmental impacts, leading to potentially misleading or oversimplified conclusions. This study introduces a new framework for integrating societal change into LCA using Agent-Based Modeling (ABM). Our approach leverages LCA metamodels, integrated with the ABM via defined linking parameters, to enable real-time feedback between agent decisions and environmental outcomes, while minimizing computational demands. We illustrate the applications of this framework in a photovoltaic case study, examining how end-of-life choices affect abiotic depletion and climate change. The ABM-LCA integration employs metamodels with high predictive performance, achieving R² values of 0.95 for abiotic depletion and 0.82 for climate change. Linking parameters are based on the number of PV modules entering different end-of-life pathways. Societal change, as modeled in the ABM, is driven by the assumption that increased awareness of environmental impacts promotes circular behaviors. The photovoltaic case study provides an illustrative exploration of how consumer choices may affect environmental impacts, such as abiotic depletion and climate change, and how these impacts, in turn, may shape future consumer behavior. This work highlights the value of using a metamodel-driven, integrated ABM-LCA modeling framework for decision-making in complex systems, uncovering unexpected system behaviors and offering insights into sustainable transitions.