The energy transition is a complex process, involving multiple interconnected pathways. Therefore, understanding citizens' perceptions of this transition requires a systems thinking approach—one that recognises the interdependencies among these pathways and their collective role in achieving the energy transition. Mental models offer a valuable lens into such perceptions, as they capture individuals' assumptions about the causal relationships among components of the energy transition pathway. Aligning citizens' mental models with national energy transition policies is essential for cultivating public support and participation, yet this relationship remains underexplored. This study investigates how citizens' mental models of the energy transition align with national policy and how these mental models relate to perceptions of the effectiveness and priority of pathway components. Focusing on the Netherlands and Norway, we examine the centrality of 16 energy transition pathway components within citizens' mental models, comparing this with their prominence in national policy reviews and citizens' ratings of priority and effectiveness. Results show (1) strong correlations between the prominence of pathway components in mental models and their prominence in national policy reviews, and (2) moderate to strong correlations between the prominence of the pathway components in the mental models with citizens' perceived priority and effectiveness of the pathway components. These findings provide valuable insights for enhancing public engagement and support for energy transition strategies.

The complex and contextual nature of sustainability challenges demands an approach that integrates quantitative complexity science with transdisciplinary approaches to create an integrated understanding of system change. We present a systematic literature analysis from an emerging field we term Transdisciplinary Complexity Science for Sustainability and derive best practices for how this research approach can foster learning and action for sustainability. Based on our analyses, we identify key areas for future research and provide concrete recommendations for carrying out Transdisciplinary Complexity Science for Sustainability.

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.

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.